FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning
Authors: Jaemin Shin
Hyungjun Yoon
Seungjoo Lee
Sungjoon Park
Yunxin Liu
Jinho D. Choi
Sung-Ju Lee
Venue: EMNLP 2023 Main (2023)
Keywords: mental health monitoring
federated learning
mobile healthcare
on-device ML
speech
TL;DR: We propose FedTherapist, a privacy-preserving mobile system using federated learning to monitor mental health via speech and keyboard input, outperforming non-language methods in predicting depression, stress, anxiety, and mood.

Abstract: Psychiatrists diagnose mental disorders via the linguistic use of patients. Still, due to data privacy, existing passive mental health monitoring systems use alternative features such as activity, app usage, and location via mobile devices. We propose FedTherapist, a mobile mental health monitoring system that utilizes continuous speech and keyboard input in a privacy-preserving way via federated learning. We explore multiple model designs by comparing their performance and overhead for FedTherapist to overcome the complex nature of on-device language model training on smartphones. We further propose a Context-Aware Language Learning (CALL) methodology to effectively utilize smartphones' large and noisy text for mental health signal sensing. Our IRB-approved evaluation of the prediction of self-reported depression, stress, anxiety, and mood from 46 participants shows higher accuracy of FedTherapist compared with the performance with non-language features, achieving 0.15 AUROC improvement and 8.21% MAE reduction.

FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning
Jaemin Shin', Hyungjun Yoon', Seungjoo Lee', Sungjoon Park?, Yunxin Liu', Jinho D. Choi', Sung-Ju Lee
KAIST AS Tsinghua University "Emory University
(jaemin. shin, hyungjun. seungjoo. lee, profs]]kkaist.ac. ar. sung.joon. py liuyamxin@air.
jinho. choi edu
Abstract
tal disorders by conversing with patients (Murphy et al., 2000). While analyzing linguistic use is ideal for monitoring smartphone users' mental health, substantial privacy concerns it raises present chal- lenges in amassing sufficient data to train advanced NLP neural networks (Devlin et al., 2019).
Psychiatrists diagnose mental disorders via the linguistic use of patients. Still, due to data privacy, existing passive mental health mon- itoring systems alternative features such as activity, app usage, and location via mo- bile devices. We propose FedTherapist, mo- bile mental health monitoring system that uti- lizes continuous speech and keyboard input in a crivacy-preserving way via federated learn- ing. We explore multiple model designs by comparing their performance and overhead FedTherapist overcome the complex nature of on-device language model training on smart- phones. We further propose a Context-Aware Language Learning (CAL.)) methodology ef- fectively utilize smartphones' large and noisy for mental health signal sensing. Our IRB- approved evaluation of the prediction of self- reported depression, stress, anxiety, and mood from 46 participants shows higher accuracy of FedTherapist compared with the performance with non-language features, achieving 0.15 AU- ROC improvement and 8.21% % MAE reduction.
We propose FedTherapist, privacy-preserving mental health monitoring system that leverages user-generated text (speech and keyboard) on smartphones via Federated Learning (FL). FL de- centralizes model training client devices (e.g.. smartphones) using locally stored data (MeMahan et al., 2016), ensuring privacy on FedTherapisl by only collecting securely aggregated model updates. For a detailed introduction to FL., see Appendix A.
Despite recent advances in FL + NLP (Lin al., 2022; Xu et al., 2023; Zhang et al., 2023a), on- device training (i.e., training on smartphones) of language models for mental status monitoring re- mains unexplored. We explore the best model de- sign for FedTherapist across multiple candidates, including Large Language Models (LLMs), by comparing their mental health monitoring perfor- mance and smartphone overhead.
1 Introduction
Nearly a billion people worldwide are living with mental disorders, which seriously affect onc's cog- nition, emotion regulation, and behavior. Early diagnosis and proper treatment the keys to alle- viating the negative impact of the mental disor- der (Sharp and Lipsky, 2002). However, most patients have been unaware of their disorder for years (Epstein et al., 2010), which delays treatment while the symptoms worsen.
In realizing FedTherapist, the challenge remains to effectively capture mental health-related signals from large corpus of spoken and typed user lan- guage smartphones, which differs from prior NLP mental health studies based on social me- dia (Yates et al., 2017; Park al., 2020) see Appendix G. To address such a challenge, we pro- pose Context-Aware Language Learning (CALL) methodology, which integrates various temporal contexts of users (e.g., time, location) captured on smartphones to enhance the model's 's ability to sense mental health signals from the text data. Our eval- uation of 46 participants shows that FedTherapist with CALL achieves more accurate mental health prediction than the model trained with non-text data (Wang et al., 2018), achicving 0.15 AUROC improvement and 8.21 % reduction in MAE.
Given their ubiquity in users' lives, researchers have leveraged smartphones to resolve this un- awareness problem, using features such as phone usage patterns, location, and activity for seam- less mental health monitoring (LiKamWa et al., 2013; Wang et al., 2014, 2018; Li and Sano, 2020; Tlachac et al., 2022a). However, these features fail to reflect how licensed psychiatrists diagnose men-

---

2 Data Collection
Performance Smariphune Overhead"
Methods
Depression
CPUI
Memory
We conducted IRB (Institutional Review Board)- approved data collection study evaluate FedTher- apist on real-world user data. Although FL works without user data collection, we collected the data to fully explore the potential of smartphone text data on mental health monitoring. We recruited 52 participants over the Amazon Mechanical Turk (MTurk) who identified English as the first and only language they use daily. Participants collected data for 10 days on our Android application, where we provided no instructions or restrictions to the participants' behavior during the study so that we collect data from their normal daily routine. The study details are in Appendix B, including how we assured the integrity of our data, removing six abnormal participants from the evaluation.
Fixed-BERT MLP
0.716
35%
219MB
End-to-End BERT MLP
0.524
68%
864MB
L.M
0.406
N/A
N/A!
Table 1: Comparison of candidate mental health pre- diction methods. * averaged measurement on Google Pixel (2016) and Samsung Galaxy S21 (2021). t: mea- sured in AUROC; 4: measured in average per-core uti- lization; 8; failed loading on test smartphones.
ment classification tasks (Adhikari et al., 2019). We adopt pre-trained BERT and only train MLP via FL. (2) End-to Lod-ttoEnte BERT + MLP: We use the same model architecture as the former but perform end-to-end training. including BERT and MLP. (3) LLM: Given recent breakthroughs and state- of-the-art performance of Large Language Models (LLMS) (OpenAI, 2023), we train and use LLM to prompt user text and output mental health status. We compared the model designs by measur- ing the depression detection performance (Sec- tion 4.1.2) and their feasibility of operation on two smartphones noted in Table 1. We adopted widely used base BERT models such as Distil- BERT (Sanh et al., 2019) and RoBERTa (Liu al., 2019) and reported the best performance. We used LLaMa-7B (Touvron et al., 2023) as LLM, one of the smallest publicly available LLM, for its feasi- bility on smartphones. We applied LoRA (Hu et al., 2022) for End-to-End End BERT + MLP and LLM ex- periments, for lightweight fine-tuning at resource- constrained mobile scenarios. We conducted exper- iments using Leave-One-User-O00 (LOUO) cross- validation, segmenting the users into 1 test user, 5 for validation, and 40 for training: the experiments were repeated 46 times by setting each user as a test user, with the subsequent five users serving as the validation set. Appendix D and E.4 present more details on experimental methods.
We collected two types of text imput: (1) speech: user-spoken words on the microphone-equipee smartphones, and (2) keyboard: typed characters on smartphone keyboards. On average, we col- lected 13,492:14.222 speech and 4.52115,994 keyboard words per participant. We describe how we implemented a system collect these inputs on smartphones in Appendix C. To compare FedTher- apist with the non-text data, we collected the data used in a mobile depression detection study (Wang et al., 2018) detailed in Section 1,, along with the text data. We collected ground truth on four mental health statuses: depression, stress, anxiety, and mood, as detailed in Section 4.1.2.
To ensure participant privacy, carefully con- trolled data collection, storage, and access as in the Ethics Statement. Given the highly sensitive nature the data, will not be released publicly and will be deleted post-study following IRB guidelines.
3 FedTherapist
Table shows the comparison results. We found that inference and training of Fixed-BERT MLP and End-to-End BERT + MLP are both available on our test smartphones, while LLM failed due to its memory constraints, requiring > 8GB free memory to load 8-bit quantized LLaMa-7B. Fixed- BERT + MLP results in 0. 192 and 0.310 higher AUROC than End-to-End End BERT + MLP and LLM, respectively, in depression detection, while it in- curs less smartphone overhead by training only MI.. We hypothesize that the subpar performance of BERT and LLM results from overfitting on our 40-user training sample, as shown in Figure 6. Past
3.1 Mental Health Prediction Model
FedTherapist aims to deliver accurate mental health monitoring using user-generated text data from smartphones, employing FL. Given that FL neces- sitates training a model on cooource-constrained user smartphones, it is essential that our model re- mains accurate and lightweight. We explore three candidate model designs: (1) Fixed-BERT MLP: The model consists of text embedding model followed by a multilayer perceptron (MLP). We selected BERT (Devlin et al., 2019) as our text embedding method for its effectiveness in docu-

---

unee
location
motien
350
Rom are
dystime
other
mmmatton
communictton
unddy
niiitime
Tome
swioonay
communaation
Tome
user_text.bxt
keylooard
belt:
teat timase rightinne
test location home
tedt: locaione nther
text moninnee statonary nary
lost mallonne in mozon
lest: appee com
Bext:
limee daytime
aporee
other
enbeedinng
enbodding
Embeedäng
erbbodding
MLLP
MUP
MLP
MUP
MUP
MLP
MILP
MLP
output
output
ouiiptt
cujpui
oulput
outpit
ostiui
mental health status
Figure I: Design of Context-Awar Language Learning (CAL.L) methodology for FedTherupist. CALJ maps the user's text to multiple temporal contexts (time, location, motion, and app). Each piece text trains the relevant models on each context; for example, the text generated daytime and home used train both the 'daytime' and 'home' models. CALL ensembles the context model outputs to determine the mental health status of a user.
research (Dai and Le, 2015; Sun et al., 2019) cor- roborates that pre-trained models overfit small-scale datasets, compromising their generaliz- ability. This could be mitigated by using a larger training dataset from broader population.
text at night at home than daytime at the workplace. We integrate four types of contexts that avail- able smartphones, each based on previous psy- chological studies follows:
Time (T): Time, indicative of when text gen- eration happens, was chosen as context due to humans' daily emotional cycles (English and Carstensen, 2014; Trampe et al., 2015). These studies imply significant emotional differences between morning and evening, hence we di- vide 24 hours into two contexts: daytime (TD: 9AM-6PM) and nighttime (Ty: 6PM~9AM). Location (L): Smartphone GPS infers user lo- cation during text generation. Motivated by a study (Lively and Powell, 2006) showing emo- tional expression is influenced by the domain (home or workplace), we divided GPS data into two contexts: home (Lz) and other locations (Lo). We identified the most frequented location from clustered GPS data as home.
Fixed-Bi Band-BERT++ + MLP is also superior in privacy, as partially training the model addresses concerns that gradient sharing during FL could result in data leakage or even sentence reconstruction (Huang al., 2021; Gupta et al., 2022). While acknowl- edge the risk of attackers possibly reconstructing original text after recovering the embedded text representations (Coavoux et al., 2018; Song and Raghunathan, 2020), we could further integrate protection methodologies to such an attack (Lee et al., 2022). Thus, we use Fixed-BERT + MLP our model design for FedTherapist, with Distil- BERT that showed the best performance (Table 3).
3.2 Context-Aware Language Learning
As an average person speaks more than 16,000 words and sends 71 text messages per day (Mehl et al., 2007; Harrison and Gilmore, 2012), process- ing such large corpus of noisy text input smart- phones for accurate mental health sensing is yet unexplored. To address such challenge, we pro- pose Context-Aware Language Learning (CALL) methodology for FedTherapist.
Motion (M): Smartphone accelcrometers indi- cate user movement during text generation. In- spired by a study (Gross et al., 2012) reveal- ing emotion expression varies with physical ac- tivity, we used Google Activity Recognition API (Google, 2022a) to distinguish two contexts: stationary (Mg) and in motion (Mar).
Application (A): We categorized applications used for typing into two contexts: communi- cation (Ac) and others (Ao), based on stud- ies predicting mental status from text mes- sages (Tlachac et al., 2021, 2022c). Applications in the "Communication" category of Google Play Store, such WhatsApp. are classified as com- munication; otherwise classified others. Figure 1 depicts the mental health prediction model of CALL, which aggregates the user text on
We designed CALL to integrate various tempo- ral contexts of user (e.g., time, location, etc.) captured on smartphone to enhance the model's ability to sense mental health signals from the text data. The intuition behind our design to let the neural network focus on the user-generated text in a context in which users are highly likely to ex- pose their mental health status. For example, users would more likely to express their emotions via

