Dual-Personalizing Adapter for Federated Foundation Models
Authors: yiyuan yang
Guodong Long
Tao Shen
Jing Jiang
Michael Blumenstein
Venue: NeurIPS 2024 poster (2024)
Keywords: Federated Learning
Foundation Models
Personalization
Test-Time Distribution Shifts
Primary area: machine_learning_for_other_sciences_and_fields

Abstract: Recently, foundation models, particularly large language models (LLMs), have demonstrated an impressive ability to adapt to various tasks by fine-tuning diverse instruction data. Notably, federated foundation models (FedFM) emerge as a privacy preservation method to fine-tune models collaboratively under federated learning (FL) settings by leveraging many distributed datasets with non-IID data. To alleviate communication and computation overhead, parameter-efficient methods are introduced for efficiency, and some research adapted personalization methods to FedFM for better user preferences alignment. However, a critical gap in existing research is the neglect of test-time distribution shifts in real-world applications, and conventional methods for test-time distribution shifts in personalized FL are less effective for FedFM due to their failure to adapt to complex distribution shift scenarios and the requirement to train all parameters. To bridge this gap, we refine the setting in FedFM, termed test-time personalization, which aims to learn personalized federated foundation models on clients while effectively handling test-time distribution shifts simultaneously. To address challenges in this setting, we explore a simple yet effective solution, a Federated Dual-Personalizing Adapter (FedDPA) architecture. By co-working with a foundation model, a global adapter and a local adapter jointly tackle the test-time distribution shifts and client-specific personalization. Additionally, we introduce an instance-wise dynamic weighting mechanism that dynamically integrates the global and local adapters for each test instance during inference, facilitating effective test-time personalization. The effectiveness of the proposed method has been evaluated on benchmark datasets across different NLP tasks.

Dual-Personalizing Adapter for Federated Foundation Models
Yiyuan Yang
Guodong Long
Australian AI Institute, Faculty Engineering IT University of Technology Sydney Yiyuan. Yang-1 1esudentt uts edu. au
Australian AI Institute, Faculty of Engineering IT University of Technology Sydney Guodong. LongRuts edu. au
Tao Shen
Jing Jiang
Australian AI Institute, Faculty of Engineering IT University of Technology Sydney Tao Sheng@uts edu, au
Australian Al Institute,
Faculty of Engineering & University of Technology Sydney Jing JiangSuts edu, au
Michael Blumenstein Australian AI Institute, Faculty of Engineering & IT University of Technology Sydney Riichae..Blmensteeeee.. Blumenstein@uts. edu. au
Abstract
Recently, foundation models, particularly large language models (LLMs), have demonstrated impressive ability adapt various tasks by fine-tuning diverse instruction data. Notably, federated foundation models (FedFM) emerge as a privacy preservation method fine-tune models collaboratively under federated learning (FL) settings by leveraging many distributed datasets with non-IID data. To alleviate communication and computation overhead, marameter-efficiee methods are introduced for efficiency, and some research adapted personalization methods FedFM for better user preferences alignment. However, a critical gap existing research is the neglect of test-time distribution shifts in real-world applications, and conventional methods for test-time distribution shifts in personalized FL are less efffective for FedFM due to their failure to adapt complex distribution shift scenarios and the requirement to train all parameters. To bridge this gap, we refine the setting in FedFM, termed test-time personalization, which aims learn personalized federated foundation models on clients while effectively handling test-time distribution shifts simultancously. To address challenges in this setting, we explore a simple yet effective solution, Federated mual-Personalizing Adapter (FedDPA) architecture. By co-working with foundation model, a global adapter and a local adapter jointly tackle the test-time distribution shifts and client-specific personalization. Additionally, introduce instance-wisg -iiee dynamic weighting mechanism that dynamically integrates the global and local adapters for each test instance during inference, facilitating effective test-time personalization. The effectiveness of the proposed method has been evaluated on benchmark datasets across different NLP tasks with released code
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Introduction
Foundation models, especially the large language model (LLM) in natural language processing (NLP), have nearly exhausted public data sources for training. This necessitates alternative solutions to further improve these foundation models by leveraging private or protected data sources, such as business data in companies, smartphones, and so on. Federated foundation models (FedFM) 41 offer a promising solution by integrating federated learning (FL) frameworks to enhance the foundation models in decentralized manner. Built upon existing Parameter-effiiint fine-tuning (PEFT) methods 33 14 13], FedFM a collaboratively fine-tuning framework that leverages private datasets with privacy preservation and avoiding overfitting from client-specific fine-tuning.
Test-time distribution shift in FL [66 29 presents a significant challenge in practical scenarios, as clients may encounter unseen learning tasks during the testing and model inference phases. For example, a client accustomed to writing emails in English may require translation assistance when working on new project in Chinese. Therefore, it is imperative for the deployed machine learning model be capable of tackling the test-time distribution shifts from the training data, and our paper addresses this critical issue of test-time distribution shifts within the FedFM scenario. Previous works in test-time FL (29 [6] predominantly utilize conventional deep learning models that are trained from scratch in federated settings. Recent FedFM methods mainly focus on addressing specific challenges related to data heterogeneity ( and communication overheads [35] 27). However, none of these methods have discussed test-time distribution shifts in FedFM scenarios.
To fill this gap, we propose novel FedFM framework that is robust client-specific alignment and test-time distribution shifts simuitaneously. With the support of a foundation model with PEFT methods, we first refine the federated setting. termed test-time personalization, which follows: 1) each client needs to train a personalized model using its own data from target task, and 2) during testing, each client's personalized model needs be robust to tackle the receiving new tasks (unseen in training) with different distributions (test-time distribution shift). Essentially, the proposed test-time personalization in FL. could be simply viewed as an optimization task to seek sweet trade-off between client-specific model personalization and model generalization to test data.
For test-time personalization in FedFM, two primary challenges-test-iinng distribution shifts and personalization-nccessiii learning tailored to distinct objectives, and the training cost of foundation models also represents a significant concern. To address these issues, we explore simple yet effective method, dubbed Federated Dual-Personalizing Adapter (FedDPA), where each client learns a global adapter to learn generic knowledge from the aggregation for test-time tasks and maintains a local adapter for targeted ability personalization. During the inference phase, the local and global adapters are dynamically integrated facilitate prediction, where an instance-wing wise dynamic weighting mechanism is proposed to autonomously adjudicate the proportional contribution of the local and global adapters for each test instance. Experimental results demonstrate that our method achieves state-of-the performance on benchmarks and all data and code are released Our main contributions are summarized as follows:
We are the first explore the test-time distribution shifts problem in federated foundation models for practical application scenarios alignment.
We introduce a new method, namely dual-personaliziig adapter, to emphasize learning both generic and personalized knowledge in the context of FedFM with test-time personalization.
We conduct an exhaustive analysis using heterogeneous FL benchmarks across diverse NLP tasks. The empirical outcomes reveal that our method attains state-of tfte-of-theeart performance, underscoring its superior test-time personalization capabilities than existing methods.
2
Related Work
2.1
Adapter-based PEFT Methods
Given the substantial computational and storage burdens associated with directly fine-tuning founda- tion models, the community has shifted towards embracing parameter-efficien methods (33), with the adapter family [1] being a notable exemplar. According to different architectures, methods in

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the adapter family can be categorized into four types. The first one is pompt-based learning [8, 21 which is aimed learning the continuous/soft prompt for discrete optimization. The second one is reparametrization-baas techniques to decompose the high-dimensional matrices. The third one is series Adapters [12), which introduce additional learnable modules in sequential manner within specific sublayers. The last one is parallel Adapters [[]] which focus on learning additional learnable modules in a parallel way with distinct sublayers. In this context, our exploration delves into the adapter-based PEFT methods of federated foundation models.
methods [13 achieving parameter efficiency by utilizing low-rank
2.2 Federated Foundation Models
With the advent of foundation models, there has been a burgeoning interest 44
41 26 in integrating these models within the FL setting. Particularly, in light of the inherent computation and
communication cost, recent work [17 43 6 endeavors have delved deeper into integrating adapter- based parameter-efficien tuning (PEFT) methods with federated foundation models. Building upon this, multitude of studies have emerged to navigate the challenges of incorporating federated foundation models with adapter- based PEFT methods. The paper [42 stands at the forefront, initiating the integration of instruction tuning within federated LLM frameworks. Addressing data-related issues, the paper [2] introduced a data-driven initialization approach to mitigate the primary challenges associated with LoRA in highly heterogeneous data scenarios. addition, the research presented in 15 proposed method to annotate unlabeled client-side data by harnessing the prowess of large models to address data scareity concerns. To further optimize the communication and computational overheads associated with federated foundation models, the works 35 27 34 emphasize advancing gradient-free optimization methods suitable for devices with limited memory and computing power. For personalization, paper (38) focused on designing specific training paradigm for LoRA to achieve more effective personalization
visual hodel--heeroogeneuus scenarios. Diverging from these approaches, work delves into the realm of personalization with adapters in federated foundation models, extending the scope of research in this area.
2.3 Personalized Federated Learning
To address the necessity of personalization for individual clients. personalized Federated Learning (PFL) (28). which aims at training to cater to individual client preferences and needs, is proposed. Broadly, existing PFL methods can be categorized into two primary types: fine-tuning the global model for personalization or learning additional personalized models. Research works [10 7 in the first category fine-tuned the whole or part of the global model with each client's local dataset for personalization. While research works 19 22 in the second category is to learn the additional personalized layers or model through local aggregation. Nonetheless, a prevalent limitation among these PFL approaches is their concentrated focus on specifically targeted task, often at the expense of performance when encountering test-time distribution shifts.
To fill this gap, recent research has shifted focus towards exploring different test-time distribution shifts in PFL.. In contrast studies (39 g in federated continual/incrementa learning possessing ample annotated data from different distributions for training to address shifts, test-time FL focuses on handling distribution shifts during testing without the availability of annotated data for further training. One strand of research 33 32) concentrates on addressing test-time distribution shifts that occur when new clients are introduced during the testing phase by module/prior adaptation. Another line of studies[29 16] aims to tackle distribution shifts in existing clients during testing by aligning test features with existing features. Our paper falls into the second type and differs from previous work by exploring this challenge within the framework of foundation models, which are characterized by extensive parameter scales and more complex test-time distribution shifts.
3 Problem Definition
3.1 Test-time Personalization in FedFM
Considering M clients in an FL system, each client possesses its distinct local training dataset D brain and test dataset De where m indexes a client. One data pair in datasets is denoted as (æz, y), where is the input data and y is its corresponding label.
3

