Title: Dual-Personalizing Adapter for Federated Foundation Models
Venue: NeurIPS 2024
Accepted: yes
Keywords: Federated Learning

Foundation Models

Personalization

Test-Time Distribution Shifts
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

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 Fed erated D ualP ersonalizing A dapter (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 with released code.

1 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) [44, 41] offer a promising solution by integrating federated learning (FL) frameworks to enhance the foundation models in a decentralized manner. Built upon existing Parameter-efficient fine-tuning (PEFT) methods [33, 14, 13], FedFM is 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 [16, 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 a new project in Chinese. Therefore, it is imperative for the deployed machine learning model to 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, 16] 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 [2, 15] 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 a novel FedFM framework that is robust to client-specific alignment and test-time distribution shifts simultaneously. 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 a target task, and 2) during testing, each client's personalized model needs to 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 a sweet trade-off between client-specific model personalization and model generalization to test data.

For test-time personalization in FedFM, two primary challenges-test-time distribution shifts and personalization-necessitate learning tailored to distinct objectives, and the training cost of foundation models also represents a significant concern. To address these issues, we explore a simple yet effective method, dubbed Fed erated D ualP ersonalizing A dapter (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 to facilitate prediction, where an instance-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-art performance on benchmarks and all data and code are released 1 . Our main contributions are summarized as follows:

We are the first to explore the test-time distribution shifts problem in federated foundation models for practical application scenarios alignment.

We introduce a new method, namely dual-personalizing 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-the-art 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 foundation models, the community has shifted towards embracing parameter-efficient methods [33], with the adapter family [14] being a notable exemplar. According to different architectures, methods in

the adapter family can be categorized into four types. The first one is prompt-based learning [18, 21], which is aimed at learning the continuous/soft prompt for discrete optimization. The second one is reparametrization-based methods [13, 9], achieving parameter efficiency by utilizing low-rank techniques to decompose the high-dimensional matrices. The third one is series Adapters [12], which introduce additional learnable modules in a sequential manner within specific sublayers. The last one is parallel Adapters [11], 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.

2.2 Federated Foundation Models

With the advent of foundation models, there has been a burgeoning interest [44, 41, 26, 5] 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 adapterbased parameter-efficient tuning (PEFT) methods with federated foundation models. Building upon this, a 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. In addition, the research presented in [15] proposed a method to annotate unlabeled client-side data by harnessing the prowess of large models to address data scarcity 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 a specific training paradigm for LoRA to achieve more effective personalization in visual model-heterogeneous scenarios. Diverging from these approaches, our 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 a 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 to studies [39, 8] in federated continual/incremental 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 [3, 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 m train and test dataset D m test , where m indexes a client. One data pair in datasets is denoted as ( x , y ) , where x is the input data and y is its corresponding label.

Client 1

Client 2

Client n

User:

In which year was the Falklands War?

Response:

1982

Task:

Open QA

User:

If  A man sitting close to the center of a room that is

filled with pictures, does this mean that "man sits"?

Response:

yes

Task:

Entailment

...

Server

A 1  B 1

A 2  B 2

...

A B

Global LoRA

User:

"What are Cushman and Wakefield known for?"

What kind of thing would answer this question?

Response:

description

Task:

Question Classification

Client to Server

Server to Client

Communication

Offline

A 1 , B 1

A, B

A, B

A 2 , B 2

A, B

A 3 , B 3

Trainable LoRA

A 1

B 1

A '

1

B '

1

Local

Global

Trainable LoRA

A 2

B 2

A '

2

B '

2

Local

Global

Trainable LoRA

A 3

B 3

A '

3

B '

3

Local

Global

̸

In each client m , we will introduce the training and testing phases separately for our test-time personalization setting. In the training phase, model training utilizes solely the local dataset D m train , which is derived from the distribution P s m . While in the testing phase, the test dataset D m test comprises two components: the test set D m s driven from the same distribution P s m as training data, and additional test sets D t m under data distribution shifts P s m ( x , y ) = P t m ( x , y ) . Therefore, the test dataset is D m test = D m s ∪ D t m , and we call these datasets D t m as test-time datasets. Unlike previous works[29, 16] in test-time FL concentrating on either feature-level shifts P s m ( x ) = P t m ( x ) or label shifts P s m ( y ) = P t m ( y ) , we investigate a more complex scenario where various distribution shifts, including semantic shifts, domain shifts and others, exist simultaneously. 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 of the model in each client should not only perform well on the test set D m s (refer to personalization) but also have comparable results on the test-time dataset D t m (refer to test-time performance). This objective is consistent with the practical scenarios, since users primarily focus on 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 is the primary focus, followed by optimizing test-time tasks. Our proposed method introduced in section 4 is designed to achieve personalization within FedFM while ensuring comparable results for test-time tasks, and its vital intuition is illustrated below.

Considering a foundation model, it comprises a main body f ( θ ) , which holds most of the parameters and processes input x to produce output features h = f ( x ; θ ) . Additionally, there is a tail g ( θ t ) that maps these features to the output space (e.g., vocabulary), resulting in the predicted result ˆ y = g ( h ; θ t ) . Typically, the focus in tuning and adaptations primarily lies on the main body f ( θ ) because the tail g ( θ t ) , usually a linear function, remains unchanged (frozen) during tuning [14].