---

Methods
CI.+NonText
FlxTTex
FedTheraaist (HIL
Data Type
Non Text Data
Speech (S)
Keyboard (K)
S+K
Speech (S)
Keyboard (K)
S+K
Depression (AUROC f)
0.625+0010
0.627:+0.004
0.710::0.00
0.571 0.775:00011
0.746=00000
0.721=00008
Stress (MAE 1)
20.8310.03
23.44.10.02
24.2
24.78.10.03
21.3410.01
20.07.00.1
19.12__0001
Anxiety (MAE ()
20.95:10.6
25.80.0..03
26.85.
27.30.10.03
22.56:
21.39
20.56..0.02
Mood (MAF.)) J)
18.76+0009
22.85+00.03
23.21++0.02
23.67++0.2
19.11+002
19.18=000
18.57-00 -.00
Table 2: Mental health monitoring performance on different methods.
each temporal context and trains separate models. CALL performs ensemble learning on N context models and takes weighted sum the models' outputs determine the user's mental health status. We use N for keyboard input with two con- texts each from T, L. M, A contexts and N = for speech input without contexts. When we use both speech and keyboard input, we use all the contexts and use N = 14. We implemented two types of ensemble weights in our experiments: (1) Eς: averaging the model outputs (W1, W2. ..... WN t): and (2) Eg: weighted sum of model outputs using the trained ensemble weights W over FL.
bile devices. (2) Text: we use Federated Learn- ing (FL) to train Fixed-BERT + MLP model with the speech and keyboard input text data captured on smartphones collected from our data collection. While FL is often outperformed by CL. (Nilsson et al., 2018), CL on text data is avoided due to privacy concerns. (3) FedTherapist: we applied CALL to Text.xt. We tested three sets of text data; speech input only (S), keyboard input only (K), and both (S K). We employed the Leave-One- User- Out (LOUO) cross-validatin technique in our ex- periments. Out of 46 users, one was configured the test user and the remaining 45 were used for training. This setup was iteratively conducted 46 times, setting each user as the test user. We report the test performance after running 1,000 epochs for CL or rounds for FL. Detailed experimental methodologies are presented in Appendix D.
In terms of privacy, an attacker could reveal the private information of a user based on which con- text models' gradients are non-zero. For example, if a user's LH model gradient is non-zero while the Lo gradient is zero, an attacker could infer that user stayed home most of the time. To address this problem, we could adopt Secure Aggregation protocol (Bonawitz al., 2017) that securely ag- gregates the FL elients' model gradients to prevent attackers from acquiring individual model updates.
4.1.2 Tasks and Metrics.
We evaluated on following tasks and metrics: (1) Depression (Classification): post the 10-day study, depression was surveyed on participants through the PHQ-9 test questionnaire (Wang et al., 2018) 8). We label participant mildly depressed if PHQ- 9 score 2 5 (Kroenke and Spitzer, 2002), and use the 10-day study data for prediction. We test the Area Under the ROC curve (AUROC) as a metric. (2) Stress, Anxiety, and Mood (Regression): we use the data from each day to predict the level of stress, anxiety, and mood, which were self-rated by participants on 0~000 scale as in previous literature (Li and Sano, 2020); O and 100 each indicate the negative and positive feelings. We test the Mean Absolute Error (MAB) as metric evaluate regression.
4 Evaluation
We answer the following key questions: (1) How beneficial is using the text data mental health monitoring tasks? (2) How much performance im- provement does FedTherapist achieve with CALL? (3) How do each context model and ensemble meth- ods of CALL perform?
4.1 Experimental Setup
4.1.1 Methods and Data Types.
We compare three methods: (1) CL+NonText: we use Centralized Learning (CL) to train an MLP model on the non-text data utilized by previous study (Wang et al., 2018), which uses the follow- ing features: stationary time, conversation count, sleep end time, location duration, unlock duration, unlock counts, and number of places visited. We extracted the features by preprocessing the device logs and raw sensor values such as GPS, accelerom- eter, or ambient light sensor from participant's 's mo-
4.2 Comparison of Data Types and Methods
Table 2 displays the performance of three methods for mental health monitoring. In the depression task. which utilizes 10-day data input, FexTexx outperforms Co+NenText by 0.15 AUROC. Con- versely, CL+NonText excels over FL+Teex in re- maining tasks that use one-day data input, with 2.61~66.35 lower MAE. These results suggest that

---

0.8
2727 Ea
Ry
Ti
Tv
Luy
B000 Lo
Ms
My
Ap
Ao
0.7
1000 aboolute
0.55
0.1
10.0
0.1
0.0
0.0
cusemble
sporch
key beured
ensemble
*peeeh
Reg Foorr
(a) Depression.
(b) Stress.
Inemn absohute crror
0.3
10000 al ateohhtee error
ensenable
speech
locy board
cnsembble
speech
Aey board
Anxicty.
Mood.
Figure 2: Performance ensemble methods (Ea: ensemble by averaging, Eg: ensemble by weighted sum) of
CALL and context models (Tp: daytime, Ty: nighttime, Luz: home, Lo: other locations, Ms: stationary, Ma in motion, Aç: communication applications, and Ao: other applications) on four mental health monitoring tasks. All subgraphs share the same legend with Figure 2a and 2b.
longer-duration text input more effectively captures mental health signals, likely due to the complexity detecting these signals within the noisy text, thus leading models to struggle with shorter-term data. Unlike Tex+ we find that FedTherapist consistently achieves improved performance over Ce+NonText on all tasks, achieving 121 higher AUROC on depression and 0.19~1.71 lower MAE on remaining tasks. FedTherapist also achieves en- hanced performance on stress, anxiety, and mood tasks than Tex with 4.32~6.74 lower MAE, with comparable AUROC (0.746 0.775) on de- pression. These results demonstrate that FedTher- apist with CALL generally improves our model in detecting mental health signals from the raw text, regardless of the data collection period.
MAE for remaining stress (19.12), anxiety (20.56), and mood (18.57) tasks, albeit with an AUROC of 0.579 for depression. This implies that some context models are not always helpful for stress, anxiefy, and mood tasks, that weighted sum outper- forms the averaging, unlike the depression task. Among the time contexts, Tn performs better on the depression task on the keyboard, while To excels speech, indicating more emotional ex- pression at daytime speech and nighttime keyboard use. For the location, LH outperforms Lo in both speech and keyboard, suggesting greater mental state disclosure at home. With the motion contexts, texts during movement (Ms) provide superior men- tal state indicators M.. For the application contexts, Ao significantly improves AUROC over Ac. underscoring the value of considering non- communication app inputs, unlike prior studies that focus only on the messenger app inputs (Tlachac and Rundensteiner, 2020; Tlachac et al., 2022c).
Our findings recommend the combined use of speech and keyboard input (S+K) with FedThera- pist, owing to its superior results stress, anxiety, and mood tasks (achieving a lower MAE between 0.54 and 2.22) and a similar performance in depres- sion tasks (0.721 vs. 0.746) over single text type up- proaches (S or K). S and K each excels at different tasks; S outperforms in mood with FedTherapist, while K shows superior performance in other tasks.
Conclusion
We presented FedTherapist, mobile mental health monitoring system leveraging continuous speech and keyboard input while preserving user privacy via Federated Learning (FL). Our Context-Aware Language Learning (CALL) empowers FedThera- pist to effectively sense the mental health signal from a large and noisy text data on smartphones. Our evaluation with 46 participants on depression, stress, anxicty, and mood prediction shows that FedTherapist outperforms the model trained on non-language features. We believe FedTherapist demonstrated the feasiblity of on-device NLP on smartphones that enable usage of user- sver-generated linguistic data without privacy concerns.
4.3 Comparison of Context Models and Ensemble Methods of CALL
Figure 2 illustrates the performance of each con- text model and ensemble methods of CALL (Sec- tion 3.2) across four mental health monitoring tasks (Section 4.1.2). Among ensemble methods, the av- eraging method FA excels on the depression task with 0.702 AUROC but underperforms elsewhere. The weighted ensemble, Ep, records the lowest

---

Limitations
the privacy threat on participants should always be carefully considered (Suster et al., 2017; Benton et al., 2017), we performed the following based on a previous work (Chen et al., 2019) to mini- mize the risk that originates from our study. (1) We provided detailed instructions on all data types we are collecting during the study to the partici- pants. (2) We only accepted participants who pro- vided their own signatures confirm that they are aware of the types of collected data. (3) We used VoiceFilter (Wang et al., 2019), the state-of-ther art voice separation technology to avoid collecting the speech of others who are not part of our study. (4) The collected data on smartphones were saved only in our Android application's 's sandbox storage where access from other applications is restricted. (5) The data securely transmitted through en- crypted communication with our SSL-certified and HIPAA Acooppiaannnnt database server. (6) The data was available only to the authors from the same in- stitution as the corresponding author. (7) We do not plan to share the collected data externally and will delete the data when our IRB approval expires.
Unlike previous approaches (Wang et al., 2014, 2018; Li and Sano, 2020) on mobile mental health monitoring that collected data over 10 weeks, our data collection was held for only 10 days. More- over, our study pool of 46 English-speaking partic- ipants do not represent regional & cultural differ- ences of language among users. Thus, the reported evaluations should be considered as exploratory. Note that our study gathered participants from US and Canada, spanning ages from 20s to 60s, while previous studies were conducted mostly on univer- sity students. Studying FedTherapist on different languages for longer term would be the next step for generalizing our findings.
The accuracy of our speech collection in un- constrained environments was not investigated. To avoid collecting the voice of non-target users and only record the speech in high quality, our speech text collection module (Section C.1) inte- grated Voice Activity Detector (Google, 2022b), VoiceFiilter (Wang et al., 2019, 2020), language classifier (Team, 2021), and speech recognition model (Cephei, 2022). Such machine learning- driven components, especially VoiceFilter, may yield errors at noisy or untrained environments (Es- kimez et al., 2022). However, the speech data in our experiment shows comparable or better results on stress, anxiety, and mood tasks with accurately collected keyboard data; the speech data also im- proves performance when it is jointly used with the keyboard data with FedTherapist. This result hints that the speech data collected from our speech text collection module could be effectively utilized in mental health monitoring tasks.
A Acknowledgments
This work was supported by the Institute of Infor- mation & communications Technology Planning & Evaluation (UTPP) grant funded by the Korea gov- ernment (MSIT) (No. 2022-0-00064) and the Insti- tute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00495).
References
Ashutosh Adhikari, Achyudh Ram, Raphael Tang, and Jimmy Lin, 2019. Docbert: Bert for document classi- fication. arXiv preprint 1rX4v:1904.088
When comparing the model designs for FedTher- apist in Section 3.1, we were limited to train on larger batch sizes (c.g., 128) for BERT and LLM as indicated in Appendix D.2, due to our GPU mem- ory restrictions (24GB on NVIDIA RTX 3090). Thus, our findings on comparison between model designs could be altered when tested on more pow- erful hardware or different data, but we still believe Brred-BERT-- + MLP is a promising option for mo- bile scenarios, given its low system overhead and high mental health monitoring performance.
Ashwin Karthik Ambalavanan, Pranjali Dileep Jagtap. Soumya Adhya, and Murthy Devarakonda. 2019. Us- ing contextual representations for suicide risk assess- ment from Internet forums. In Proceedings of the Sixth Workshop on Computational Linguistics and Climical Psychology. pages 172-176, Minneapolis. Minnesota. Association for Computational Linguis- tics.
Android. 2021. Android accessibility service api.
https://developerr anddoooiddper.rriiiiiiioooo.i android. com/ snfereecee/aooo////cceessslli/sv AccessibilittServicc.
Ethics Statement
The collection of text data on user smartphones has been previously discouraged as it contains highly private information (Tlachac et al., 2022a). As
Mario Ezra Aragón, Adrian Pastor López-Monroy, Luis Carlos González-Gurroll, and Manuel Montes-y Gómez, 2019. Detecting depression in social media