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A,
Server lo Client
AB
Communicatio
As. A.B
Frozen LLM
Trainable LoRA
Frozen LLM
Trainable LoRA
Frozen LLM
Trainable LoRA
1.8
Aro
Aq
Taak
Task:
Quastion Class Ficadion
Entail
whicn the Faalaadee Wee??
User: "Winal Costronn Walkddee for?
User: man dose the carar room lot in pictures, does this mean that maan altrr?
Response: 1962
Wher knd thing souud snower thes queedio??
Reaponser devcztpttonn
Resporse: you
Client
Client 2
Client
Figure 1: The overall framework of FedDPA. Each client contains a frozen LLM, a trainable global adapter (LoRA) and trainable local adapter (LoRA) with specific task, where the global adapter (LoRA) is for test-time tasks and the local adapter () is for personalization. During the training, only the parameters of the global adapter (LoRA) are transmitted the server for aggregation.
In each client m, will introduce the training and testing phases separately for test-time personalization setting. In the training phase, model training utilizes solely the local dataset Dirii which is derived from the distribution pm.. While in the testing phase, the test dataset D comprises two components: the test set D driven from the same distribution pm as training data, and additional test DD under data distribution shifts P.r(ar, (æx, y) + (rr y). Therefore, the test dataset Te = ID U D and we call these datasets ID as test-time datasets. Unlike previous works 29 [6] test-time FL. concentrating on either feature-level shifts pm (a)) o (ar( label shifts (z)
(y)+ * Pr (y), we investigate more complex scenario where various distribution shifts, including semantic shifts, domain shifts and others, exist simultancously. This is aligned with the practical application of foundation models, which often encounter testing data originating from diverse domains, backgrounds, or populations.
Therefore, the objective the model in each client should not only perform well on the test set DTT (refer to personalization) but also have comparable results the fest-time dataset D, (refer to test-time performance). This objective is consistent with the practical scenarios, since users primarily focus the abilities they often utilize (abundant data available for training) and occasionally also introduce new tasks (limited to test data).
3.2 Challenges
The test-time personalization setting raises two pivotal considerations: personalization and test-time distribution shifts. Personalization the primary focus, followed by optimizing test-time tasks. Our proposed method introduced in section El is designed to achieve personalization within FedFM while ensuring comparable results for test-time tasks, and its vital intuition illustrated below.
Considering foundation model, it comprises a main body f(0), which holds most of the parameters and processes input to produce output features h f(x; 8). Additionally, there is a tail g(,)) that maps these features to the output space (c.g., vocabulary). resulting in the predicted result = hh; 0,). Typically, the focus tuning and adaptations primarily lies on the main body f()) because the tail (0.), usually linear function, remains unchanged (frozen) during tuning [14]
The key to addressing test-time distribution shifts lies learning generic features universally applicable across disparate distributions D. That is, learning foundation model f(0) to satisfy (sff(x 0), y) = P.(f(w; ce 0), y) although
Discordance between Personalization and Test-time Tasks.

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(. + P, y). FL methodology designed to learn generic features across diverse non-III data (different distributions) through aggregation algorithms [5] 29 Therefore, we tailor FL training for addressing test-time tasks with the objective ming Lr.. (0), where Lr... represents the loss function designed for learning generic features towards all clients' distributions Patt- However, personalization focuses on aligning the model with the specific distribution P.. which means learning a foundation model f(0) with the objective ming Pp, (0), where Lp, represents the loss function de- signed for learning personalized features towards the specific distribution. Therefore, the discordance between specific distribution alignment for personalization and generic feature learning for test-time tasks leads to inconsistent optimization objectives.
The above analysis motivates a dual model strategy- one model for test-time tasks and one model for personalization to realize test-time personalization in FedFM. This strategy, together with our other techniques presented below for FedFM scenarios, constitutes the foundation of our method.
4 Proposed Method
To align with the application scenarios, we consider the rest-time personalization setting in FedFM. Following a similar assumption from Mixture of Experts [23]. any test-time task (distributions) to client can be approximated as mixture of training tasks seen by other clients in the federated learning system. Therefore, each client primarily personalizes its model based on its local training task, while also tackling unseen test-time tasks by leveraging insights gained from other clients in the federated learning system. Discussions of other scenarios can be found in Appendix C
In test-time personalization, test-time distribution shifts and personalization are two main issues that need be addressed, and their optimization objectives toward different distributions are inconsistent. To address these challenges and consider the efficient learning of FedFM, we propose a Federated Adapter (FedDPA) system for each client, as shown in Fig IT During training, global adapter is employed to acquire generic features by FL training for test-time tasks (Sec. 4.1). Meanwhile, to address personalization, local adapter maintained locally to align with the client's specific distribution, and leverages generic knowledge from the global adapter for faster learning (Sec. (.2)). During the inference, the learned global and local adapters are dynamically combined using weight generated by the instance-wise dynamic weighting mechanism for each input test instance, realizing test-time personalization (Sec. 4.3).
Dual-PPrsonalley
The Overall Objective. Considering the computation and communication cost of FedFM, we utilize the adapter- PEFT methods, which only learn a small part of parameters AO while keeping most of the parameters 0 frozen. Our proposed FedDPA is to learn the global adapter A0g and local adapters AMT simultaneously across M client to realize test-time personalization,
min (0.T(AmT))
Σ ((( (0; (0: A
(1)
s.t.
AO E arg min ((m.)))Pou (0; A0,: A0(*
20,
where the first part is a standard personalized FI. loss to find optimal personalized models by minimiz- ing the sum of loss local training tasks pm (-), and the second part constraint term to seek an optimal solution by minimizing the test-time loss ((a.g))Pou()> Because we assume that the test-time task is unseen
client but observed by other clients, the test-time loss can be estimated using the Empirical Risk Minimization of client's training tasks minso, EM T'm (0; 202). where Patt denotes all distributions of tasks in all clients, Y'm denotes each client's weight for ag- gregation (e.g., in FedAvg, T' the proportion of each client's data number to all clients' data number) and L. denotes the loss for each client over its local training dataset. Since the above objective cannot be solved directly, we propose to alternatively learn the global and local adapters in sequential manner (FedDPA-F with local adapter fine-tuning) iterative manner (FedDPA-T with local adapter training). Detailed algorithms of these two methods are in Appendix A.3 A.3
Remark. To simplify the illustration, we use LLM as the backbone and adopt LoRA 13 as the adapter-based PEFT method our framework. The overall framework is easy to adapt to other types of backbone and other adapter-bases based PEFT methods. LoRA decomposes the training weight into
5

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frozen weight 0, and trainable weight derived by the multiplication of two low-rank weights A A A0". The data heterogeneity in FL with LLM primarily manifests as distribution shifts across various NLP tasks among different clients, driven by diverse backgrounds, topics, and other contextual factors, and local loss fm for all NI.P tasks a standard language modeling objective
4.1 Generic Learning of Global Model
Addressing test-time distribution shifts requires the acquisition of generic knowledge that is applicable across various distributions [T. The conventional federated learning process inherently designed to aggregate this generic knowledge among different non-IID data. Consequently, we utilize the adapter trained within the FL context as the global adapter for addressing test-time tasks. To further enhance generic learning, our model aggregation strategy is based on the client number rather than the number of data by considering the potential biases stemming from different numbers of tasks.
At each client, there consists a frozen LLM model fx; θ) with a global lightweight global adapter (LoRA) 20, 9 = A A0" This global adapter used for aggregation by sending to the server. Notably, the server's s role is limited to computing the aggregated adapter A0g. thus obviating the need for maintaining a large-scale model. Similar to the standard FL process, for each client m, the adapter weight 20 is learned locally and sent to the server. Upon receipt of the adapter weights from all clients, the server employs FedAvg [24 aggregate them and sends A0g back to each client as their initialized parameter in a new round. It can be formulated as:
1 M AO
Server: 20,-
Client: A0" arg min Lm (0; A0,), initialized with 20, (2) 20,
Remark. Other federated algorithms like FedProx (00 can also be applied with LoRA tuning of this global model learning (in Appendix[B.]] In this paper, we just take FedAvg an example.
4.2 Personalization of Local Model
The previously developed global model, which focuses on acquiring generic features across diverse datasets, faces challenges with personalization due to inconsistent optimization objectives. To address this, we integrate a local adapter to better align with each client's 's specific distribution. We explore two methods shown in Fig 2 1) Learning sequentially: after global adapter training, the local adapter initialized by the learned global adapter and directly fine-tuned; 2) Learning iteratively: during each communication round of global adapter training, the local adapter is re e--intializzed from its last state, fine-tuned alongside the frozen global adapter, and maintained locally without communication.
Local LoRA Learning
Overall Learning
[N] FecDDAUFF global LoRA and local fine-tunin LoRA All communication
Local LORA
(b) FedDPA-T. T. global LoRA and local training LoRA
communication
communication
communication 2
Gicball (RAI waminn Proswer LaRA Leamning Procees
(a) Local LoRA Fine
Local LoRA Training
Figure 2: Frameworks of two personalized methods for local adapter (LoRA) are shown on the left, with their overall learning processes on the right.
To be more specific, a local adapter (LoRA) s0, Ao is introduced. Thus, each client contains three components: frozen LLM 0, global adapter (LoRÁ) 20, and local adapter (LoRA) ~M As delineated in Fig (a), for the first method, after global training, the local adapter is first initialized by the global adapter denoted as ~M.h 20,, then fine-tuned on local data to get the final local adapter. As shown in Fig 2 (b), for the second method, during each communication round in training for each adapter layer, upon receiving an input h. it simultaneously traverses the frozen L.M,, the frozen global adapter and the local adapter. The process entails an initial fusion of the outputs from

