F ed T herapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning (2023)

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.

## introduction nearly a billion people worldwide are living with mental disorders, which seriously affect one's cognition, emotion regulation, and behavior. early diagnosis and proper treatment are the keys to alleviating the negative impact of the mental disorder @xcite . however, most patients have been unaware of their disorder for years @xcite , which delays treatment while the symptoms worsen. given their ubiquity in users' lives, researchers have leveraged smartphones to resolve this unawareness problem, using features such as phone usage patterns, location, and activity for seamless mental health monitoring @xcite @xcite @xcite . however, these features fail to reflect how licensed psychiatrists diagnose men-tal disorders by conversing with patients @xcite . while analyzing linguistic use is ideal for monitoring smartphone users' mental health, substantial privacy concerns it raises present challenges in amassing sufficient data to train advanced nlp neural networks @xcite . we propose fedtherapist, a privacy-preserving mental health monitoring system that leverages user-generated text (speech and keyboard) on smartphones via federated learning (fl). fl decentralizes model training on client devices (e.g., smartphones) using locally stored data @xcite , ensuring privacy on fedtherapist by only collecting securely aggregated model updates. ## data collection we conducted an irb (institutional review board)approved data collection study to evaluate fedtherapist 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. we collected two types of text input: (1) speech: user-spoken words on the microphone-equipped smartphones, and (2) keyboard: typed characters on smartphone keyboards. on average, we collected 13,492±14,222 speech and 4,521±5,994 keyboard words per participant. we describe how we implemented a system to collect these inputs on smartphones in appendix c. to compare fedtherapist with the non-text data, we collected the data used in a mobile depression detection study @xcite detailed in section 4.1.1, 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, we carefully controlled data collection, storage, and access as in the ethics statement. given the highly sensitive nature of the data, it will not be released publicly and will be deleted post-study following irb guidelines. ## evaluation we answer the following key questions: (1) how beneficial is using the text data on mental health monitoring tasks? (2) how much performance improvement does fedtherapist achieve with call? ## conclusion we presented fedtherapist, a 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 fedtherapist to effectively sense the mental health signal from a large and noisy text data on smartphones. our evaluation with 46 participants on depression, stress, anxiety, 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-generated linguistic data without privacy concerns.