---

using fine- ine-grained emotions. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Hu- man Language Technologies, Volume I (Long and Short Papers), pages 1481-1486, Minneapolis, Min- nesota. Association for Computational Linguistics.
Maximin Coavoux, Shashi Narayan, and Shay Co- hen. 2018. sivacc-preserving neural representations of text. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Process- ing, pages 1-10, Brussels, Belgium. Association for Computational Linguistics.
Adrian Benton, Glen Coppersmith, and Mark Dredze. 2017. Ethical research protocols for social media health research. In Proceedings of the First ACL Workshop on Ethics in Natural Language Process- ing, pages 94-102, Valencia, Spain. Association for Computational Linguistics.
Glen Coppersmith, Mark Dredze, and Craig Harman. 2014. Quantifying mental health signals in twitter. In Proceedings of the workshop on computational linguistics and clinical psychology: From linguistic signal to climical reality, pages 51-60.
E. Jane Costello. 2016. Early detection and prevention of mental health problems: developmental epidemi- ology and systems of support. Journal of Clinical Child & Adolescent Psychology, 45(6):710-717.
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Anto- nio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017. Practical secure aggregation for sevacy-preserving machine learning. In Proceedings of the 2017 ACM SIGSAC Conference Computer and Communica- tions Security, pages 1175-1191.
Andrew M Dai and Quoc V Le. 2015, Semi-supervised sequence learning. In Advances in Neural Informa- rion Processing Systems, volume 28, Curran Asso- ciates, Inc.
Eoin Brophy, Maarten De Vos, Geraldine Boylan, and Tomás Ward. 2021. Estimation of continuous blood pressure from ppg via a federated learning approach. Sensors, 21(18)6311.
Ittai Dayan, Holger R Roth, Aoxiao Zhong, Ahmed Harouni, Amilcare Gentili, Anas Z Abidin, Andrew Liu, Anthony Beardsworth Costa, Bradford J Wood, Chien-Sung Tsai, et al. 2021. Federated learning for predicting clinical outcomes in patients with covid- 19. Nature medicine, 27(10):135--1743
Yekta Said Can and Cem Ersoy. 2021. Privacy- preserving federated deep learning for wearable iot- hased biomedical monitoring. ACM Trans. Internet Technol., 21(.).
Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. 2021. Predicting depression via social media. Proceedings of the International AAAI Conference on Web and Social Media, 7(1):128-137
Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analy- sis. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous comput- ing, pages 1293-1304.
Munmun De Choudhury, Emre Kiciman, Mark Dredze, Glen Coppersmith, and Mrinal Kumar. 2016. Discov- cring shifts to suicidal ideation from mental health content in social media. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI 16, page 2098-2110, New York, NY. USA. Association for Computing Machinery.
Alpha Cephei. 2022. Vosk speech recognition toolkit: Offiine speech recognition api for android, ios, rasp- berry pi and servers with python, java, c# and node. https:/// //gi www..tooo.aaooo.eecooooootottttt wtt.///t/aaaaeeeeee wwwpacappahaaaaaeeeeeeea wwspaaccee/ookaap..//aaa api.
Jacob Devlin, Ming-Was Wei Chang, Kenton Lec, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, Volume (Long and Short Pupers), pages 4171-415 41861 Minneapolis, Minnesota. Association for Computational Linguistics.
Richard Chen, Filip Jankovic, Nikki Marinsek, Luca Foschini, Lampros Kourtis, Alessio Signorini, Melissa Pugh. Jie Shen, Roy Yaari, Vera Maljkovic, Mare Sunga, Han Hee Song. Hyun Joon Jung. Belle Tseng, and Andrew Trister. 2019. Developing mea- sures of cognitive impairment in the real world from consumer- grade multimodal sensor streams. Pro- ceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '19, page 2145-2155, New York, NY, USA. Association for Computing Machinery.
Tammy English and Laura Carstensen. 2014. Emotional experience in the mornings and the evenings: con- sideration of age differences in specific emotions by time of day. Frontiers in Psychology, 5.
Xuetong Chen, Martin D Sykora, Thomas W Jackson, and Suzanne Elayan. 2018 What about mood swings: Identifying depression twitter with temporal mea- sures of emotions. In Companion Proceedings of the The Web Conference 2018 pages 1653-1660.
Ronald M Epstein, Paul R Duberstein, Mitchell Feld- man, Aaron B Rochlen, Robert A Bell, Richard L Kravitz, Camille Cipri, Jennifer D Becker, Patri- cia M Bamonti, and Debora Paterniti. 2010. 'i didn't 't know what was wrong:" how people with un- diagnosed depression recognize, name and explain their distress. Journal of general internal medicine. 25(9):554-961.
Yiqiang Chen, Xin Qin, Jindong Wang, Chaohui Yu, and Wen Gao. 2020. Fedhealth: A federated transfer learning framework for wearable healthcare. IEEE Intelligent Systems, 35(4):83-93.

---

Sefik Emre Eskimez, Takuya Yoshioka, Huaming Wang. Xiaofei Wang. Zhuo Chen, and Xuedong Huang. 2022. Personalized speech enhancement: New mod- els and comprehensive evaluation. In ICASSP 2022. IEEE.
Ahmed Husseini Orabi, Prasadith Buddhitha, Mahmoud Husseini Orabi, and Diana Inkpen. 2018. Deep learn- ing for depression detection of Twitter users. In Pro- ceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pages 88-97, New Orleans, LA. Associa- tion for Computational Linguistics.
Ethan Fast, Binbin Chen, and Michael S. Bernstein. 2016. Empath: Understanding topic signals in large- scale text. In Proceedings of the 2016 CHI Confer- ence on Human Factors in Computing Systems, CHI '16. page 4647-4657, New York, NY, USA. Associa- tion for Computing Machinery.
Xiaotang Jiang, Huan Wang, Yiliu Chen, Ziqi Wu, Lichuan Wang. Bin Zou, Yafeng Yang. Zongyang Cui, Yu Cai, Tianhang Yu, Chengfei Lyu, and Zhihua Wu. 2020. Mnn: A universal and efficient inference engine. In Proceedings of Machine Learning and Systems, volume 2, pages 1-13.
Google. 2022a. api. gttp://cccttoo///llllllyeeeeeggt
Google activity recogmition /ttpps://deeeloperss..ee google. com/ hndtos/d/reeferreee//////////ggp
Hyunwoo Kim, Jack Hessel, Liwei Jiang, Ximing Lu, Youngjae Yu, Pei Zhou, Ronan Le Bras, Malihe Alikhani, Gunhee Kim, Maarten Sap. et al. 2022. Soda: Million-scale dialogue distillation with so- cial commonsense contextualization. arXiv preprint arXiv:2212. 10465.
Google. 2022b. Real-time communication for the web. httpp::/webbtc.
M. Melissa Gross, Elizabeth A. Crane, and Barbara L. Fredrickson. 2012. Effort-shape and kinematic as-
Yoon Kim. 2014. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1746-1751, Doha, Qatar. Association for Computational Linguis- tics.
sessment of bodily expression of emotion during gait. Human Movement Science, 31(1):202-23 221
Samyak Gupta, Yangsibo Huang, 7exuan Zhong, Tianyu Gao, Kai Li, and Danqi Chen. 2022. Recov- cring private text federated learning of language models. In Advances in Neural Information Process- ing Systems (NeurIPS).
Kurt Kroenke and Robert L Spitzer. 2002. The phq-9; a new depression diagnostic and severity measure. Psychiatric annals, 32(9):509-515.
Marissa A. Harrison and Angela L. Gilmore. 2012. U txt when? college students' social contexts of text messaging. The Social Science Journal, 49(4):513-
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020. ALBERT: A lite BERT for self-supervised learning of language representations. In 8th Inter- national Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net.
518. Special Issue: National and state politics: A current assessment.
Susan C Herring and Sanja Kapidzic. 2015. Teens, gender, and self-presentation in social media. In- ternational encyclopedia of social and behavioral sciences, 2:1-16.
Garam Lee, Minsoo Kim, Jai Hyun Park, Seung- won Hwang, and Jung Hee Cheon. 2022. Privacy- preserving text classification on BERT embeddings with homomorphic encryption. In Proceedings of the 2022 Conference of the North American Chap- ter of the Association for Computational Linguistics: Human Language Technologies, pages 3169-3175, Seattle, United States. Association for Computational Linguistics.
Karen Hovsepian, Mustafa al' Absi, Emre Ertin, Thomas Kamarck, Motohiro Nakajima, and Santosh Kumar. 2015. Cstress: Towards gold standard for contin- uous stress assessment in the mobile environment. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 115 page 493-504, New York, NY, USA. Association for Computing Machinery.
Haeyun Lee, Young Jun Chai, Hyunjin Joo, Kyungsu Lee, Jae Youn Hwang, Seok-Mo Kim, Kwangsoon Kim, Inn-Chul Nam, June Young Choi, Hyeong Won Yu, et al. 2021. Federated learning for thyroid ultra- sound image analysis to protect personal information: Validation study in a real health care environment. JMIR medical informatics, 25)x299
Edward Hu, yelong shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, AbdeIrahman Mohamed, Omer Levy. Veselin Stoyanov. and Luke Zettlemoyer. 2020. BART: Denoising cequence-to-sequence pre-training for natural language generation, translation, and com- prehension. In Proceedings of the 58th Annual Meet- ing of the Association for Compulational Linguistics,
Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, and Sanjeev Arora. 2021. Evaluating gradient inver- sion attacks and defenses in federated learning. In Advances in Neural Information Processing Systems, volume 34, pages 7232-7241, Curran Associates, Inc.

---

pages 7871-7880, Online, Association for Computa- tional Linguistics.
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al. 2016. Communication- learning of deep networks from decentralized data. arXiv preprint 1622.06622
Boning Li and Akane Sano. 2020. Extraction and in- terpretation of deep autoencoder-based temporal fea- tures from wearables for forecasting personalized mood, health, and stress. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 4(2).
Matthias R. Mchl, Simine Vazire, Nairán Ramírez- Esparza, Richard B. Slatcher, and James W. Pen- nehaker. 2007. Are women really more talkative than men? Science, 3 88868834)
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar San- jabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated optimization in heterogeneous networks. In Proceedings of Machine Learning and Systems, volume 2. pages 450.
Abhinav Mehrotra, Fani Tsapeli, Robert Hendley, and Mirco Musolesi. 2017. Mytraces: Investigating corre- lation and causation between users' emotional states and mobile phone interaction. Proc. ACM Interact. Mob. Wearable Ubiqqiiooss Technol. 1(3).
Robert LiKam Wa, Yunxin Liu, Nicholas D. Lane, and Lin Zhong. 2013. Moodscope: Building a mood sen- sor from smartphone usage patterns. In Proceeding of the 11th Annual International Conference Mo- bile Systems, Applications, and Services, MobiSys 113, page 389-402, New York, NY, USA. Association for Computing Machinery.
Mehrab Bin Morshed, Koustuv Saha, Richard Li, Sid- ney K. D' Melll,, Mummun De Choudhury, Gregory D. Abowd, and Thomas Plötz. 2019, Prediction of mood instability with passive sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 3(3).
Jane M Murphy, Richard R Monson, Nan M Laird, Arthur M Sobol, and Alexander H Leighton. 2000. A comparison of diagnostic interviews for depression in the stirling county study: challenges for psychi- atric epidemiology. Archives of general psychiatry, 57(3):230-25)
Bill Yuchen Lin, Chaoyang He, Zihang Ze, Hulin Wang. Yufen Hua, Christophe Dupuy, Rahul Gupta, Mahdi Soltanolkotabi, Xiang Ren, and Salman Aves- timehr. 2022. FedNLP: Benchmarking federated learning methods for natural language processing tasks. In Findings of the Association for Compu- tational Linguistics: NAACL 2022, pages 157-175, Seattle, United States. Association for Computational Linguisties.
Adrian Nilsson. Simon Smith, Gregor Ulm, Emil Gus- tavsson, and Mats Jirstrand. 2018. A performance evaluation of federated learning algorithms. In Pro- ceedings of the Second Workshop on Distributed In- frastructures for Deep Learning. DIDI. '18 page 1-8, New York, NY, USA. Association for Comput- ing Machinery.
Bingyan Liu, Yifeng Cai, Ziqi Zhang, Yuanchun Li, Leye Wang, Ding Li, Yao Guo, and Xiangqun Chen.
2022. Distfl: Distribution-aware federated learn- ing for mobile scenarios. Proc. ACM Interact. Moh. Wearable Ubiquitous Technol. 5(4).
Peter Norvig. 2016. How to write a spelling corrector. https://norvig: http:s///orviig.cccssellll----
Terrance Liu, Paul Pu Liang Michal Muszynski, Ryo Ishii, David Brent, Randy Auerbach. Nicholas Allen, and Louis Louis-Philippe Morency. 2020. Mul- timodal privacy-preserving mood prediction from mobile data: A preliminary study. arXiv preprint arXiv:2012.02359.
html.
R OpenAl. 2023. Gpt-4 technical report. arXiv, pages 087744.7
Minsu Park, Chiyoung Cha, and Meeyoung Cha. 2012. Depressive moods of users portrayed in twitter. In Proceedings of the 18th ACM International Con- ference on Knowledge Discovery and Data Mining, SIGKDD 2012, pages 1-8.
Yinhan Liu, Myle Oit, Naman Goyal, Jingfei Du, Man- dar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining ap- proach. arXiv preprint
Sungjoon Park, Kiwoong Park, Jaimeen Ahn, and Alice Oh. 2020. Suicidal risk detection for military per- sonnel. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages
Kathryn J. Lively and Brian Powell. 2006. Emotional expression at work and at home: Domain, status, or individual characteristics? Social Psychology Quarterly, 69(1):17-38
Online. Association for Computational Linguistics
Paul H Lysaker, Kelly D Buck, Giampaolo Salvatore, Raffaele Popolo, and Giancarlo Dimaggio. 2009. Lack of awareness illness in schizophrenia: con- ceptualizations, correlates and treatment approaches. Expert review of neurotherapeuties 97)):133510033
Inna Pirina and Çaăn Çöltekin. 2018 Identifying de- pression on Reddit: The effect of training data. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applica- tions Workshop & Shared Task, pages 9-12, Brussels, Belgium. Association for Computational Linguistics.
Sourab Mangrulkar, Sylvain Gugger, I Lysandre Debut, Younes Belkada, and Sayak Paul. 2022. Peft: State- oh-the-art parameter- earfmeter-efficientt fine-tuning methods. https:/ / thub. cww htggg/ugggigg wwwuuinngfacee weaeggingffcpppppe peft.
Andrew G Reece and Christopher M Danforth, 2017. Instagram photos reveal predictive markers of depres- Sion. EPJ Data Science, 6(1):15.