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both the local and global adapters with predefined weighting factor of a, followed by integration
with the output of the LLM to yield the final result h' 0h + ((1 a) A0gh + a A0(") h). Therefore, the learning of the local adapter A for these two methods can be unified as:
A arg min L. (0; A0g: A0("), initialized with 40g or previous A0T" AM
(3)
4.3 LLM-enhanced Instance-wise Dynamic Weighting Mechanism
As discussed in previous test-time FL methods [66, a dynamic combination of global components and personalized components can improve generalization while reducing the cost of typer-parametee tuning in the deployment stage. Considering the disparate data distributions that characterize test-time tasks and local tasks and the wealth of training instances of local tasks available to each client, we propose an instance-wise dynamic weighting mechanism to calculate the similarity between the input instance and local instances, using this metric determine the appropriate weight balance for the global and local adapter combination. To facilitate this, the representation of each input instance essential. Leveraging the robust capability of pre-trained LLMs abstract input sentences, we utilize the hidden states from the final layer of the LLM as the representation. Given that the LLM is recoder-based with tokens attending only to preceding tokens, the embedding of the final token is considered representative of the entire input for similarity evaluation. Furthermore, to enhance the representation quality, the global adapter, which embodies generic knowledge, is incorporated into this embedding process.
More specifically, during the inference stage, for each input instance x in client, we randomly sample S instances {æo, T1,,.. L.} from the local training dataset. These instances are then fed into the LLM, augmented with the global adapter, to obtain the last token's 's embeddings from the final layer, denoted as w, and {w Wæy...., W,,,,, respectively. Subsequently, we calculate the similarity between the input representation w. and each sampled local representation in (Wags Way .... Ws, resulting in a score range of [0, 1]. Finally, we average all scores to obtain the final result, represented Ei= & Sim( w,,, W..). ). where Sim represents the function to calculate the similarity, and À is a scale factor in (0, 1] to restrict the maximum similarity score (especially for FedDPA-T).
as C22 X
&
Through this method, the balancing of weights between the global and local adapters is dynamically adjusted for each test instance, ensuring the model not only tailors to the individual client's 's specific needs but also benefits from the aggregated model's generic knowledge across test-time tasks.
5
Experiment
5.1 Experiment Setting
We construct two federated datasets from Flan (31) which is collection of various NLP tasks from over 60 datasets for instruction turning. In order to be better suitable for FL settings, we randomly select 8 NLP tasks from different datasets for each federated dataset and downsample the original datasets with more details in Appendix A ROGUE-1 is taken as a metric.
Datasets.
Baselines and Implementation. We compare our methods with four baselines based on the same model architecture; centralized model, Local-finetuned model, FedIT [42) and FedLoRA (38). The centralized model is trained on all data of tasks in one center. The local-finetuned model infers that only local data are used to train the model without any communication with other clients or the server.
We distribute data between clients based on the NLP task for data heterogeneity, where different NLP tasks generated from different contextual factors inherently suffer from various complex distribution shifts. Since we select 8 NLP tasks, corresponding to M = 8 Flients.For each client, the local task serves as the primary focus for personalization, while the tasks from other clients are taken as test-time tasks. To better evaluate the effectiveness of methods, we assume that all clients are activated for every communication round and set the communication round K = 20. The alpaca-LoRA is adapted the base model initialized with LLaMA-7B The updating weight of local LoRA training (FedDPA-T) is a = 0.5 (A= = 0.5) for federated dataset and a = 0.3 (A = 0.3) for federated dataset
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7

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2. We set S = 5 and choose cosine similarity for instance-wiss dynamic weighting mechanism. More details are Appendix A.2
Table 1: Personalization and test-time personalization results of different models on federated dataset FedDPA-F represents the model with the local fine-tunin lin-tuning adapter and FedDPA-T represents the model with the local training adapter. Linguistic represents the linguistic acceptability task, Word Dis represents the word disambiguation task, and Question CLS represents question classification task.
Federated Dataset
Methods
Para
Entail -ment
Structure to Text
Text For Linguistic Acc
Word Dis
Core ference
Question CLS
Average
phrase
-matting
Personalization
Centralized
77.00
82.00
72.58
96.59
70.50
63.50
77.59
89.00
78.60
FedIT
69.00
83.00
71.25
96.32
71.50
62.50
75.43
91.50
77.50
FedLoRA
77.50
84.00
71.49
96.69
73.50
65.00
75.27
92.00
79.43
Local-finetuned
74.50
80.00
73.71
97.36
75.00
54.50
68,55
89.50
76.64
FedDPA-F
79.00
84.50
72.06
72.00
65.00
73.86
92.50
79.48
FedDPA-T
80.50
84.50
72.79
96.51
73.50
62.00
77.93
94.00
80.22
Test-Time Personalization
Local-finetned
48.99
47.24
27.53
22.66
48.86
49.07
46.45
52.09
42.86
FedLoRA
75.56
76.55
75.21
76.16
74.64
74.99
76.97
75.63
FedDPA-F
78.10
77.36
77.18
76.98
77.11
76.23
76.84
77.19
77.12
FedDPA-T
76.20
75.51
76.19
75.63
74.86
74.60
74.77
75.96
75.47
5.2
Main Results
We compare FedDPA with other baselines on two main evaluation facets: personalization (scores on targeted local tasks) and test-fime personalization (average scores all tasks including test-time tasks). As evidenced in Table and Table 2, our proposed dual qual-personalizing adapter methods (both fine-tuning and training) exhibit superior performance personalization compared other baseline models, which demonstrates the effectiveness of local adapter maintenance for enhancing performance on the targeted local task. For test-time personalization, the FedDPA-F method stands out the most effective among all personalized models, which suggests that incorporating learning from the global adapter can be instrumental in adapting to test-time distribution shifts for more comprehensive model achievement. Additionally, given that the global adapter aggregated on different distributions matin certain generalization capabilities, the local adapter of FedDPA-F has better generalization performance than that of FedDPA-T, which leads to better performance on most test-time tasks. More importantly, is noteworthy that while centralized or global models may yield higher average
Table 2: Personalization and test-time personalization results different models on federated dataset 2. FedDPA-F represents the model with the local fine-tuning adapter and FedDPA-T represents the model with the local training adapter. Reading Com represents the reading comprehension task.
Federated Dataset
Methods
Para -phrase
Common -sense
Entail -ment
Text For Summari Reading Com
Senti -mett
Open QA
Average
-matting
-zation
Personalization
Centralized
87.00
64.67
77.00
90.65
29.12
76.00
72.50
76.17
71.64
FedIT
86.00
63.13
79.00
89,80
30.36
75.50
72.00
81.06
72.07
FedLoRA
87.00
64.12
84.50
89.52
27.13
76.50
73.50
79.62
72.74
Local-finetuned
75.00
53.51
81.00
91.28
27.51
69.00
72.50
79.31
68.64
FedDPA-F
88.00
64.80
84.25
89,82
29.58
78.50
72,00
80.89
73.48
FedDPA-T
90.50
70.54
82.00
91.81
30.75
$1.00
75.00
91.07
75.33
Test- Time Personalization
Local- finet-fie
48.21
49.07
49.75
21.86
17.35
48.57
44.04
48.19
40.88
Fedl
69.60
71.64
71.09
71.28
65.63
68.89
70.32
70.44
69.86
Fe
71.64
72.28
72.42
72.39
71.12
70.46
71.00
71.82
71.64
FedDPA-T
71.63
72.66
71.20
72.58
70.58
69.21
70.67
71.62
71.27