---

Koustuv Saha, Ted Grover, Stephen M Mattingly, Vedant Das Swain, Pranshu Gupta, Gonzalo Mar- tinez, Pablo Robles-Granda, Gloria Mark, Aaron Striegel, and Mummun De Choudhury. 2021. Person- centered predictions of psychological constructs with social media contextualized by multimodal sensing. Proceedings of the ACM on Interactive, Mobile, Wear- able and Ubiquitous Technologies, 5(1):1-32.
October 18-20, 2019, Proceedings 18, pages 194- 206. Springer.
Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou, 2020. MoBileBERT: compact task-agnostic BERT for resource-limited devices. In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics. pages 2158-2170, Online, Association for Computa- tional Linguistics.
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv: 190.00100
Simon Suster, Stéphan Tulkens, and Walter Daclemans. 2017. A short review of ethical challenges in clinical natural language processing. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing, pages 80 87, Valencia, Spain. Associa- tion for Computational Linguistics.
Lisa K Sharp and Martin S Lipsky. 2002. Screening for depression across the lifespan: a review of measures for use in primary care settings. American family physician, 66(6):1001.
Silero Team. 2021. Silero vad: pre-trained enterprise- grade voice activity detector (vad), number detec- tor and language classifier. https:/ https://github com/ s /etrr/////sellaaaaae
Micah Sheller, Brandon Edwards, Anthony Reina, Jason Martin, Sarthak Pati, Aikaterini Kotrotsou, Mikhail Milchenko, Weilin Xu, Daniel Marcus, Rivka R Colen, et al. 2020. Federated learning in medicine: facilitating multi-institutinaal collabora- tions without sharing patient data, Scientific reports,
ML Tlachac, Katherine Dixon- Dixon-Gordon, and Elke Run- densteiner. 2021. Screening for suicidal ideation with text messages. In 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), pages 1-4, IEEE.
10.1):1-12
Jaemin Shin, Yuanchun Li, Yunxin Liu, and Sung- Ju Lee. 2022. Fedbalancer: Data and pace control for
ML Tlachac, Ricardo Flores, Miranda Reisch, Katic Houskeeper, and Elke A. Rundensteiner. 2022a. Deprest-cat: Retrospective smartphone call and text logs collected during the covid- 19 pandemic to screen for mental illnesses. Proc. ACM Interact. Mob. Wear- able Ubiquitous Technol. 6(2).
efficient federated learning on heterogeneous clients. In Proceedings of the 20th Anmual International Con- ference on Mobile Systems, Applications and Ser- vices, MohiSys '22, page 436-449, New York, NY, USA. Association for Computing Machinery.
Santiago Silva, Boris A Gutman, Eduardo Romero, Paul M Thompson, Andre Altmann, and Marco Lorenzi. 2019. Federated learning in distributed med- ical databases: Meta-analysii of large-scale subcorti- cal brain data. In 2019 IEEE 16th international sym- posium on biomedical imaging (ISBI 2019), pages 270-274, IEEE
ML Tlachac, Ricardo Flores, Miranda Reisch, Rimsha Kayastha, Nina Taurich, Veronica Melican, Connor Bruneau, Hunter Caouette, Joshua Lovering, Firmal Toto, et al. 2022b. Studentsadd: Rapid mobile de- pression and suicidal ideation screening of college students during the coronavirus pandemic, Proceed- ings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 6(2):132..
Sanjay Sri Vallabh Singapuram, Fan Lai, Chuheng Hu, and Mosharaf Chowdhury. 2022. Swan: neural engine for efficient dnn training on smartphone socs. arXiv preprint arXiv:2206. 04687.
ML Tlachac and Elke Rundensteiner. 2020. Screening for depression with retrospectively harvested private versus public text. IEEE IEEEjournal of biomedical and health informatics, 24(11)3326-333
Congzheng Song and Ananth Raghunathan. 2020. In-
formation leakage in embedding models. In Pro-
ML Tlachac, Avantika Shrestha, Mahum Shah, Ben- jamin Litterer, and Elke A. Rundensteiner. 2022c. Automated construction of lexicons to improve de- pression screening with text messages. IEEE Journal of Biomedical and Health Informatics, pages 1-8.
ceedings of the 2020 ACM SIGSAC Conference on
Computer and Communications Security, CCS '20. page 377-390, New York, NY, USA. Association for Computing Machinery.
Statista. 2022.
Number of smartphone sub- scriptions worldwide from 2016 to 2021, with forecasts from 2022 to 2027. https:
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. Llama: Open and effi- cient foundation language models. arXiv preprint 130..1..0...3971
//www. stwwista.s htmm/sttatisiics3369999... number-of
Chi Sun, Xipeng Qiu, Vige Xu, and Xuanjing Huang. 2019. How to fine-tune bert for text classification? In Chinese Computational Linguistics: 18th China National Conference, CCL 2019, Kunming, China,
Debra Trampe, Jordi Quoidbach, and Maxime Taquet. 2015. Emotions in everyday life. PloS one, 10( 15::00455555..

---

Unicode, 2022. Emoji charts. https://unicode. org/ emoji/cchrttsfull-eeee meaill--mji--ttthhee
Rui Wang, Weichen Wang. Alex daSilva, Jeremy F Huckins, William M Kelley, Todd F Heatherton, and Andrew T Campbell. 2018. Tracking depression dy- namics in college students using mobile phone and wearable sensing. Proceedings of the ACM on Inter- active, Mobile, Wearable and Ubiquitous Technolo- gies, 2(1):1-26.
Alper Kursat Uysal and Serkan Gunal. 2014. The im- pact of preprocessing on text classification. Informa- tion Processing & Management, 50(1): 104-112.
Akhil Vaid, Suraj K Jaladanki, Jie Xu, Shelly Teng. Arvind Kumar, Samuel Lee. Sulaiman Somani, Is- han Paranipe, Jessica K De Freitas, Tingyi Wanyan, al. 2021. Federated learning of electronic health records to improve mortality prediction in hospital- ized patients with sovid-19: machine learning ap- proach. JMIR medical informatics, 9(1 24422777
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier- ric Cistae, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer. Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020. Transform- ers: Sfate-of-the-ar natural language processing. In Proceedings of the 2020 Conference on Empirical Methods Natural Language Processing: System Demonstrations, pages 45, Online. Association for Computational Linguistics.
S Vijayarani, Ms J Ilamathi, Ms Nithya, et al. 2015. Pre- processing techniques for text mining-an overview. International Journal of Computer Science & Com- munication Networks, 5(1):-16.
Erin Vogel and Jason P Rose. 2016. Self-reflection and interpersonal connection: Making the most of self- lulf-preseetation on social media. Translational Is- sues Psychological Science, 2(3):294.
Zheng Xu, Yanxiang Zhang, Galen Andrew, Christo- pher A Choquette-Chool Peter Kairouz, H Brendan McMahan, Jesse Rosenstock, and Yuanbo Zhang. 2023. Federated learning of ghoard language mod- els with differential privacy. Proceedings of the 61th Annual Meeting of the Association for Computa- tional Linguistics. Toronto, Canada, Association for Computational Linguistics.
Quan Wang. Ignacio Lopez Moreno, Mert Saglam, Kevin Wilson, Alan Chiao, Renjie Liu, Yanzhang He, Wei Li, Jason Pelecanos, Marily Nika, and Alexander Gruenstein. 2020. VoiceFilter-Lite Streaming Tar- geted Voice Separation for On- Deviee Speech Recog- nition. Proc. Interspeech 2020, pages 2677-2681
Andrew Yates, Arman Cohan, and Nazli Goharian. 2017. Depression and self-harm risk assessment in online forums. In Proceedings of the 2017 Conference Empirical Methods in Natural Language Processing. pages 2968-2978, Copenhagen, Denmark. Associa- tion for Computational Linguistics.
Quan Wang. Hannah Muckenhirn, Kevin Wilson, Prashant Sridhar, Zelin Wu, John R. Hershey, Rif A. Saurous, Ron J. Weiss, Ye Jia, and Ignacio Lopez Moreno. 2019. VoiceFilter: Targeted Voice Separa- tion by Speaker-Conditions In Proc. Interspeech 2019. pages
Spectrogram Masking,
Rui Wang, Min S. H. Aung, Saced Abdullah, Rachel Brian, Andrew T. Campbell, Tanzeem Choudhury, Marta Hauser, John Kane, Michael Merrill, Emily A. Scherer, Vincent W. S. Tseng, and Dror Ben-Zeev. 2016. Crosscheck: Toward passive sensing and detection of mental health changes in people with schizophrenia. In Proceedings of the 2016 ACM In- ternational Joint Conference on Pervasive and Ubiq- uitous Computing, UbiComp "16, page 886-897, New York, NY, USA. Association for Computing Machinery.
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago On- tamon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, and Amr Abmed. 2020. Big bird: Trans- formers for longer sequences. Advances in Neural Information Processing Systems, volume 33, pages 17283 17297. Curran Associates, Inc.
Tianshu Zhang, Changchang Liu, Wei-Han Lee, Yu Su, and Huan Sun. 2023a. Federated learning for seman- tic parsing: Task formulation, evaluation setup, new algorithms. In Proceedings of the 61th Anmual Meet- ing of the Association for Computational Linguistics, Toronto, Canada. Association for Computational Lin- guistics.
Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanic Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T Campbell. 2014. Stu- dentlife: assessing mental health, academic perfor- mance and behavioral trends of college students us- ing smartphones. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiq- uitous computing, pages 3-14.
Zhuo Zhang, Xiangjing Hu, Jingyuan Zhang, Yating Zhang, Hui Wang, Lizhen Qu, and Zenglin Xu. 2023b. Fedlegal: The first world federated leamn ing benchmark for legal nlp. In Proceedings of the 61th Annual Meeting of the Association for Computa- tional Linguistics, Toronto, Canada. Association for Computational Linguistics.
Rui Wang, Weichen Wang, Min S. H. Aung, Dror Ben- Zeev, Rachel Brian, Andrew T. Campbell, Tanzeem Choudhury, Marta Hauser, John Kane, Emily A. Scherer, and Megan Walsh. 2017. Predicting symp- tom trajectories of schizophrenia using mobile sens- ing. Proc. ACM Interact. Mob, Wearable Ubiquitous Technol., 1(3).

---

A Federated Learning
Fed hhrrppet
FedTherapit apiet
Federated Learning (FL) (McMahan et al., 2016; Li et al., 2020; Shin et al., 2022; Liu et al., 2022) is a recent machine learning paradigm that enables model training without data collection. FL globally trains model from the distributed user data mul- tiple mobile devices. We apply FL with FedTher- apist to utilize the privacy-sensitive text data on smartphones for effective mental health monitor- ing. The most commonly adopted FL methodol- ogy is FedAvg (McMahan et al., 2016), which operates synchronously across clients over mul- tiple rounds as follows: at the beginning of each round, the FL server randomly samples K clients out of total N clients (K < N). The sampled clients download the up-to-date model weights WR at round R then train on the local client data for E epochs. The client's 's updated model (w/++ for client i) gets uploaded to the server, which is further aggregated into a new updated model as WR+1 + Ei= ii=1 "A "R++ where n, indicates client i's data size and n indicates the data size sum on K sampled clients.
LOMND: 1015 caasunn skoo?
Taak Record & submit your voice. incomplete
Teak Downlood required files. incomplate
your volcs. quet START
READ OUT LOUD BELOW WHILE RECORDING
f- 101M/B down load)
She neel your dark mutl in graapy wash noter your. Etrtt ask cortn bustons will greatlt apprectated, Chidden can consume many frutt cand in one artting Cooperation allong anderttandiig une ciinna Chjesta made pontus bositfu.
Task Start the
camyy un only nag
START
1hss take a whel start
Everytay Tank: Tell about your day!
suRver
STGRT
You con bocess the survey with address
Figure 3: Screenshots of our data collection applica- tion. Participants completed three tasks to join the study. First, participants were asked to upload their voice sam- ple (Task 1) to use VoiceFilter (Wang et al., 2019) (Sec- tion C. Participants then downloaded the model files used in our local data collection module (Task 2), and started the study (Task 3). We asked the participants for daily mental health status report during the study.
$72 compensation. Figure 4 shows the word count of collected data on each participant, where an av- crage of 13,492+114,22 and 4.521=5,994 words are collected on speech and keyboard input, respec- tively. Figure 5 shows the count of the participant responses on each mental health status.
Various recent healthcare applications, such as brain cancer imaging (Sheller et al., 2020; Lee et al., 2021), clinical support for COVID-19 (Dayan et al.. 2021; Vaid et al., 2021), MRI data analysis (Silva et al., 2019), blood pressure monitoring (Brophy et al.. 2021), Parkinson's 's diseases diagnosis (Chen et al., 2020), and stress level prediction (Can and Ersoy, 2021) have employed FL. Additionaly, FL has been recently approached on NLP tasks, such next word prediction (Xu et al., 2023), semantic parsing (Zhang et al., 2023a), and legal text data processing (Zhang et al., 2023b). None of the previ- ous approaches, however, capitalize a user's ' speech and keyboard input on smartphones with FL. We propose FedTherapist, mental health monitoring system to leverage beer-generated linguistic expres- sions smartphones with FL.
As we conducted our study via MTurk, we could not directly monitor whether users fully followed our instructions while collecting data. For example, we could not confirm if users installed our data collection application on their main smartphone. After collecting data, we removed six out of 52 participants from the evaluation for showing abnor- mal data statistics. Three were excluded for not having keyboard input throughout the data collec- tion duration. The other three were dropped for being completely stationary for ten days, which could imply that the users might have installed the application on their secondary phones.
C Data Collection App Implementation
B Data Collection Study Details
C.1 Speech Text Collection Module
The data collection study was conducted for three weeks. Online Amazon Mechanical Turk (MTurk) participants freely joined our study during that pe- riod by downloading our Android upplication, com- pleting a survey, and executing three tasks as illus- trated in Figure 3. The survey asked the participants to confirm if they were fully aware of the data that were being collected during the study. The data col- lection study for each user lasted for ten days, with
Capturing user speech from the continuous audio input on a smartphone presents several challenges: (1) As the user could speak at any time of the day, continuous microphone recording is desired, lead- ing to a significant smartphone battery drain. (2) The voice of non-target users could be included in the audio. This must be excluded not only to achieve effective mental health prediction of the target user but also to protect the privacy of non-

---

wurd word count
60000
given audio file. If the classifier outputs language that the user speaks, we regard the audio as high- quality recording of the user's voice and apply a speech recognition model to retrieve the text data.
40000
Speech
20000
80000
We implemented the speech text collection mod- ule on our Android app to demonstrate its on- capability and conduct FedTherapist evaluation on real-worke user devices. We adopted three publicly available models: a lightweight VAD model from Google WebRTC (Google, 2022b), language classifier from Silero (Team, 2021) that searches among 95 languages, and an offline speech recog- nition model, Vosk (Cephei, 2022) (en-us model). To enable inference on smartphones, we reduced the number of convolution layers from eight to four and changed the bidirectional LSTM to uni- directional LSTM the VoiceFilter model. For models that do not provide an Android implementa- tion (e.g., VoiceFilter), we used MNN (Jiang et al., 2020) to convert the Py Torch version of the models into an Android executable. To minimize the smart- phone battery drain and preserve the user experi- ence, we implemented to fully execute the module only when the device is idle and being charged. The recorded audio files from the duty cycling sys- tem are temporarily saved on the device until the device gets connected charger.
Keyboard
20000
10000
0
10
20
30
40
participants sorted word count
Figure 4: Word count of each participant's collected speech and keyboard input from the 10-day study.
Count
Couut
8 &
34
10 9) 50 60 70 S01T0
PHQQ9 acores
Self-report scores
(a) Depression (PHQ-9).
(b) Stress (low high).
Caunt
B
5) 68 70
90
10) Self-reported scores
20.30
Sclf-roport Scores
(c) Anxicty (low high).
(d) Mond (sad- happy).
Figure 5: Count of participant responses different
mental health status scores.
target users. (3) The user voice could be recorded in low quality with noise, as the recording occurs in unconstrained, real-world environments. The dis- tance between the user's mouth and the smartphone microphone could be far, and the background noise in the audio makes it difficult to acquire a clear voice from a user.
C.2 Keyboard Text Collection Module
The keyboard text collection module captures all the input text the user enters with their smartphone keyboard. Our implementation on our app tracked the text input events on Android smartphones us- ing the Android Accessibility Service (Android, 2021). We preprocessed the collected text as fol- lows (Uysal and Gunal, 2014; Vijayarani et al.. 2015): (1) Emails, hashtags, links, mentions, punc- tuations, and numerical values were removed: (2) emojis were replaced with CLDR (Common Locale Data Repository) Short Names (Unicode, 2022), and abbreviations were replaced with what they stand for; (3) the letters that were excessively re- peated were reduced; and (4) typos were corrected using the auto-correction API (Norvig, 2016).
We implemented our speech text collection mod- ule to collect the user speech data while mitigating the aforementioned challenges as follows: To avoid continuous recording (Challenge 1), we adopted a duty cycling system with a Voice Activity De- tector (VAD) model (Wang al., 2014): it starts recording and infers on VAD model for a minute in every four minutes check whether there is an ongoing conversation. The module continues recording if a conversation is detected, where the recorded audio is inferred on VoiceFilter (Wang et al., 2019, 2020), a model that removes the non- target users' voice based on the target user's sample voice (Challenge 2). Next, the VAD model is ap- plied the VoiceFillerr output to remove audio without the target user's voice. To ensure that the processed audio contains the target user's voice (Challenge 3), we designed the module to leverage a language classifier that outputs the language of a
D Detailed Experimental Methodologies
D.1 Handling Long Input Sequences
Our model input for four mental health prediction tasks (Section 4.1. often exceeds the max input size of the models, such as BERT (512 words on BERT-base (Devlin et al., 2019)) or LLM (2048

---

1.0
0.-
0.55
0.9
0.8
AUROC
AUROC
0.52
1088
0.8-
train val
8.6
loss
0.43-
0.40
0.45-
0.3
epochs
epochs
epochs
epochs
(a) BERT-LOSS.
BERT-AUROC.
(c) LLM-Loss.
(d) LLM-AUROC.
Figure 6: Loss and AUROC results train, validation, and samples epochs from fine-tuning experiments in Section 3.1. Figure 6a and 6b indicate fine-tuning experiment of End-to-End BERT MLP using a pre-trained RoBERTa-base (Liu et al., 2019) model with a learning rate of 0.0001. Figure and 6d depict fine-tuning experiment of LLM pre-trained LLaMa-7B model with learning rate of 0.0002. More details experimental methods are found Section 3.1 and Appendix D. All subgraphs share the same legend with Figure 6a.
words LLaMa-7B (Touvron al., 2023)). In such cases, we divided the input text into 512-word chunks and tested the following methodologies, inspired by Sun et al. (2019): (1) full+pool: we forward the chunks through the embedding model and then a maxpool layer. One embedding vector is generated, which we further use for prediction with multilayer perceptron (MLP). We include as many chunks our GPU memory permits. (2) single: we forward chunks individually through the embedding model and MLP. We test the model performance by predicting based on the averaged logits from multiple chunks. We experimented with both options and reported better performance.
restrictions, selected batch size of 16 for sin- gle experiments and 2 for full+pool experiments. For Section 4 experiments with FL (FL+Texi and FedTherapist), we applied FedAvg (McMahan et al., 2016) (Appendix A), widely-used FL al- gorithm; exploring other FL approaches is not the focus this work. We report better performance among the context models and ensemble methods for CALL experiments. We trained a Logistic Re- gression (LR) model as an MLP and applied Lasso regularization for the regression. A learning rate of 0.01 and batch size of 10 were adopted.
For vross-validation, we employed the Leave- One- -ue-User-Out (LOUO), splitting participants into a single test user and training users. We trained the model on the training users for 1000 epochs for CL and 1000 rounds for FL.. We then test on the single test user, with each participant rotated as the test user. We used three random seeds to repeat and average the performance.
D.2 Training and Cross Validation
For Section 3.1 experiments with training BERT and LLM, to target resource-constraine mobile scenarios, we applied LoRA (Hu et al., 2022), a lightweight fine-tuning technique. Moreover, we trained the 8-bit quantized LLaMa-7B model as LLM for resource-efficieet training. For the BERT experiment, applied DistilBERT-base (Sanh al., 2019), RoBERTa-base (Liu al., 2019), and BERT-base (Devlin al., 2019) and reported the best performance. For each experiment, we ap- plied sequence classification models and LoRA from PEFT (Parameter-Efficien Fine-Tuning) li- brary (Mangrulkar et al., 2022), both from Hug- gingFace (Wolf et al., 2020). Based on Hu et al. (2022), adopted the learning rate from set of [0.0005, 0.0004, 0.0003, 0.0002, 0.00003] with linear learning rate decay scheduler. We fine-tuned BERT and LLM until 30 and epochs, respectively, and reported the test AUROC from an epoch with minimum validation loss. Given our GPU memory
E Evaluation Results
E.1 Comparison with Existing Methods
We conducted a comprehensive comparative anal- ysis of FedTherapist against closely related ap- proaches:
CNN + MLP: Modeled after Yates et al. (2017) and Kim (2014), this CNN-based text classifica- tion employs convolutional layer and MLP. We used Yates al. (2017)'s hyperparameters for depression identification.
Bag-of-words + MLP: This model employs an MLP with bag-of-words input with TF-IDF, based on Pirina and Çöltekii (2018)'s depression detection. We experimented with a n-gram range

---

of [1, 3] and representation sizes from [1000, 5000, 10000, 20000].
larger parameter size (BERT-base, BIRRT-large) or a model for longer input sequences (BigBird) underperforms the lighter models on all tasks. We suspect that these disparities between models arise from variations in their pretraining data and meth- ods, particularly in how well they correlate with our user-generated speech and keyboard data.
Empath + MLP: Taking eues from Tlachac et al. (2021), which utilizes smartphone SMS mes- sages, the model utilizes output representations from Empath (Fast et al.. 2016), which analyzes text over 200 lexical categories.
To ensure a fair comparison, we trained all base- line models using learning rates from [0.0001, 0.001, 0.01, 0.1J and evaluated their performance at each of the 1000 epochs. We measured the best per- formance achieved by these models over the 1,000 epochs. For FedTherapist, we reported the test AU- ROC after its 1,000 epochs without selecting its peak performance. Despite this, FedTherapist still demonstrated superior results of 0.721 AUROC, while CNN + MLP, Bag-ofe wordss + MLP. and Em- path + MLP each resulted in 0.418, 0.564, 0.635 AUROC respectively.
E.3 Fine-tuning BERT for FedTherapist In our Fixed-BERT + MLP model for FedThera- pist, utilize pretrained BERT models trained on traditional text corpuses such as Wikipedia BookCorpus (Devlin et al., 2019). As FedThera- pist primarily deals with conversation bbssdd, user- generated text data from smartphones, we evaluated unsupervised fine-tuning of BERT on SODA (Kim et al., 2022), a large-scale dialogue dataset. Three pretrained models, BERT-base, RoBERTa- base, and DistilBERT- were used. BERT-base was fine-tuned using Masked LM (MLM) and Next Sentence Prediction (NSP) objectives. We utilized only the MLM objective for RoBERTa-base, mir- roring RoBERTa-base' 's strategy (Liu et al.. 2019). For DistilBERTThase, we distilled the knowledge from our SODA-pretrained BERT-base model. We conducted fine-tuning in two ways: (1) SODA: we consider each dialogue in the SODA dataset as single input and (2) SODA- the dialogue was divided per speaker and trained as separate in- puts, reflecting FedTherapist's s input data that only contains texts from single mobile user.
The result shows that FedTherapist outperforms other methods in depression detection on our dataset. Limited training samples from only 46 users in our dataset might have hindered the ef- fectiveness of CNNs trained from scratch in contrast, Yates et al. (2017) trained with Reddit posts from more than 100,000 users. Other results suggest that using frozen BERT in FedTherapist shows better mental health monitoring performance than using bag-of- wordds or Empath.
E.2 Comparison of Text Embedding Models
Table 4 reveals that fine-tuning with BERT-base significantly improved performance (0.197 AU- ROC) on the depression task, while RoBERTa-base and Distil showed comparable perfor- mance (~ 0.7 AUROC). SODA and SODA-single resulted in similar performances, potentially due to lack of modeling objectives understand con- versation structure. We leave the inclusion of such objectives as future work.
Table 3 reports the performance of the following text embedding methods: widely used BERT mod- els (BERT-base, BERT-large (Devlin et al., 2019), RoBERTa (Liu et al., 2019)), lightweight BERT models tailored for mobile devices (ALBERT (Lan et al., 2020). MobileBERT (Sun et al., 2020), Dis- GIBERT (Sanh et al., 2019)). BART base (Lewis et al.. 2020), and a text embedding model for longer sequences, BigBird (Zaheer et al., 2020). For each model, used pretrained uncased models from HuggingFace (Wolf et al.. 2020), and used the full+ method (Appendix D.1) for user texts exceeding a model's 's maximum input length.
E.4 System Overhead of FedTherapist
We tested the system overhead of FedTherapist on two scenarios: (1) data collection and (2) on-device training. For the data collection scenario, we tested when the device is performing (i) duty cycling and (ii) full execution of the speech text collection mod- ule (Appendix C.1), which runs only when the device is idle and being charged. Note that we measured only the speech data collection and not keyboard data collection as the latter is lightweight without running any deep learning models.
DistilBERT outperforms other methods in de- pression, while MobileBERT outperforms other methods in stress, anxiety, and mood. As Mobile- BERT shows the lowest performance on depres- sion, we selected DistilBERT for our FedTherapist evaluation in Section 4, which performs compara- bly to other methods in other tasks. BERT with