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performances across all tasks, they fall short in excelling at specific tasks for personalization, aligning with the conclusions of the previous study [30].
Analysis
6.1
Convergence Analysis
We present the convergence analysis of our methods in Figure 3 Figure (a) compares our methods with other baselines for personalization, with the results showcasing the average performance on target local tasks across all clients. Notably, our methods exhibit more rapid convergence compared to FedIT' and achieve notable performance enhancements after five communication rounds. Despite sharing similar trends with Fod,, our approaches, particularly the FedDPA-T, ultimately out- perform in personalization. For more granular insight into test-time personalization convergence, Figure 3 (b) compares average performance on all tasks, including each client's targeted local and test-time tasks. The results substantiate that our approaches demonstrate faster convergence rates, further bolstering the efficacy of our methods.
Table 3: Ablation study of instance-wise dy- namic weighting mechanism (Auto). repre- sents personalization, and TTP represents test- time personalization.
Table 4: Ablation study of updating weight. represents personalization, and TTP represents
test-time personalization.
Fed Dataset 1
Fed Dataset
Methods
Fed Dataset
Fed Dataset 2
P
TTP
P
TTP
Methods
Auto
P
TTP
P
TTP
0.3
79.69
75.85
75,33
71.27
79.06
76.97
73.17
71.70
FedDPA-T
0.5
80.22
75.47
74.10
70.72
FedDPA-F
79.48
77.12
73.48
71.64
0.7
79.88
75.01
74.04
69.95
FedDPA-T
79.57
60.06
73.75
63.57
80.22
75.47
75.33
71.27
[e] Personafization (Datese: 1)
(b) TTP (Dateset
(4) Personalization (Dataset 1)
(b) TTP [Dstaset
1633967
8
14 35 18 22
14 15 30 Communicstoo Rourds
5 33
Communicaion Rounds
Figure 3: Average accuracy varies as communi- cation rounds.
Figure 4: Average accuracy varies different client participation numbers.
6.2 Ablation Study
Impact of Instance-Wise Dynamic Weighting Mechanism. To explore the impact of the instance- wise dynamic weighting mechanism, we implemented experiments with FedDPA methods on different datasets. As shown in Table the incorporation of an instance-wise dynamic weighting mechanism contributes significantly to enhancing performance in both personalization and test-time personaliza- tion scenarios. More ablation studies are in Appendix B.2
Impact of Updating Weight a. In this study, we investigated the influence of the updating weight during FedDPA-T training with its value E {03, 0.5, 0.7). As can be seen in Table for test- personalization, increasing updating weight a will decrease the performance due to the increased proportion of the local adapter in the model, while for personalization. different updating weights are required for different datasets to achieve their optimal results.
Impact of Client Number. To better align with the FL setting in practical application, we scaled up clients to 40 and implemented experiments with sample rate {0.2, 0.4, 0.6, 0.8, 1)- For each communication round, the server will select clients from each task based on the sample rate (more details Appendix(A.I] As shown in Figure as the client participant rates increase, model accuracy also increases more participating clients provide more data for knowledge learning. Besides,

---

FedDPA-F - outperforms all baselines, whereas FedDPA-T exhibits somewhat inferior performance, potentially due overfitting issues when handling a small dataset.
More experiments and analyses of scalability and efficiency can be found in Appendix B.3 and B.4
7 Conclusion
Federated Foundation Model (FedFM) is promising direction enhance existing Foundation Models, c.g. LLM, by leveraging private data sources. Test-time distribution shift is a critically important problem to ensure the practicability of the FedFM system. This work is the first to propose the test-time FedFM setting. To tackle this challenging scenario, we propose a novel dual- personalizing adapter for the FedFM framework. The method evaluated on public NLP tasks that are adapted to mimic the test-time FedFM setting. This work is the first step towards this direction. We focus on defining a new learning scenario, proposing basic learning framework, and setting up the benchmark datasets. Our future works will be in two directions: the first is to rethink this problem from a theoretical perspective, and the second is to enhance the benchmark setting with more datasets in real applications.
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Appendix
A Implementation Details
A.1 Datasets
In this paper, have developed two federated datasets derived from the Flan BI, and details to construct these datasets are elucidated in this section. Flan encompasses a diverse array of NLP tasks, each comprising multiple datasets. These tasks, generated from different contextual factors, inherently experience various complex distribution shifts. To align with FL settings, we employed stratified selection process, randomly choosing one dataset from each of the eight distinct tasks from Flan to form each federated dataset. In addition, to simulate client local data scarcity [24), we implemented downsampling strategy, reducing the size of each selected local dataset to 300 training instances and 200 testing instances. Consequently, each constructed federated dataset encompasses eight distinct NLP tasks, culminating in a whole dataset comprising 2400 training examples and 1600 testing examples across all tasks. The specific tasks and datasets included in each federated dataset are cataloged in Table
The NLP tasks within these datasets can be broadly divided into two types: generation tasks and classification tasks. To facilitate uniform processing by LLM,, all tasks are converted into a generative format, employing distinct instructions for each dataset. Illustrative examples of these data for both classification and generation tasks provided in Table [] For the input of the LLM,, we adopted simple template, the details of which are delineated in Table 7
Dataset Partitioning for Ablation Study. In our ablation study in section 662 examining the client number to align with FL settings, we divided each task in our constructed federated datasets into five subsets, each comprising an equal number of training data. Based on our assumption that each client associated with a single task, this division results in total of 40 clients, with each client possessing a local dataset of 60 training examples. To mimic real-world FL communication dynamics, we employed randomized selection process for clients (subsets) within each task according to specified sample rates. Accordingly, for sample rates specified as (0.2, 0.4, 0.6, 0.8, 1}, we selected 1,2,3.4, and 5 clients (subsets) per task, leading to 8, 16, 24, 32, and 40 clients participating in federated communications, respectively. The evaluation phase involves computing the average results across these selected clients for each specified sample rate, which provides comprehensive analysis of how client numbers influence the performance of our method.
Different with Multi-Task Learning. Although both multi-task learning and our test-time person alization setting involve multiple tasks during training and testing, multi-task learning operates in a centralized setting, whereas setting is based on federated learning. a distributed setting. More im- portantly, our setting accounts for test-time distribution shifts, a challenge that is typically overlooked in conventional multi-task learning. Additionally, from a centralized foundation model serves as a strong baseline for multi-task learning. In foundation models, all tasks are standardized into uniform format, and the model benefits from task aask-agnostic token embeddings learned through extensive pre-training on diverse data. Thus, directly fine-tuning on this multi-task data represents the implementation of multi-task learning using foundation models We have included this baseline, referred to as "Centralized," in our experimental comparisons.
the combination of multi-task data
40
A.2 Baselines and Implementation
In this section, detailed descriptions of the implementation of FedDPA and each baseline compared in this study will be provided:
Centralized model: This model is formulated by aggregating all available data from various tasks a single centralized center for training purposes, with 50 epochs to optimize.
Local-finetuned model: This model trains independently without any external communi- cation from other clients or a central server. It is specifically trained on data pertaining to a single task, dedicating 50 epochs to optimize for task-specific performance without the influence of external data.
13

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Table 5: Tasks and datasets of constructed federated dataset and federated dataset 2.
Federated Dataset
Federated Dataset 2
Task
Dataset qlue_qqp
Task
Dataset
Paraphrase
Paraphrase
paws_wa wiki
Entailment
snli
Commonsense
hellaswag
Structure to text
Entailment
qnli
Text formatting
fix fix_punet
Text formatting
word_ gord_seement gigaword
Linguistic acceptability
cola
Summarization
Word disambiguation
wic
Reading comprehension Sentiment
bool q
Coreference
definite wwwtllppooooooooootee resolution
sentiment
Question classification
trec
Open-domain QA
acr ear_
Table Examples of data in our constructed federated datasets.
Data Examples
The father convinced his son that it is possible for him day become knight, but he may never achieve such status coming from a peasant family.
Who is "he"?
Input:
OPTIONS:
The father
his son
Output:
His son
Police are seeking former village chief in north china for allegedly killing his political rivals in an attack apparently motivated by local power plays, state press reported monday.
Input:
Can you generate short summary of the above paragraph? Former chinese village head wanted for political murders
Output:
FedIT model [42]: The FedIT model is the final aggregated global model derived from diverse local client datasets after training. It embodies the essence of collaborative learning inherent to federated learning, assimilating knowledge from a multitude of client-specific data sources.
FedLoRA model [38]: Here, we adapt the training paradigm in paper [3 to NLP tasks by focusing on training the lightweight LoRA for aggregation while keeping the majority of the LLM parameters frozen. Subsequently, a personalized adaptation process is employed, where the globally aggregated LoRA undergoes further local training on each local client's dataset to tailor the learning outcomes to individual client needs.
FedDPA-F: FedDPA-F is the combination of the global adapter and the local fine-tuning adapter. During the inference, the scale factor is À in the instance-wise dynamic weighting mechanism.
FedDPA-T: FedDPA-T the combination of the global adapter and the local training adapter. Since the global adapter contributes to the training of the local adapter for personalization, is essential to restrict the similarity score during the inference. This adjustment is necessary to ensure that the integration of the global and local adapters achieves optimal personalization outcomes. Thus, the scale factor is set equal to the updating weight a used in the local adapter training.
All models are implemented using LoRA to enhance learning efficiency, with the rank of LoRA set as 8 and only applied to Wg and W.. For FL methods, each client conducts 10 local epochs
Table 7: Prompt Template.
Template
Instruction: [instruction]
Prompt Input
Response:
14