---

Methods
ALBFFRT-base
MabileBERT 25M
DistiBERT-hase 66M
RoRERTa-hase
BERT-base 110M
BERT-large
BART-base
RigRird
Parameter Size
11M
110M
340M
140M
128M
Depression (AUROC ")
0.526 00007
0.319:000
0.775:0000
0.729 10.00
0,50010011
0.622:0004
0.636
0.626:00009
Stress (MAF.)) (]
24.75:0002
23:98+001
24.78:0.01
24.59-0.05
24.80:10.06
24.76:1004
24.29:005
24.95-0.0
Anxiety (MAF.))
26.95-0002
25.99+001
27.30+0.01
27.05-0.06
27.07:00.44
27.05+000
26.74=004
27.25-0.04
Mood (MAE.
23.57=002
22.82+001
23.67+0.02
23.79-0.02
23.54+0002
23.01+0033
23.09:002
23.58-0.04
Table 3: Mental health monitoring performance on different text embedding methods at epoch 200.
Methods
BERT-basc
RoBEKTa-base
DistiBBEKT-haad
creased CPU (> 45%), memory usage (> 800MB), and latency (> 10 seconds per epoch) over Fixed- BERT + MLP. While Fixed-BERT + MLP still con- sumes > 25% in CPU, its per-epoch latency is ex- tremely short (< 0.1 seconds), and the overhead in training is negligible. LLM measurement was not possible the model was not loaded, requir- ing 8GB memory, which exceeds the test smart- phones' memory. We chose Fixed-BERY + MLP for FedTherapist, given that it achieves the highest mental health detection performance (Table 1) and the lowest smartphone overhead.
Pretrained
0.560
0.729
0.775
SODA
0.757
0.696
0,701
SODA-singlc 0,744
0.665
0.714
Table 4: Effect of fine-tuning on text embedding meth- ods on mental health monitoring performance, measured in AUROC on depression task.
Testing each model design candidate from Sec- tion 3.1 for on-device training, we used Termux, a Linux Terminal emulator on Android, for un- modified PyTorch model training following previ- work (Singapuram al., 2022). We utilized RoBERTa-base (Liu et al., 2019) and a linear clas- sifier as BERT and MLP, respectively, and LLaMa- 7B (Touvron et al., 2023) model as LLM. To mimic FedTherapist's s user device training, we trained on a randomly generated text dataset that reflects the average word count from our study participants.
F
Discussion
F.1 Context Features
We designed our context-aware language learning (CALL) based on eight context models, where the context models train on the text data collected at each context (Section 3.2). However, a combina- tion of different contexts (e.g., texts that are col- lected at nighttime and at home, i.c., TN + LH) could be viable option improve the perfor- mance of FedTherapist with CALL. Moreover, con- texts other than those we considered (time, location, motion, and application) could be used to further improve CALL For the speech input, audio features such as a tone or pitch of a voice could be utilized, For the keyboard input, features such as typing speed (e.g., word per minute) could be leveraged. We leave such an investigation future work.
For CPU and memory measurement, we reported average per-core utilization on smartphone CPU and the max memory usage from the 5-min execu- tion of each sub-scenarios. For latency of speech text collection module, measured the time to process 15-seconds audio file that our module pro- cesses. We measured the time to train one epoch as latency for on-device training. We used two com- modity Android smartphones for the experiment: Pixel (2016) and Galaxy S21 (2021).
Table presents system overhead results. There is a significant gap in CPU and memory usage between the data collection sub-scenarios. Duty cy- cling exhibits minimal CPU (< 1.5%) and memory usage (< 200MB), while the speech text collection module significantly increases CPU (> 15%) and memory (> 1GB) usage due to loading and operat- ing multiple models like VoiceFilter (Wang al., 2019). These results support our design to run the module only when the device is idle and charging. Moreover, the latency results confirm that our mod- ule processes audio in real-time with processing time shorter than the audio length.
F.2 Applicability of FedTherapist on Users with Little Speech and Keyboard Input
As FedTherapist takes users' speech and keyboard input from smartphones, one might wonder about the performance of FedTherapist on users who pro- duce less input; i.e., users who do not type much on smartphones speak less than others. Figure 7 shows the test loss on users' input word count from speech and keyboard on the depression task. For either input type, correlation between the word count and the test loss is not observed. The par- ticipants with less word count (< 10,000) mostly
For FL, End-to-Ent End BERT+MLP shows in-

---

Scenario
Sub-Scenario
Device
CPU (%)
Memory (MB)
Latency (sec)
Pixel
1.16
119
N/AT
Duty Cycling
Data Collection (Soction C)
Galaxy S21
0.29
191
N/A
Pixel
49.21
1245
11.20
Speech Text Collection Module
Galaxy $21
15.84
1104
9.76
Pixel
27.05
215
0.08
MLP
Galaxy S21
42.80
222
0.03
Pixel
85.59
842
13.63
On-Device Training
End-to-End BERT + MLP
Galaxy
49.68
886
38.89
Pixel
N/A
N/A
N/A
LLM
Galaxy S21
N/A
N/A
N/A
Table 5: Smartphone overhead on scenarios of FedTherupist: CPU denotes average per-core utilization post 5-min
execution. I: restricted to measure individual Voice Activity Detector model execution from its API during
duty cycling; 9; N/A measurements, the model is not loaded on tested smartphones due to memory constraints,
cally tracked smartphone-based features on sleep. mobility, conversations, and phone usage. Wang et al. (2014) also conducted the StudentLife study, which identified the correlation between smart- phone sensing and the mental wellbeing measures (e.g., stress, loneliness, depression, etc.) univer- sity students. Mehrotra et al. (2017) conducted study that finds correlation between users' emo- tional states and mobile phone interaction, that in- cludes application usage log, notification log, and communication log (e.g., count and length of calls and SMSes). Canzian and Musolesi (2015) intro- duced a depression detection system based on loca- tion trajectories and daily questionnaire responses of users. DepreST-CAT (Tlachae al., 2022a) is depression and anxiety screening method that leverages the time-series call and text logs with- out the language content on smartphones. and Sano (2020) proposed a framework that automati- cally extracts efficient temporal features from skin conductance, skin temperature, and acceleration signals for mental health assessment. cStress (Hov- sepian et al., 2015) is stress monitoring system wearable sensors that captures users' ECG (electro- cardiograph) and respiration data. LiKam Wa al. (2013) and Morshed al. (2019) developed models that predict individuals' moods based on mobile sensors or smartphone usage data.
test 12
test loss
word count
word count (b) Keyboard.
(a) Speech.
Figure 7: Test loss on users' input word count from speech and keyboard experiments. The results show that FedTherapisi is applicable on users with little speech and keyboard input (< 1,000 words on each input).
resulted in low test loss (< 1). user with only 128 words of keyboard input even resulted a test loss 0.154. This result shows that FedTherapist could also be effective on users with little speech and keyboard input.
G Related Work
Mental Health Monitoring on Mobile Devices The early detection and treatment of mental ill- nesses are the keys to reducing the negative impact of the discases (Sharp and Lipsky, 2002; Costello, unaware of their mental health problems and this results in delay of treat- ment (Lysaker al., 2009; Epstein et al., 2010). To combat this issue, many researchers explored the possibility of passively monitoring users' mental health on mobile devices.
2016). Many patients
In summary, much research has contributed to mobile mental health monitoring systems with non-text data. However, believe that utiliz- ing user-generated linguistic expressions could be viable option achieve more accurate mental health monitoring on mobile devices. Thus, we propose FedTherapist, a mobile system that effec- tively leverages text data from users to achieve
Most previous approaches focused develop- ing mental health monitoring systems with non-text data, leveraging mobile sensors and device usage statistics of user. Wang et (2016, 2017, 2018) explored the association between the symptoms of schizophrenia and depression with the automati-

---

accurate mental health monitoring while preserv- ing user privacy. While there have been few ap- proaches that utilized the text data to perform mo- bile health screening on mobile devices (Liu et al., 2020; Tlachac and Rundensteiner, 2020; Tlachac et al., 2022b, 2021, 2022c), they were limited to partial text data (e.g., text data from messenger ap- plications), or required user's active text input. Moreover, these approaches do not discuss how such large and noisy text data could be effectively utilized for mental health prediction, nor explore different model designs for language-based mental health monitoring on smartphones. Unlike such previous approaches, FedTherapist fully utilizes a user's speech and keyboard input on every ap- plication on smartphone. We explore multiple model structures, including BERT and LLMs, and propose user context-aware methodology to ef- fectively leverage raw data with FedTherapist to achieve accurate mental health monitoring.
focus on developing mental health monitoring system on smartphones owned by more than six billion people (Statista, 2022). We fully leverage smartphone speech and keyboard input for effective mental health monitoring.
Mental Health Monitoring on Social Media
Leveraging user's linguistic expressions for men- tal health screening has been widely studied for social media, where users freely express their opin- ions and share their status in their own words. User posts on various platforms such as Red- dit (De Choudhury et al., 2016; Yates et al., 2017: Aragón et al., 2019; Ambalavanan et al., 2019),
Twitter (Park et al., 2012; Coppersmith et al., 2014;
Husseini Orabi et al., 2018; Chen et al., 2018),
Facebook (De Choudhury et al., 2021; Saha et al., 2021), and Instagram (Reece and Danforth, 2017)
have been utilized predict the mental health status of users. Previous studies identified spe- cific pattern of linguistic expressions related to the mental status change or disorders; De Choudhury et al. (2016) identified distinctive markers from Reddit users related to linguistic structure and post- readability that characterize suicidal ideation. Phys- exploited to complement
ical behavior sensing social media data in mental status prediction (Saha al., 2021), and temporal emotion measures were leveraged to detect the physiological constructs on Twitter (Chen al., 2018).
However, social media-based approaches have limited applicability as they only be used by active social media users. Moreover, mental health prediction based on social media could be biased as users tend to idealize themselves on social media rather than expressing their real emotions (Vogel and Rose, 2016; Herring and Kapidzic, 2015). We


Reviews:
[{"review_id": "gI9oLHR5zY", "reviewer": "Reviewer_Y3PY", "summary": "", "questions": "- What procedure was used for hyperparameter tuning for end-to-end BERT since the claim is that the model is over-fitting. Was a validation set used for it or some early stopping used instead of using 200 epochs as mentioned in appendix?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your thoughtful comments and feedback. In our revised manuscript, we intend to address these points as follows:\n\n**Comment 1 (Question A): Hyperparameter tuning procedure for end-to-end BERT**\n\nWe appreciate your question regarding the hyperparameter tuning process for our end-to-end BERT experiments. In the updated manuscript, we will provide a detailed clarification of this procedure, outlined below:\n\nFor the *previous end-to-end BERT experiment* in Section 3.1, we applied LoRA [1], a lightweight fine-tuning technique primarily based on hyperparameters from Huggingface's RoBERTa fine-tuning example [2]. Specifically:\n\n* We selected from a learning rate set of [0.0005, 0.0004, 0.0003, 0.0002, 0.00003], as recommended in the LoRA paper, reporting the highest AUROC. We used the linear learning rate decay scheduler as in LoRA. \n* While the LoRA paper for RoBERTa used a batch size of 16 or 32, we only used 16 due to our GPU memory restrictions (24GB on NVIDIA RTX 3090).\n* We performed cross-validation on 46 clients with the Leave-One-User-Out (LOUO) strategy by splitting the data into 1 test user and 45 train users. We trained until 30 epochs as in LoRA, and tested AUROC for every epoch, repeating such a process 46 times by setting each user as a test user. In the paper, we reported the highest average AUROC from all 30 epochs. Note that for Fixed-BERT + MLP (FedTherapist), we straightforwardly reported the test AUROC after its 1000 epochs without selecting its peak performance.\n\nTo enhance clarity, we conducted a *new experiment* using LOUO cross-validation, segmenting the users into 1 test user, 5 for validation, and 40 for training. LLaMa 7B was also experimented as LLM, with parameters based on LoRA’s GPT-2 tuning. The table below presents the average test AUROC at the epoch with the lowest validation loss:\n\n| Methods| Fixed-BERT + MLP | End-to-End BERT + MLP | LLM   |\n|-----|-------|------|-------|\n| Depression (AUROC)| 0.716            | 0.524                 | 0.406 |\n\nFixed-BERT+MLP outperforms End-to-End BERT+MLP and LLM in experiments that included validation set users. This advantage results from BERT and LLM overfitting on our 40-user training sample. Past research [3, 4] corroborates that pre-trained models can overfit on small-scale datasets, compromising their generalizability. This disparity with other studies stems from our dataset size, and we expect our results to align with others when applying a larger, more diverse training dataset.\n\n**Comment 2: Novelty compared to a previous approach**\n\nWe sincerely appreciate that you highlighted a closely related work. We would like to clarify FedTherapist’s novelty compared with a previous approach (Liu et al., 2020 [5]) as follows:\n\n* **Multi-Context Integration:** FedTherapist involves Context-Aware Language Learning (CALL), which integrates four types of smartphone contexts — time, location, motion, and app — to enhance the model’s ability to sense mental health signals. In contrast, Liu et al.’s approach solely incorporates one type of context, the app usage, with text data.\n* **Modeling Technique:** FedTherapist employs ensemble learning across multiple context models to aid accurate mental health sensing with CALL. It differs from Liu et al.’s approach, which directly concatenates the text and the app usage features as an input of a single model.\n* **Model Design Exploration:** FedTherapist explores candidate model designs for language-based mental health monitoring on smartphones. This includes comparing BERT and LLM models' performance and smartphone overhead (CPU and memory usage). Conversely, Liu et al. solely utilize a bag-of-words methodology. For a more comprehensive comparison, we plan to add the bag-of-words-based model design in our comparison in our revised manuscript.\n* **Data Types:** We've designed and evaluated FedTherapist on two types of user text data from smartphones: speech and keyboard input, while Liu et al. only leveraged the keyboard input.\n\nIn our revised manuscript, we will clarify our novelty compared to Liu et al.’s approach.\n\n**References:**\n\n[1] Hu, Edward J., et al. \"LoRA: Low-rank adaptation of large language models.\" ICLR 2022.\n\n[2] HuggingFace, “LoRA Sequence Classification Example”, https://github.com/huggingface/peft/blob/main/examples/sequence_classification/LoRA.ipynb\n\n[3] Dai, Andrew M., and Quoc V. Le. \"Semi-supervised sequence learning.\" NeurIPS 2015.\n\n[4] Sun, Chi, et al. \"How to fine-tune bert for text classification?.\" CCL 2019.\n\n[5] Liu, Terrance, et al. “Multimodal privacy-preserving mood prediction from mobile data: A preliminary study.” arXiv preprint arXiv:2012.02359.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- This work is of interest not just to NLP community but also to broader mental health community, with an attempt to provide a solution to an important problem \n- Paper is well written and easy to follow\n- Authors made a huge effort from data collection to a technical solution for the problem which can use both speech / text data from keyboard input, which pushes the area of mental health and therapy forward \n", "reasons_to_reject": "- Not clear if hyper-parameter tuning was done and how it was done, as they reported results for end-to-end BERT are lower than a frozen BERT model which is contradiction to similar papers (Matero, 2019)\n- Method is not particularly novel given similar approaches have been proposed on multi-modal data (Liu, 2020) for mental health before, though use of speech + text is novel\n\nMatero, M., Idnani, A., Son, Y., Giorgi, S., Vu, H., Zamani, M., Limbachiya, P., Guntuku, S.C. and Schwartz, H.A., 2019, June. Suicide risk assessment with multi-level dual-context language and BERT. In Proceedings of the sixth workshop on computational linguistics and clinical psychology (pp. 39-44).\n\nLiu, T., Liang, P.P., Muszynski, M., Ishii, R., Brent, D., Auerbach, R., Allen, N. and Morency, L.P., 2020. Multimodal privacy-preserving mood prediction from mobile data: A preliminary study. arXiv preprint arXiv:2012.02359.", "questions_to_authors": "- What procedure was used for hyperparameter tuning for end-to-end BERT since the claim is that the model is over-fitting. Was a validation set used for it or some early stopping used instead of using 200 epochs as mentioned in appendix?", "paper_topic_and_main_contributions": "FedTherapist is introduced in this work which is a federated learning method that can be used on mobile devices for tracking mental health of the device user with their spoken and written communication.  They further explore a context aware method by adding contexual features like time of the day, to capture user's emotional state which shows improvement in results. The federated learning method allows user privacy by not having to transfer user data out of device, with on-device models updates with user data.", "excitement": 3, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "Matero, M., Idnani, A., Son, Y., Giorgi, S., Vu, H., Zamani, M., Limbachiya, P., Guntuku, S.C. and Schwartz, H.A., 2019, June. Suicide risk assessment with multi-level dual-context language and BERT. In Proceedings of the sixth workshop on computational linguistics and clinical psychology (pp. 39-44).\n\nLiu, T., Liang, P.P., Muszynski, M., Ishii, R., Brent, D., Auerbach, R., Allen, N. and Morency, L.P., 2020. Multimodal privacy-preserving mood prediction from mobile data: A preliminary study. arXiv preprint arXiv:2012.02359."}}, {"review_id": "ezKeWLm9nT", "reviewer": "Reviewer_YJZA", "summary": "", "questions": "Question A: Could you provide more details on the privacy-preserving measures implemented in FedTherapist beyond the use of Federated Learning? How are potential privacy risks mitigated?\n\nQuestion B: The evaluation of the system was conducted on a relatively small sample size of 46 participants. How do you plan to validate the system's effectiveness on a larger scale?\n\nQuestion C: Could you elaborate on the comparative analysis with other existing methods or systems for mental health monitoring? How does FedTherapist perform in comparison to these methods?\n\nQuestion D: Are there plans to release the software or pre-trained models to the community to enhance reproducibility and further research in this area?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are deeply grateful for your thoughtful comments, especially for recognizing our work as an innovative approach and a significant step toward real-world mental health monitoring. We plan to address your valuable suggestions in our revised manuscript as follows:\n\n**Comment 1 (Question A): Privacy analysis beyond federated learning**\n\nThank you for emphasizing the importance of privacy preservation in FedTherapist. Given that recent studies [1, 2, 3] have highlighted potential data leakage risks associated with sharing model updates in FL, we concur that a rigorous privacy analysis of FedTherapist is paramount.\n\nIn Appendix F.2 of our submitted paper, we discussed the privacy measures adopted in FedTherapist in detail. To briefly summarize:\n\n* FedTherapist’s Fixed BERT + MLP design only trains MLP via FL, which takes BERT embeddings as an input. Such a design hinders potential attackers' direct recovery of raw user text. \n* However, we acknowledged the risk of attackers possibly recovering original text from embedded representations [4, 5]. To tackle this, we discussed integrating protection methodologies to such an attack [6] with FedTherapist as a future work.\n* Lastly, the risk exists where an attacker might deduce private user information based on non-zero gradients of specific context models. Our proposed solution is to adopt a widely used Secure Aggregation protocol [7] to prevent an attacker from acquiring individual model updates during FL.\n\nIn our revised manuscript, we will include a summarized privacy analysis of FedTherapist within the main body of the paper.\n\n\n**Comment 2 (Question B): Small-scale experiment**\n\nThank you for your comment regarding the scale of our study. Due to the sensitive nature of our collected data, including chat messages and medical status, our compensatory costs per participant ($72 for ten days) were higher than those of typical non-invasive studies. This, combined with budget limitations, led to our sample size of 46 participants. However, it's worth highlighting that our study draws from a diverse age range from the 20s to 60s, in contrast to prior smartphone-based studies that primarily targeted university students [8, 9].\n\nA large-scale study remains our paramount objective and is our most desired future work. Moving forward, we plan to deploy federated learning on user smartphones, eliminating the need for direct data collection, which would be cost-effective and allow us to validate our findings at a larger scale.\n\n**Comment 3 (Question C): Comparative analysis with other existing methods**\n\nThank you for your feedback. We conducted a comprehensive comparative analysis of FedTherapist against closely related approaches:\n\n* **CNN + MLP:** Modeled after Yates et al. [10] and Kim et al. [11], this CNN-based text classification employs a convolutional layer and an MLP (Multi-Layer Perceptron). We used Yates et al. 's hyperparameters for depression identification.\n* **Bag-of-words + MLP:** This model employs an MLP with a bag-of-words input with TF-IDF, based on Pirina et al.'s [12] depression detection. We experimented with a n-gram range of [1, 3] and representation sizes from [1000, 5000, 10000, 20000].\n* **Empath + MLP:** Taking cues from Tlachac et al. [13], which utilizes smartphone SMS messages, the model utilizes output representations from Empath [14], which analyzes text over 200 lexical categories.\n\nTo ensure a fair comparison, we trained all baseline models using learning rates from [0.0001, 0.001, 0.01, 0.1] and evaluated their performance at each of the 1000 epochs. We presented the best performance achieved by these models over the 1000 epochs in the table below. For FedTherapist, we reported the test AUROC after its 1000 epochs without selecting its peak performance. Despite this, FedTherapist still demonstrated superior results as follows:\n\n\n| Methods            | FedTherapist | CNN+MLP | Bag-of-words+MLP | Empath+MLP |\n|--------------------|--------------|---------|------------------|------------|\n| Depression (AUROC) | 0.721        | 0.418   | 0.564            | 0.635      |\n\nThe result shows that FedTherapist outperforms other methods in depression detection on our dataset. Limited training samples from only 46 users in our dataset might have hindered the effectiveness of CNNs trained from scratch — in contrast, Yates et al. trained with Reddit posts from more than 100,000 users. Other results suggest that using frozen BERT for text representations in FedTherapist shows better mental health monitoring performance than using bag-of-words or Empath.\n\nIn our revised manuscript, we will clarify the points above and include results from the regression mental health monitoring tasks. We anticipate these to exhibit a trend similar to that in depression.\n\n**Comment 4 (Question D): Plans to release the software or pre-trained models**\n\nThank you for your suggestion. Following the publication of our paper, we intend to release both (1) the Android application utilized during our data collection phase and (2) the source code for data preprocessing and model training (including federated learning). This will empower the research community to replicate and build upon our work. However, in line with our approved IRB (Institutional Research Board) proposal, we cannot share the collected or processed user data and the pre-trained models to protect participants' privacy.\n\n**References:**\n\n[1] Zhu, Ligeng, Zhijian Liu, and Song Han. \"Deep leakage from gradients.\" NeurIPS 2019.\n\n[2] Geiping, Jonas, et al. \"Inverting gradients-how easy is it to break privacy in federated learning?.\" NeurIPS 2020.\n\n[3] Huang, Yangsibo, et al. \"Evaluating gradient inversion attacks and defenses in federated learning.\" NeurIPS 2021.\n\n[4] Coavoux, Maximin, et al. “Privacy-preserving Neural Representations of Text” EMNLP 2018.\n\n[5] Song, Congzheng, and Ananth Raghunathan. \"Information leakage in embedding models.\" ACM CCS 2020.\n\n[6] Lee, Garam, et al. “Privacy-Preserving Text Classification on BERT Embeddings with Homomorphic Encryption.” NAACL 2022.\n\n[7] Bonawitz, Keith, et al. \"Practical secure aggregation for privacy-preserving machine learning.\" ACM CCS 2017.\n\n[8] Wang, Rui, et al. \"StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones.\" ACM UbiComp 2014.\n\n[9] Li, Boning, and Akane Sano. \"Extraction and interpretation of deep autoencoder-based temporal features from wearables for forecasting personalized mood, health, and stress.\" ACM IMWUT 2020.\n\n[10] Yates, Andrew, Arman Cohan, and Nazli Goharian. \"Depression and self-harm risk assessment in online forums.\" EMNLP 2017.\n\n[11] Kim, Yoon. \"Convolutional neural networks for sentence classification.\" EMNLP 2014.\n\n[12] Pirina, Inna, and Çağrı Çöltekin. \"Identifying depression on reddit: The effect of training data.\" EMNLP Workshop SMM4H 2018.\n\n[13] Tlachac, M. L., Katherine Dixon-Gordon, and Elke Rundensteiner. \"Screening for suicidal ideation with text messages.\" IEEE BHI 2021.\n\n[14] Fast, Ethan, Binbin Chen, and Michael S. Bernstein. \"Empath: Understanding topic signals in large-scale text.\" ACM CHI 2016.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1 The paper presents an innovative approach to mental health monitoring by leveraging user-generated text data on smartphones. This represents a significant step forward in the application of NLP techniques to real-world health monitoring.\n\n2 The use of Federated Learning in the proposed system, FedTherapist, addresses significant privacy concerns associated with the use of personal data for health monitoring. This could set a precedent for future research in this area.\n\n3 The empirical evaluation of the proposed system using real-world user data adds credibility to the research and provides a benchmark for future studies in this area.\n\n", "reasons_to_reject": "1 The evaluation of the proposed system is conducted on a relatively small sample size of 46 participants. This might limit the generalizability of the results. A larger-scale study would provide more robust evidence of the system's effectiveness.\n\n2 The paper could benefit from a more extensive comparative analysis with other existing methods or systems for mental health monitoring. This would provide a clearer picture of the relative strengths and weaknesses of the proposed system. \n\n3 While the paper emphasizes the privacy-preserving nature of the proposed system, it does not provide a detailed analysis of potential privacy risks or how they are mitigated, beyond the use of Federated Learning. A more thorough discussion of privacy implications would strengthen the paper.\n\n4 The paper does not explicitly mention the release of software or pre-trained models, which could limit the reproducibility of the research and its impact on the community.", "questions_to_authors": "Question A: Could you provide more details on the privacy-preserving measures implemented in FedTherapist beyond the use of Federated Learning? How are potential privacy risks mitigated?\n\nQuestion B: The evaluation of the system was conducted on a relatively small sample size of 46 participants. How do you plan to validate the system's effectiveness on a larger scale?\n\nQuestion C: Could you elaborate on the comparative analysis with other existing methods or systems for mental health monitoring? How does FedTherapist perform in comparison to these methods?\n\nQuestion D: Are there plans to release the software or pre-trained models to the community to enhance reproducibility and further research in this area?", "paper_topic_and_main_contributions": "This paper presents \"FedTherapist,\" a privacy-preserving mobile system for mental health monitoring using user-generated text data (speech and keyboard input) on smartphones. The system employs Federated Learning to decentralize model training, ensuring privacy. The authors explore multiple model designs, concluding that the Fixed-BERT + MLP model offers the best balance between performance and smartphone overhead. They also introduce the Context-Aware Language Learning (CALL) methodology, which integrates various temporal contexts of a user to enhance the model's ability to sense mental health signals. The evaluation shows that FedTherapist with CALL achieves higher accuracy in predicting mental health statuses than models trained with non-text data.\n\n\n", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "xWxs3n7scb", "reviewer": "Reviewer_PGBC", "summary": "", "questions": "A- In Section 4.1, for the method CL+NonText, you refer to a previous work, However, could you give some details of the learning mehtod? Is it a neural network? Another method?\nB- For the non-experts in the area (such as myself), could you provide the meaning of the acronym IRB? It is mentioned twice in the paper, but it is not explained anywhere. ", "limitations": "", "rating": 3, "confidence": 1, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We truly appreciate your thoughtful comments and valuable suggestions. We plan to address them in our revised manuscript as below:\n\n**Comment 1: Important details being omitted from the main body**\n\nThank you for highlighting the critical details that were inadvertently confined to the appendices. We understand and agree that a reader should be able to comprehend the essence of our research without having to cross-reference the supplementary sections.\n\nIn direct response to your feedback:\n\n* We will integrate the experimental methodologies for Tables 1 and 2 into the main body, ensuring that readers can grasp our methodology without referencing the appendices.\n* We will ensure that the responses to the questions introduced in Section 4 are presented within the main body itself.\n\nThe extra page permitted for the camera-ready version will greatly aid in making these revisions. We sincerely appreciate your feedback and commit to ensure the manuscript's clarity and self-completeness within the main body.\n\n**Comment 2: Validating the performance of end-to-end BERT + MLP and LLM**\n\nThank you for your feedback on verifying the over-fitting of BERT and LLM experiments. We re-ran the experiments to share the learning curve of train-dev(validation)-test sets, which we will include in our revised manuscript. Specifically:\n\nWe re-ran the experiments by performing LOUO cross-validation, splitting users into one test user, five validation users, and 40 train users. We trained until 30 epochs and five epochs for RoBERTa and LLaMa, respectively as in LoRA [1], as follows:\n\n**End-to-End BERT learning curve (Loss):**\n\n| Epochs     | 0     | 5     | 10    | 15    | 20    | 25    | 30    |\n|------------|-------|-------|-------|-------|-------|-------|-------|\n| Train loss | 0.696 | 0.634 | 0.583 | 0.528 | 0.478 | 0.437 | 0.418 |\n| Val loss   | 0.696 | 0.754 | 0.791 | 0.841 | 0.931 | 0.978 | 1.004 |\n| Test loss  | 0.702 | 0.742 | 0.801 | 0.880 | 0.999 | 0.993 | 1.030 |\n\n**End-to-End BERT learning curve (AUROC):**\n\n| Epochs     | 0     | 5     | 10    | 15    | 20    | 25    | 30    |\n|------------|-------|-------|-------|-------|-------|-------|-------|\n| Train AUROC | 0.457 | 0.392 | 0.523 | 0.406 | 0.511 | 0.637 | 0.600 |\n| Val AUROC  | 0.477 | 0.248 | 0.346 | 0.482 | 0.325 | 0.407 | 0.454 |\n| Test AUROC  | 0.438 | 0.423 | 0.411 | 0.351 | 0.253 | 0.361 | 0.401 |\n\n**LLM learning curve (Loss):**\n\n| Epochs     | 0     | 1     | 2     | 3     | 4     | 5     |\n|------------|-------|-------|-------|-------|-------|-------|\n| Train loss | 1.502 | 0.714 | 0.694 | 0.682 | 0.617 | 0.544 |\n| Val loss   | 1.285 | 0.788 | 0.724 | 0.770 | 0.767 | 0.845 |\n| Test loss  | 1.061 | 0.777 | 0.747 | 0.775 | 0.742 | 0.811 |\n\n**LLM learning curve (AUROC):**\n\n| Epochs     | 0     | 1     | 2     | 3     | 4     | 5     |\n|------------|-------|-------|-------|-------|-------|-------|\n| Train AUROC | 0.525 | 0.566 | 0.451 | 0.501 | 0.429 | 0.575 |\n| Val AUROC | 0.596 | 0.337 | 0.446 | 0.462 | 0.367 | 0.316 |\n| Test AUROC | 0.686 | 0.389 | 0.383 | 0.323 | 0.400 | 0.345 |\n\nAs shown in the above results, both end-to-end BERT and LLM models show low train loss and high validation and test loss over the epochs, which shows the overfitting of those models during fine-tuning. We hypothesize the overfitting of BERT and LLM happens as our training samples are only from 40 users; previous studies [2, 3] corroborate that fine-tuning the pre-trained models can overfit and lose generalization on smaller-scale train datasets.\n\n**Comment 3 (Question A): Detailed explanation of a previous work (CL + NonText)**\n\nFor CL+NonText, we followed the approach outlined by Wang et al. [4]. This involves training a linear model on the following lasso-selected mobile features: stationary time, conversation count, heart rate, sleep end time, location duration, unlock duration, unlock counts, and number of places visited. As we target a smartphone-based system, we excluded the heart rate feature, which Wang et al. measured on a wristband. We extracted the features by preprocessing the device logs and raw sensor values such as GPS, accelerometer, or ambient light sensor from participant’s mobile devices. Our revised manuscript will provide a more detailed description of this method for clarity.\n\n**Comment 4 (Question B): Meaning of the acronym IRB**\n\nThank you for highlighting this oversight. IRB stands for “Institutional Review Board”. IRB’s approval is essential for conducting research involving human participants. The primary purpose of securing IRB approval is to guarantee the ethical treatment and protection of human subjects involved in the study [5]. In our revised manuscript, we will ensure that this acronym is clearly defined for the benefit of all readers.\n\n**References:**\n\n[1] Hu, Edward J., et al. \"LoRA: Low-rank adaptation of large language models.\" ICLR 2022.\n\n[2] Dai, Andrew M., and Quoc V. Le. \"Semi-supervised sequence learning.\" NeurIPS 2015.\n\n[3] Sun, Chi, et al. \"How to fine-tune bert for text classification?.\" CCL 2019.\n\n[4] Wang, Rui, et al. \"Tracking depression dynamics in college students using mobile phone and wearable sensing.\" ACM IMWUT 2018.\n\n[5] Morris, Marilyn C., and Jason Z. Morris. \"The importance of virtue ethics in the IRB.\" Research Ethics 12.4 (2016): 201-216.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "A- The field of mental health monitoring  is very important nowadays.\nB- The paper is well-motivated and founded on previous work in the area of mental health.\nC- The presented results are strong and support the idea of using textual data for mental health monitoring.", "reasons_to_reject": "A- Many important details are omitted from the main body of the paper. I could find some of them in the appendices, but I think they should be in the main body, since the paper should be understood without the appendices. For example, if you don't read the appendices, you don't know that the results of Table 2 come from a leave-one-user-out cross-validation. I think this is very important. For Table 1 results, I don't still know which data splits were used and how. Another example: in Section 4, authors say they answer 4 questions. However, they also say that questions 3 and 4 are answered in Appendix E. In my opinion this should not happen. Or you answer the questions in the main body or you don't introduce the questions until the appendices.\nB- In Line 162, authors mention that the lower performance of end-to-end BERT + MLP and LLM could be due to overfitting. To validate the hypothesis we would need to know train-dev-test results and learning curves. I couldn't find this information anywhere.", "questions_to_authors": "A- In Section 4.1, for the method CL+NonText, you refer to a previous work, However, could you give some details of the learning mehtod? Is it a neural network? Another method?\nB- For the non-experts in the area (such as myself), could you provide the meaning of the acronym IRB? It is mentioned twice in the paper, but it is not explained anywhere. ", "paper_topic_and_main_contributions": "This paper presents a system to assess the mental health of users through textual data generated by their smartphones. Textual data is extracted from keyboard and speech (microphone) inputs. Given the sensitivity of the data, authors propose to use Federated Learning, in order to preserve the privacy of the users. They also develop an ensemble architecture, which is designed to process textual data depending on the context in which it was generated. As context, authors identify Time, Location, Motion and Application, based on previous studies of the field. After collecting data from 46 users, authors compare different variants of their proposals and also a non-textual system based on previous work. They conclude that using textual information is beneficial for mental health monitoring.", "excitement": 3, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: In general, reviewers found that the paper sufficiently supported its major claims and arguments, with only minor points requiring extra support or details.  Their enthusiasm ranged from ambivalent to strong, with Reviewer YJZA in particular advocating for the paper and noting that the work presents an innovative path forward in the application of NLP to real-world health monitoring.