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with batch size of 32. We implement all the methods using Py Torch and conduct all experiments on NVIDIA Quadro RTX 6000 GPU.
A.3 Algorithm
Algorithm I: FedDPA-F
R Number of clients M; number of communication rounds K; local step size y7; freeze foundation model 0: initial global adapter A0g and local adapters A ... ,A0
local training datasets D Dirains"" "..
Dtrain
M
// Learn Global Adapter
for k 4- 1 to do
Sample clients S C {1,..., M);
Communicate A0g to all clients m E S;
for each client E S in parallel do
Initialize A0% +- A0;;
A + Client local training( (0, A0,), Dm Communicate 20t to the server;
trains 7);
[Equation
Construct 20, E [S] A
[Equation
/ Learn Local Adapters
for each client E (1,... M) in parallel do
Initialize A0 <- A0g:
T Client local training( (0, A D train n):
[Equation
return 20,. (20).... ... A0M).
Algorithm 2: FedDPA-T
Require Number of clients M; number of communication rounds K; local step size 1ÿ freeze foundation model 0; initial global adapter 20, and local adapters ... ,.,'
local training datasets Dirasno" M
for k 1 to K do
Sample clients S C (1,..., M); Communicate 20, all clients m S:
for each client m E S in parallel do
Initialize 20 +- 20, and A + A0mmk-1
// Learn Global Adapter
A0 +- Client local training( (0,,0)000000000))) A0,). Dirain 7):
[Equation
9
// Learn Local Adapter
A + Client local training( (0,20,)) A0M D Dirain 1);
[Equation
Communicate A0m to the server;
Maintain 1. locally;
Construct 20, = E 1 Amm.
[Equation 2
[S] 4
return A6,, (10) A0M).
B Additional Experiments
B.1
Adaptability Analysis
To enhance applicability across diverse non-llI) environments, our method is meticulously designed with a high degree of flexibility for its adaption across various global learning frameworks, back- bones and PEFT methods for different scenarios. This adaptability is simply achieved through the straightforward substitution of the FedAvg, LLM and LoRA with alternative aggregation methods, transformer-basee foundation models and adapter-based PEFT methods during the training. In our experiment, we employ FedAvg, I.LM and LoRA as representative examples, demonstrating our methods' superior performance compared to other baselines as indicated in Table and Table To
15

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further validate the effectiveness and versatility of our approach within different federated learning contexts, we adapt our methods include the FedProx [0) framework and also implement other baselines (FedIT and FedLoRA) within FedProx to maintain a fair comparison.
Results presented in Table & indicate that methods, both training and fine-tuning methods, outperform competing approaches. Specifically, FedDPA-T excels in personalization, while FedDPA- leads in test-time personalization, maintaining consistent performance with FedAvg. These findings underscore the robustness and consistent efficacy of our methods across various global learning paradigms for different non-IlD scenarios.
Table 8: Personalization and test-time personalization results of different models with FedProx framework on federated dataset FedDPA-F represents the model with the local fine-tuning adapter and FedDPA-T represents the model with the local training adapter. Linguistic represents the linguistic acceptability task, Word Dis represents the word disambiguation task, and Question CI SS represents question classification task.
Federated Dataset
Methods
Para phrase
Entail -ment
Structure to Text
Text For Linguistic Acc
Word Dis
Core -erennce
Question CI.S
Average
-matting
Personalization
FedIT
71.00
83,00
70.38
96.32
69.50
63.50
73.55
91.50
77.34
FedLoRA
79.00
85.00
70.63
96.60
69.00
63.00
78.85
88.50
78.82
FedDPA-F
77.50
85.00
71.92
96.78
73.00
63.50
77.87
89.50
79.38
FedDPA-T T
79.50
85,50
71.62
96.89
76,00
60.00
77.28
93.50
80.04
Test-Time Personalization
FedLoRA
76.76
75.82
74.11
74.36
75.01
72.27
77.16
74.87
75.05
FedDPA-F
77.25
76.01
76.72
76.95
77.21
75.48
77.31
76.14
76.63
FedDPA-T
75.64
74.62
75.58
75.01
74.90
74.21
74.80
75.35
75.01
B.2 Instance- Dynamic Weighting Mechanism Analysis
In this section, we further examine the impact of the instance-wise dynamic weighting mecha- nism, including the similarity metric, the selected local instance number and the type of instance representation.
employ the similarity metric calculate the average similarity scores each input instance, which serves as the weight a, to dynamically balance the global and local adapters. For this purpose, cosine similarity is selected in experiment due to its better robustness and normalization with high-dimensional vectors than other metrics, and its superiority has been demonstrated in many NLP/CV works (25] 37 36). Additionally, conducted an ablation study comparing other metrics like L2-norm and Pearson correlation, and the results in Table demonstrtttttteee that cosine similarity outperforms other similarity metrics.
Impact of Similarity Metric. In Section 4.3
the selection of S, representing the number of local instances for similarity calculation, is pivotal. To comprehensively evaluate the effect of varying the number of these instances, we conduct series of experiments employing distinct local instance numbers, specifically S (1, 5, 9). The accuracy results, as depicted in Figure 5 illustrate the dependency of model performance on different instance numbers S. As demonstrated in Figure (a), in the context of personalization, it observed that our models attain a plateau accuracy when the instance number exceeds 5. This indicates a stabilization in model performance beyond this threshold of local instances. Furthermore, Figure (b) delves into the realm of test-time personalization. The findings here reveal similar results, indicating that variations in the instance number do not markedly impact the model's performance in test-time personalization.
Impact of Instance Number S. In Section 4.3
Impact of Instance Representation. In Section 4.3 our method entails utilizing the embedding of the final token from the last hidden layer the LLM, denoted as 'LAST', as the input instance representation for the purpose of similarity calculation. In this exploration, we delve into another instance representation strategy, which involves employing the average embedding of all tokens from the final hidden layer of the LLM. herein referred to AVG'. The comparative analysis, presented
16

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Personalization (Dataset
(b) TTP (Dataset
80.5
27.5
77
Accuracy (%6
79.5
Accuracy
76.5
76
FedDPA-1
FedDPA-
78.5
FedDPA-T FedDPA.F
75.5
75
Instarice Number
Instance Number
Figure 5: Average accuracy varies different instance numbers. TTP represents test-time personal- ization.
Table 10, demonstrates that employing the embedding of the last token yields superior performance relative to the strategy of averaging the embeddings of all tokens. This observed difference in perfor- mance can be attributed to the decoder structure inherent to LLMs, wherein the final token is capable of attending all preceding tokens, thereby encapsulating comprehensive sentence-level information.
Table 9: Ablation study of similarity metric (Sim). P represents personalization, and TTP represents test-time personalization. -L2 repre- sents using the L2-Norm metric, Pearson rep- resents using the Pearson correlation as metric, and Cosine represents using the cosine similarity as the metric.
Table 10: Ablation study of instance representa- tions (Emb). P represents personalization, and TTP represents test-time personalization. LAST represents using the embedding of the final token from the final hidden layer of LLM as instance representation, and AVG represents using the average embedding of all tokens from the final
Fed Dataset
hidden layer of LLM as instance representation.
Methods
Sim
P
TTP
Fed Dataset 1
Methods
Emb
FedDPA-F
-L2
77.69
77.10
TTP
Pearson
79.09
76.78
AVG
79.30
76.77
FedDPA-F
Cosine
79.48
77.12
LAST
79.48
77.12
FedDPA-T
-L2
77.58
77.30
FedDPA-T
AVG
79.65
73.36
Pearson
79.73
75.27
LAST
80.22
75.47
Cosine
80.22
75.47
B.3
Model Scalability Analysis
In order to examine the effectiveness of model scalability, we conduct experiments based larger model, LLaMA-13B. The outcomes, as presented in Table elucidate that larger models exhibit superior performance over their smaller counterparts across all personalization methods evaluated. Furthermore, it is noteworthy that FedDPA-T surpasses FedDPA-F in terms of personalization and achieves comparable results in test-time personalization. This analysis underscores the inherent advantages of larger models in enhancing model performance, alongside the advance of the FedDPA-T approach in the context of personalization and adaptability to test-time conditions.
Table 11: Ablation study of model size. represents personalization, and TTP represents test-time personalization.
Fed Dataset 1
Methods
Size
p
TTP
7B
79.48
77.12
FedDPA-F
13B
81,52
80.55
7B
80.22
75,47
FedDPA-T
13B
82.76
80.47
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B.4 Communication and Computation Analysis
In this section, we undertake a detailed examination of both the communication and computation overhead associated with our proposed model in comparison to other baseline models. The results, as detailed in Table [2] delineate the communication and computation burdens imposed by various models. Given that these models are all based on the LoRA framework and exclusively transmit LoRA weights for aggregation (with our methods specifically transmitting only the global LoRA weights), they inherently sustain minimal communication overhead. Regarding the computation overhead, the LoRA architecture permits the training of both local and global LoRAs in parallel, resulting in marginal increase in computational demands for FedDPA-T. Conversely, FedDPA-F learns the local LoRA through an additional fine-tuning phase, thereby not imposing any additional computational overhead during the training phase.
Additionally, we have conducted an analysis of the inference time associated with our models. This examination involved calculating the average inference time per instance for FedLoRA, FedDPA without the instance- wise dynamic weighting mechanism, and FedDPA. As illustrated in Table is observed that our methods incur slightly higher inference time compared to FedLoRA, This marginal increase in inference time underscores the efficiency of our proposed methods, demonstrating that the enhanced performance and capabilities are achieved with minimal impact on computational efficiency during inference.
Table 12: The communication and computation overhead of FedDPA and other baselines on Feder- ated Dataset 1.
Table 13: Average inference time per instance. Auto represents the instance- wise dynamic weighting mechanism.
Methods
Comp.
Methods
Time
FedIT
4.2M(0.06%)
0.277 TFLOPS
Fedl LoRA
3.84s
FedLoRA
4.2M(0.06%)
0.277 TFLOPS
FedDPA (w/o auto)
3.91s
FedDPA-F
4.2M(0.06%)
0.277 TFLOPS
FedDPA
4.13s
FedDPA-T
4.2M(0.06%)
0.281 TFLOPS
C Discussions
C.1 Extend to Other Scenarios of Test-Time FL
In this paper, we primarily consider an ideal scenario for our proposed test-time personalization setting, where all tasks are included for all clients. In this section, will discuss our methods under alternative scenarios.
In practical applications, is possible that some tasks remain unseen by all clients during training and may only appear during the testing phase for certain clients. In this scenario, test-time distribution shifts arise from these unseen tasks. According to previous works [5] [2], generic features learned through FL are robust to distribution shifts, even those originating from unseen test-time tasks. Consequently,
method can be directly adapted this scenario. We conducted experiments to evaluate our methods against other baselines on three unseen test-time tasks. All methods and baselines are trained
constructed Federated Dataset I and tested on three tasks not included in Federated Dataset 1. For personalized methods, we report the average score across all clients and the maximum score among all clients to provide comprehensive comparison.
As shown in Table 14 FedDPA-T outperforms all other models, indicating the effectiveness of our method unseen test-time tasks. Additionally, FedDPA-F surpasses FedLoRA, suggesting that the generic features learned through FL across diverse data distributions are robust to various distribution shifts, consistent with the findings in 22) Despite this, other techniques targeting these unseen test-time tasks could further enhance our proposed methods, which we leave for future work to explore.
Our methods are also applicable to scenarios involving the introduction of new clients. As previously analyzed, our methods are robust to different distribution shifts. By computing the similarity between instances from new clients and existing clients through our instance-wise dynamic weighting mechanism, we can identify the most similar existing client. The model of this identified client can
18