**Summary of Reviewer Feedback and Discussion:**
- **Reviewer Y3PY** liked that the work is broadly impactful and describes a substantial effort to collect data and develop a comprehensive technical solution.  However, they were uncertain about some aspects of the paper, such as whether hyperparameters were tuned, and they felt that the proposed method itself was only novel in its use of speech and text as modalities.  In their rebuttal, the authors provided clarifying information regarding their hyperparameter tuning process, as well as an additional experiment to enhance clarity.  They also more fully elaborated on FedTherapist's novelty compared to the prior approach mentioned by Reviewer Y3PY.  They promised to incorporate these revisions in the updated manuscript.  Reviewer Y3PY thanked the authors for the detailed rebuttal, and noted that while their concerns regarding hyperparameter tuning were addressed, they were still unconvinced of the approach's novelty.
- **Reviewer YJZA** felt that the work was innovative and addressed important privacy concerns associated with the use of personal data for health monitoring.  They also appreciated the empirical evaluation of the proposed system.  However, they noted that the evaluation was conducted on a relatively small sample size and that the work could benefit from more extensive comparison with other systems for mental health monitoring.  They also wished that the paper had included a detailed analysis of potential privacy risks and how the proposed approach mitigates them, and they were curious whether the software or pretrained models would be released publicly.  In their rebuttal, the authors noted that some privacy analysis is provided in the appendix but that in their revised manuscript they will summarize this in the main body of the paper.  They also acknowledged the limited sample size of their study, explaining that it was due to budget limitations and the sensitive nature of their work, but noted that they did ensure that their participant pool was distributed across a broader demographic group than seen in prior work.  They included some additional results comparing their approach to existing methods, and they noted that while they plan to release the application used for data collection and the source code for data preprocessing and model training, in keeping with IRB protocol they cannot share their data or pretrained models to protect participants' privacy.
- **Reviewer PGBC** liked that the work tackled an important and well-motivated problem, and that the presented results are strong and support the idea of using text data for mental health monitoring.  However, they felt that many important details were omitted from the main body of the paper or relegated to the appendix.  They also asked some questions pertaining to specific methodological details.  In their rebuttal, the authors noted specific elements from the appendix that they plan to move to the main body of the paper, given additional space following acceptance.  They also ran additional experiments based on questions asked by Reviewer PGBC, and presented these new results to further support the work.  They provided clarifying responses to the reviewer's other questions.
Decision: Accept-Main