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Table 14: Test-time performance on unseen tasks. All models are trained on Federated Dataset I, and these unseen test-time tasks are not included in Federated Dataset AVG represents the average score across all clients for this task, while MAX represents the highest score among these clients for this task. The best performance for AVG is bolded, and the best performance for MAX underlined.
FedLoRA
FedDPA-F
FedDPA-T
Test-Time Task
FedIT
AVG
MAX
AVG
MAX
AVG
MAX
Summarization
22.40
22.25
22.42
22.37
22.52
22.46
22.65
Reading Comprehension
69.50
68.88
73.00
69.44
72.00
71.88
76.00
Open Domain QA
78.32
76.46
78.96
78.01
79.61
78.76
79.92
then used as the initial model for the new clients, providing a more effective starting point for further training.
C.2 Limitations
The proposed pual-personalizing adapter architecture limited by model scales: the proposed methods rely on the foundation model, presupposing that each client possesses sufficient memory capacity and computational resources to store and train the foundation model with PEFT methods. 2) secure issues: the framework operates under the assumption that all clients are trusted and legally entitled access and utilize data stored on them, and the whole process does not suffer from any attacks.
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Reviews:
[{"review_id": "R8nCDN7rCP", "reviewer": "Reviewer_D5Ab", "summary": "Federated Foundation Models (FedFM) is an emerging research domain to study collaboratively fine-tuning the pre-trained foundation models. This paper studied a test-time distribution shift problem on FedFM by proposing a new dual-personalising adapter.", "questions": "1. Is it possible to apply this dual-personalizing adapter framework to other types of data, for example, image recognition, recommendation, time series, and multimodality data?\n\n2. Is it possible to merge two Federated Datasets into one so that you can have 16 clients? \n\n3. Line 315, how to fix the value of alpha? According to the design, the value of alpha should be decided by a dynamic weighting mechanism. \n\n4. In future work, the author mentioned “theoretical analysis and more datasets”. Why do you think this method needs a theoretical analysis that can make a significant difference to this paper? What are the potential major challenges to applying this method to other datasets that prevent you from finishing these experiments in this paper?", "limitations": "N/A", "rating": 7, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1) The proposed method is novel. The targeting problem is new while a new setting is created in the experimental study.\n\n2) The paper presents high-quality content and novelty design. The claimed points are well supported by theoretical discussion and experimental analysis. The appendix and source codes provide sufficient details of the experiment.\n\n3) The clarity of this paper is excellent.\n\n4) The targeting problem is significant to the emerging domain of FedFM.", "weaknesses": "1) According to Eq 1, the P_all is all potential distributions. However, in real applications, the clients are usually insufficient to represent all possible distributions. A discussion is required to clarify this assumption. \n\n2) The implementation framework heavily relies on the LoRA, thus the contribution is reduced.\n\n3) The experiment setting assumes each client owns one type of dataset or task. What’s the difference with multi-task learning? Should you compare it with some multi-task learning baseline methods?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**W1: Discussion of assumption.**\n\nYes, our main experiments are based on the assumption that all possible distributions are included in all clients. We already discussed this in the first paragraph of section 4 and Appendix C. To enhance clarity, we will rewrite this part and emphasize keywords in bold to make it clearer.\n\n**W2 and Q1: Heavily rely on the LoRA and adapt to other data types.**\n\nOur method is designed with a high degree of flexibility, facilitating its adaption across various adapter-based PEFT methods and transformer-based foundation models of different data types. In this paper, we just use LLM and LoRA as examples to illustrate our method, and our method can easily be adapted to other frameworks by substituting the LoRA and LLM with alternative adapter-based PEFT methods and transformer-based foundation models. We will revise our paper by including a discussion about adaptation to other adapter-based PEFT methods (e.g., series adapter) and other foundation models (e.g., ViT for images and UNITER for multimodality data).\n\n**W3: Different with multi-task learning.**\n \nMulti-task learning is centralized, and tuning on the combination of multi-task data of a centralized foundation model can be taken as the multi-task learning baseline. As in the centralized foundation model, all tasks are standardized into a uniform format (refer to Appendix A.1 and Table 6), and the model already acquires task-agnostic token embedding by the extensive data pre-training; directly tuning on these multi-task data is the implementation of multi-task learning with foundation models [1]. We have included this baseline, referred to as \"Centralized,\" in our experimental comparisons. In addition, our setting accounts for test-time distribution shifts, a factor not typically addressed in multi-task learning. Although our main experiments are under the assumption that all possible distributions are included in all clients, our setting can also allow for scenarios involving unseen distributions for all clients, and our methods have demonstrated robustness in these scenarios, as detailed in Appendix C.1. We will add a discussion in related work to clarify the difference with multi-task learning.\n\n[1] Yu, J., Dai, Y., Liu, X., Huang, J., Shen, Y., Zhang, K., ... & Chen, Y. (2024). Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras. arXiv preprint arXiv:2404.18961.\n\n**Q2: Merge two federated datasets into one for 16 clients.**\n\nYes, we can merge these two datasets into one for more clients. As explored in section 6.2 of the ablation study on the impact of client number, our methods could maintain performance when scaling up the clients (up to 40 clients) in Figure 4. Therefore, our methods are supposed to be effective when having more clients and more tasks.\n\n**Q3: How to fix the value of alpha.**\n\nIn line 315, we aim to investigate the impact of the global adapter on the training of the local adapter in FedDPA-T. As illustrated on the left of Fig 2(b), during the local adapter training of FedDPA-T, we employ the frozen global adapter to expedite the learning of the local adapter. This is because the global adapter contains certain task-related knowledge to accelerate the local adapter’s learning. It is worth noting that during the training phase, there are no distribution shifts; such shifts occur solely in the inference/testing phase, and the dynamic weighting mechanism is only used in the inference phase to determine the value of alpha for addressing test-time distribution shifts. In contrast, in the training phase, alpha is only used to control the contribution of the frozen global adapter to the local adapter learning. Therefore, during the training of FedDPA-T, we can fix the value of alpha, as its function differs from that during inference. To enhance clarity, we will revise the symbol and use two distinct symbols to differentiate these uses.\n\n**Q4: Theoretical analysis and more datasets.**\n\nFor theoretical analysis, we believe that further work could rethink this problem within the context of existing theoretical frameworks and establish a new theoretical framework of FFM. For example, we can start from the perspective of out-of-domain generalization theory to rethink the generalization analysis and establish a new generalization and convergence analysis framework of FFM. \n\nOne main challenge in applying to more datasets is the significant computational resources required. Given that foundation models often consist of billions of parameters, they necessitate considerable time and storage for tuning. Additionally, the computational demand varies significantly across data types; for example, a thousand text data in the Flan occupy only about 1MB, whereas the same number of images in ImageNet can require around 130MB. Therefore, we believe that future work should explore more efficient methods for tuning FFM on larger datasets.", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Ie11uw1Nxu", "reviewer": "Reviewer_vXiN", "summary": "This paper proposes a novel dual-personalizing adapter to tackle the test-time distribution shift for federated foundation models (FedFM). FedFM is a new research domain to enhance foundation models by leveraging many fine-tuning tasks on many protected datasets for end users. The solution is essentially to tackle the trade-off of personalisation and generalisation of the client-specific models in FedFM.", "questions": "1. In line 260, what type of similarity function will be chosen and why?\n2. According to the design of alpha, if the new test instance is more similar to the client’s training instances in a representation space, the local adapter will gain a bigger importance weight. Any theoretical discussion about this?\n3. Why FedDPA-F wins more times than FedDPA-T?\n4. Are there any other data sources other than Flan that can be used in this type of setting?", "limitations": "N/A", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. FedFM is a new research domain that has been regarded as an important pathway to enhance foundation models by leveraging decentralized data. Tackling the tradeoff between client-specific personalisation and generalisation is a key challenge of the FedFM research domain. \n2. The proposed method is a new idea with original design. Moreover, this paper is the first work to discuss the test-time generalization challenge on FedFM.  \n3. The proposed method is technique sounds. The design of the method is simple yet effective. It is very easy to follow. \n4. In terms of clarity, the paper’s contents are well organized and clearly presented with easy-to-understand figures and well-described details. \n5. The appendix with experiment details and the source codes are provided to support the reproducibility of this work.", "weaknesses": "1. The important weight of the global adapter and personalised adapter is a key factor of the proposed dual-personalising adapter mechanism in FedFM. More insightful discussion is expected to analyse the selection of the important weights. \n2. The proposed instance-wise trade-off mechanism essentially relies on the similarity between the test instance and training dataset. The current solution is straightforward and reasonable, however it could be elaborately designed.\n3. It would be better if a large-scale experiment could be conducted to further evaluate the proposed method. However, considering the lack of computation resources, this paper’s experiment is sufficient enough as an initial exploration and discussion of this research direction.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**W2 and W3: Design of trade-off mechanism and large-scale experiment.**\n\nThanks for your insightful advice. We will further explore better mechanisms from the perspective of generalization theory and more datasets in real applications.\n\n**Q1: Choice of similarity function.**\n\nWe select cosine similarity due to its better robustness and normalization with high-dimensional vectors than other metrics, and its superiority has been demonstrated in many NLP/CV works. Additionally, we conducted an ablation study comparing various similarity functions in Appendix B.2, and the results in Table 9 demonstrate cosine similarity outperforms other similarity functions.\n\n**W1 and Q2: Theoretical analysis of the design of alpha.**\n\nIn section 3.2, we conducted a preliminary theoretical analysis of the discordance between personalization and test-time tasks, which motivated us to learn two adapters: a local adapter for personalization task (training distribution) and a global adapter for test-time tasks. Therefore, when a test instance is more similar to the client’s training instance, we intuitively infer that its distribution is more similar to the training data, and because the local adapter aimed for personalization contains more knowledge of training data distribution, we increase the weight of local adapter. We will further explore the theoretical relation between them. \n\n**Q3: Why FedDPA-F wins more time than FedDPA-T.**\n\nIt is because that FedDPA-F maintains a more generalized local adapter than FedDPA-T. The difference between FedDPA-F and FedDPA-T lies in the local adapter: FedDPA-F utilizes the global adapter as initialization of the local adapter, while FedDPA-T randomly initializes the local adapter. Given that the global adapter aggregated on different distributions matin certain generalization capabilities, the local adapter of FedDPA-F has better generalization performance than that of FedDPA-T, which leads to better performance on most test-time tasks. We will add this discussion in our experiment analysis to clarify.\n\n**Q4: Other data sources.**\n \nOur method is designed with a high degree of flexibility for its adaption across various data sources. As illustrated in Appendix A.1, any public text data sources (e.g., Dolly), uniformed into the generative task with appropriate prompts like Table 6, can be used in this setting. In addition, other types of data can also be applied to our methods by replacing LLM with the corresponding foundation models. For example, for image datasets (e.g., ImageNet, MS COCO), ViT can be used as the foundation model in our methods for this setting. We will add a discussion about the adaptation to other data sources.", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WfQ3mXB59b", "reviewer": "Reviewer_mFUh", "summary": "The authors use two set of adapters to personalize a model with federated learning. The idea is to use FL to learn the global adapter whereas each device has a local adapter to personalize the model for each client.", "questions": "See Above", "limitations": "N/A", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "- The paper is well written and easy to understand \n- The overall approach is sound\n- THe authors evaluated their approach on a number of NLP tasks", "weaknesses": "Overall this is an interesting paper. However these are some areas to potentially improve: \n\n- Firstly, the overall premise: the authors say that this approach is used to learn from distribution shifts between training and test datasets. However, this is the whole reason we use federeated learning. Typically we have models trained on public deatasets (e.g., imagenet, wikipedia, etc), but these don't work on real-world applications because the domain is slightly different. FL was proposed so we can get a real-world (and real-time) signal about in-domain distributions in a privacy-preserving. As a result, we actually do want to learn this \"test\" distribution during FL. It is unclear why in the FL scenario, the \"test\" distribution is not similar to the train distribution. Overall, the paper would benefit for a much stronger motivation. \n\n- There are a lot of works that use federated learning to learn a global model and then personalize this model for each client (e.g., https://arxiv.org/abs/2103.00710). As a result, the overall approach of training a global model and then having extra client-side personalization is not entirely novel. Furhtermore, there have been many works that train adapters instead of full models in order to save on communication and on-device compute. \n\n- At section 3.1 the authors say there are two datasets belonging to two different distributions (Ps vs Pt): why does this happen within a device ? How do you detect that the distribution has shifted ? How does this happen in practice ?\n\n- The evaluation has been done in a very artificial scenario where each of the 8 devices had a completely different NLP task (section 5.1). This is quite extreme and it is likely to favour the results that take advantage of personalization. However, this extreme is unlikely to happen in the real FL deployments: typically we want to build a global model from a set of non-IID users but not to an extreme where each user has a different task. Furthemore, we attempt to train large-enough models that can tolerate some distribution shift (as long as it has been seen in the data).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**W1: The paper would benefit for a much stronger motivation.**\n\nUnlike traditional FL (especially personalized FL) which primarily addresses heterogeneity among clients during training, our setting also considers heterogeneity within each client during testing, building on insights from previous works [1]. Since the learning process of foundation models will involve more heterogeneous datasets from different tasks compared with the traditional machine learning methods, our setting aims to consider more complex scenarios with various distribution shifts in federated foundation models. For example, in traditional personalized FL, each client's training and testing are restricted to the same distribution (e.g., client 1 trains and tests on task 1); in contrast, our setting involves clients training on one task and testing on multiple different tasks (e.g., client 1 trains on task 1 and tests on tasks 1, 2, 3...). \n\nMoreover, imagine the on-device foundation model in the near future, the model should be lightweight and versatile enough to tackle many different tasks. To this motivation, this paper is the first step towards exploring a solution that can enable the lightweight on-device foundation models with desired performance on training data and also gain a better test-time generalization while the test data is from another domain or task.\n\n[1] Tan, Y., Chen, C., Zhuang, W., Dong, X., Lyu, L., & Long, G. (2024). Is heterogeneity notorious? taming heterogeneity to handle test-time shift in federated learning. Advances in Neural Information Processing Systems, 36.\n\n**W2: Novelty of our methods.**\n\nAs discussed in the recent literature on Federated Foundation Models (FFM) [2], there are many new challenges that need to be rethought. For example, training versatile foundation models requires incorporating more heterogeneous datasets within the federated learning framework. Moreover, the model communication-efficient and fine-tuning strategy also needs to be considered in the federated foundation models. Before the research community can go further to explore more exciting applications of federated foundation models, some base work must be explored. Our work is to rethink the problems of traditional FL (especially PFL) based on FFM and establish the benchmark of personalized FFM. \n\nWe are the first to explore the test-time generalization of personalized FFM. The overall design is new with refined setting (test-time personalization) in FFM and the technique framework is composed of multiple pieces including a new training paradigm with self-reconstructed datasets to train personalized FFM, and also a new dynamic weighting mechanism to determine the combination of different adapters for test-time personalization.\n\n[2] Ren, C., Yu, H., Peng, H., Tang, X., Li, A., Gao, Y., ... & Yang, Q. (2024). Advances and open challenges in federated learning with foundation models. arXiv preprint arXiv:2404.15381.\n\n**W3: Different distributions within a device and how to detect shifts.**\n\nIn federated foundation models, each client’s device will be deployed with a lightweight intelligent assistant to tackle a broad range of downstream tasks, thus it is very likely that the client needs to tackle unseen tasks with different distributions during testing.\n\nIn this paper, we make new assumptions for FFM: 1) for each device, the test distribution may differ from the train distribution, 2) different clients have different distributions as well, and 3) the test distribution of client A could be observed by another client in their training tasks. For example, in practice, a client accustomed to writing emails in English may require translation assistance when working on a new project in Chinese. Here, the client has ample historical data for training in English email writing, but none for Chinese translation, which only emerges during the testing/inference phase. Additionally, other clients may be specialized in translation tasks.\n\nTo detect the distribution shifts during the testing phase, we designed an Instance-wise Dynamic Weighting mechanism. Specifically, we measure the similarity between the representations of test and training instances to adjust the importance weight of local adapter and global adapter. More detailed discussion could be found in Section 4.3.\n\n**W4: Extreme evaluation setting.**\n\nThe problem setting and application scenario in federated foundation models have been different from the traditional federated learning methods. Our research is focused on the challenges of FFM, wherein foundation models, pre-trained on extensive datasets, already exhibit a degree of generalization capability towards general non-IID problems —a topic thoroughly investigated within traditional FL. Consequently, in the realm of FFM, we aim to address more complex scenarios, such as cross-domain and cross-dataset challenges, where centralized foundation models typically underperform [3].  \n\nIn our future work, we could add more experiments to include traditional federated learning settings by splitting a dataset into many pieces with a slight non-IID. We believe our proposed method can be easily adapted to this type of non-IID. \n\n[3] Wang, Y., Ivison, H., Dasigi, P., Hessel, J., Khot, T., Chandu, K., ... & Hajishirzi, H. (2023). How far can camels go? exploring the state of instruction tuning on open resources. Advances in Neural Information Processing Systems, 36, 74764-74786.", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "G6U4LkDkCF", "reviewer": "Reviewer_94he", "summary": "This paper focuses on personalization and test-time adaptation in federated learning of foundation models. The authors propose two solutions for this setting and show its performance on two splits of FLAN dataset.", "questions": "- Why the convergence curve of FedIT so distinct from the others? Also, it is unclear what is the difference between FedIT and FedLoRA.", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The paper is generally easy to read.\n- The constructed experimental setup is genenrally reasonable.", "weaknesses": "- The motivation and significance of the setting is not sufficiently strong. The training method in this paper is not new in personalized FL [1,2]. The test-time adaptation method is straightforward and does not convey new insight. Therefore, I do not clearly see the significance of considering personalization and test-time adaptation at the same time. \n\n- Limited performance gain. Despite that the paper focuses on personalization and test-time adaptation at the same time, it proposes two methods, one performs better at personalization while the other performs better at test-time adaptation. This is really confusing. If the authors can not find one solution that fits both two metrics, then why bother to consider these two issues in one paper?\n\n- Limited performance gain. From the two main tables in the paper, the performance of FedLoRA is stable across two metrics, which always performs the second best.\n \n[1] Exploiting shared representations for personalized federated learning\n\n[2] Perada: Parameter-efficient and generalizable federated learning personalization with guarantees", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**W1: Cannot clearly see the significance of considering personalization and test-time adaptation at the same time.**\n\nOur proposed method is **not doing test-time adaptation**. In general, the test-time adaptation methods [1] usually involve fine-tuning steps to align the model parameters with new distributions in test time. In contrast, our proposed method is designed without any adaptation step, aiming instead for test-time generalization where clients must maintain performance on their main tasks while also demonstrating generalization capabilities on new tasks during the testing stage. \n\nMoreover, our method addresses an innovative application scenario known as federated foundation models (FFM) [2], an emerging domain that significantly diverges from traditional federated learning methods. We are the first to explore the test-time generalization on personalized FFM. Unlike the works you mentioned, we refined a new setting (test-time personalization) in FFM, proposed a new training paradigm with self-reconstructed datasets to train personalized FFM in extreme scenes like cross-tasks, and also introduced a new dynamic weighting mechanism to determine the combination of different adapters for test-time personalization. We will add a discussion about the difference between our work and your mentioned works in related work.\n\n[1] Liang, J., He, R., & Tan, T. (2024). A comprehensive survey on test-time adaptation under distribution shifts. International Journal of Computer Vision, 1-34.\n\n[2] Ren, C., Yu, H., Peng, H., Tang, X., Li, A., Gao, Y., ... & Yang, Q. (2024). Advances and open challenges in federated learning with foundation models. arXiv preprint arXiv:2404.15381. \n\n**W2: Propose two methods and can not find one solution that fits both two metrics.**\n\nOur paper aims to improve the test-time generalization without sacrificing the performance on personalization metric in federated settings. In traditional machine learning methods, an algorithm should be evaluated on both validation dataset and test dataset, which in our context correspond to the personalization metric and test-time generalization metric, respectively.\n\nOur proposed framework (ref to Section 4), model architecture (ref to Figure 1) and loss function (ref to Equation 1) are unified design. The mentioned two methods can be considered as two hyperparameters to control the updating strategy (sequentially or iteratively) of local and global adapters within the federated learning process. Specifically, FedDPA-F adopts a sequential approach, first optimizing the global adapter and then optimizing the local adapter. In contrast, FedDPA-T alternates between optimizing the global and local adapters iteratively during each communication round. To facilitate understanding, Figure 2 illustrates the two options of the hyperparameter in the learning process. In the distributed learning scenario, it is a common way to try different updating strategies in federated learning, such as [3].\n\n[3] Tian Li, Shengyuan Hu, AhmadBeirami, and Virginia Smith, “Ditto: Fair and Robust Federated Learning Through Personalization”, ICML 2021\n\n**W3: Limited performance gain.**\n\nThe objective of this paper is to improve the test-time generalization of federated foundation models without sacrificing personalization performance. In two main tables (Table 1 & 2), in the test-time generalization metric, our proposed method shows significant improvement over FedLoRA. For example, both FedDPA-F and FedDPA-T demonstrate average improvements of approximately 2.3% and 0.9% over FedLoRA. Notably, in the summarization task, our approaches achieve an **8.4%** higher score than FedLoRA.\n\nSimilarly, in the personalization metric, our method achieved relatively higher performance than FedLoRA. For example, both FedDPA-F and FedDPA-T demonstrate average improvements of approximately 0.5% and 2.3% over FedLoRA. Notably, in the reading comprehension task, our methods outperform FedLoRA by **5.9%** in terms of personalization.\n\nMoreover, we will further explore one more analysis of the statistical significance. Specifically, it will involve assuming that our method can improve test-time generalization performance and subsequently employing statistical tests, such as the P-value approach, to verify the significance and validate the efficacy and confidence of our method. \n\n**Q1: Difference between FedIT and FedLoRA.**\n\nCompared to personalized federated learning methods, FedIT focuses on training a global model applicable across all clients, without incorporating personalization techniques. In each communication round of the convergence curve, FedIT evaluates the global model on test data. Conversely, FedLoRA, a personalized federated learning method, evaluates performance using fine-tuned models in each communication round. Therefore, FedIT shows a relatively lower accuracy in the early stage of the learning process. For a more detailed discussion, please refer to Appendix A.2.", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We thank all the reviewers for their valuable reviews. We also appreciate their recognition of the key contributions of our work and the efficacy of our method.

1. **Contributions** to federated foundation models:

    - "The targeting problem is significant to the emerging domain of FedFM." (Reviewer D5Ab)

    - "FedFM is a new research domain that has been regarded as an important pathway to enhance foundation models by leveraging decentralized data. Tackling the tradeoff between client-specific personalisation and generalisation is a key challenge of the FedFM research domain." (Reviewer vXiN)

2. The **novelty** of our method:

    - "The proposed method is novel. The targeting problem is new while a new setting is created in the experimental study." (Reviewer D5Ab)

    - "The proposed method is a new idea with original design. Moreover, this paper is the first work to discuss the test-time generalization challenge on FedFM." (Reviewer vXiN)

3. Comprehensive experiments and **efficacy** of our method: 

    - "The paper presents high-quality content and novelty design. The claimed points are well supported by theoretical discussion and experimental analysis. The appendix and source codes provide sufficient details of the experiment." (Reviewer D5Ab)

    - "The appendix with experiment details and the source codes are provided to support the reproducibility of this work." (Reviewer vXiN)

    - "The authors evaluated their approach on a number of NLP tasks." (Reviewer mFUh)

4. **Excellent presentation** of our paper:

    - "The clarity of this paper is excellent." (Reviewer D5Ab)

    - "In terms of clarity, the paper’s contents are well organized and clearly presented with easy-to-understand figures and well-described details." (Reviewer vXiN)

    - "The paper is well written and easy to understand." (Reviewer mFUh)

Detailed responses to each reviewer are provided below. We will incorporate all the feedback in the final version.

Meta-review: In this paper, the authors concentrate on foundational models within the framework of federated learning and aim to propose a novel approach. Currently, the paper has received four reviews, resulting in the following scores: one Strong Accept, one Accept, one Weak Accept, and one Reject.

From the positive points of the reviews, most of the reviewers feel that (1) the paper is easy to understand, (2) the proposed method is technically sound, and (3) the overall idea is interesting. However, at the same time, the reviewers have pointed out several weaknesses, such as an unconvincing motivation, unsatisfactory experimental results, and unclear technical details.

In response to these weak points, the authors provided feedback to each reviewer during the rebuttal phase, successfully addressing or clarifying quite a few concerns. Nevertheless, two main issues are not fully addressed by the rebuttal:  
(1) Unconvincing motivation: it is not entirely clear that which real-world applications motivate the studied scenario and proposed method. (Reviewer mFUh and Reviewer 94he)  
(2) Unsatisfied experimental result: two versions of their solution (FedDPA-F and FedDPA-T) are compared in the experiment, while FedDPA-T can perform worse than the baseline. (Reviewer 94he)  
Unfortunately, when the discussion period ends, the scores are still diversified, which makes the paper a borderline work.

Although this is a tough decision to make, we recommend accepting this paper for NeurIPS 2024 due to the following reasons:  
(1) Most of the reviewers feel positive about this work;  
(2) The authors have tried to resolve or explain all the weak points during the rebuttal;   
(3) From a long-term perspective, it is meaningful for a top-tier conference to encourage more researchers to explore emerging research domain (eg FedFM as done in this work), even if some preliminary results may not be perfect.   
Meanwhile, we also hope the authors will consider and address the remaining concerns during the camera ready.
Decision: Accept (poster)