Discordance between Personalization and Test-time Tasks. The key to addressing test-time distribution shifts lies in learning generic features universally applicable across disparate distributions [1]. That is, learning a foundation model f ( θ ) to satisfy P s ( f ( x ; θ ) , y ) = P t ( f ( x ; θ ) , y ) although

̸

̸

P s ( x , y ) = P t ( x , y ) . FL is a methodology designed to learn generic features across diverse non-IID data (different distributions) through aggregation algorithms [5, 29]. Therefore, we tailor FL training for addressing test-time tasks with the objective min θ L P all ( θ ) , where L P all represents the loss function designed for learning generic features towards all clients' distributions P all . However, personalization focuses on aligning the model with the specific distribution P s , which means learning a foundation model f ( θ ) with the objective min θ L P s ( θ ) , where L P s represents the loss function designed 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 test-time personalization setting in FedFM. Following a similar assumption from a Mixture of Experts [23], any test-time task (distributions) to a client can be approximated as a 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 to 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 Fed erated D ualP ersonalizing A dapter (FedDPA) system for each client, as shown in Fig 1. During training, a global adapter is employed to acquire generic features by FL training for test-time tasks (Sec. 4.1). Meanwhile, to address personalization, a local adapter is maintained locally to align with the client's specific distribution, and leverages generic knowledge from the global adapter for faster learning (Sec. 4.2). During the inference, the learned global and local adapters are dynamically combined using a weight generated by the instance-wise dynamic weighting mechanism for each input test instance, realizing test-time personalization (Sec. 4.3).

The Overall Objective. Considering the computation and communication cost of FedFM, we utilize the adapter-based PEFT methods, which only learn a small part of parameters ∆ θ while keeping most of the parameters θ frozen. Our proposed FedDPA is to learn the global adapter ∆ θ g and local adapters ∆ θ l m simultaneously across M client to realize test-time personalization,

where the first part is a standard personalized FL loss to find optimal personalized models by minimizing the sum of loss on local training tasks L ( x , y ) ∼ P s m ( . ) , and the second part is a constraint term to seek an optimal solution by minimizing the test-time loss L ( x , y ) ∼ P all ( . ) . Because we assume that the test-time task is unseen to a client but observed by other clients, the test-time loss can be estimated using the Empirical Risk Minimization of all client's training tasks min ∆ θ g ∑ M m =1 r m L m ( θ ; ∆ θ g ) , where P all denotes all distributions of tasks in all clients, r m denotes each client's weight for aggregation (e.g., in FedAvg, r m is the proportion of each client's data number to all clients' data number) and L m 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 a sequential manner (FedDPA-F with local adapter fine-tuning) or iterative manner (FedDPA-T with local adapter training) . Detailed algorithms of these two methods are in Appendix A.3.

Remark. To simplify the illustration, we use LLM as the backbone and adopt LoRA [13] as the adapter-based PEFT method in our framework. The overall framework is easy to adapt to other types of backbone and other adapter-based PEFT methods. LoRA decomposes the training weight into

a frozen weight θ , and a trainable weight derived by the multiplication of two low-rank weights ∆ θ = ∆ θ b ∆ θ a . 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 L m for all NLP tasks is a standard language modeling objective [4].

4.1 Generic Learning of Global Model

Addressing test-time distribution shifts requires the acquisition of generic knowledge that is applicable across various distributions [1]. The conventional federated learning process is 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 of a frozen LLM model f ( x ; θ ) with a global lightweight global adapter (LoRA) ∆ θ g = ∆ θ b g ∆ θ a g . This global adapter is used for aggregation by sending to the server. Notably, the server's role is limited to computing the aggregated adapter ∆ θ g , thus obviating the need for maintaining a large-scale model. Similar to the standard FL process, for each client m , the adapter weight ∆ θ g m is learned locally and sent to the server. Upon receipt of the adapter weights from all clients, the server employs FedAvg [24] to aggregate them and sends ∆ ¯ θ g back to each client as their initialized parameter in a new round. It can be formulated as:

Remark. Other federated algorithms like FedProx [20] can also be applied with LoRA tuning of this global model learning (in Appendix B.1). In this paper, we just take FedAvg as 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 specific distribution. We explore two methods as shown in Fig 2, 1) Learning sequentially: after global adapter training, the local adapter is 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-initialized from its last state, fine-tuned alongside the frozen global adapter, and maintained locally without communication.

h

h'

LLM

Global LoRA

B

A

Local LoRA

B

A

α

(b) Local LoRA Training

h

h'

LLM

Local LoRA

B

A

(a) Local LoRA Fine-tuning

Local LoRA Learning

Overall Learning Process

(a) FedDPA-F: global LoRA and local fine-tuning LoRA

(b) FedDPA-T: global LoRA and local training LoRA

Local LoRA Learning Process

Global LoRA Learning Process

All communication rounds

communication 1

communication 2

communication 3

......

To be more specific, a local adapter (LoRA) ∆ θ l = ∆ θ b l ∆ θ a l is introduced. Thus, each client contains three components: a frozen LLM θ , a global adapter (LoRA) ∆ θ g and a local adapter (LoRA) ∆ θ l m . As delineated in Fig 2 (a), for the first method, after global training, the local adapter is first initialized by the global adapter denoted as ∆ θ l m = ∆ θ g , 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 LLM, the frozen global adapter and the local adapter. The process entails an initial fusion of the outputs from

both the local and global adapters with a predefined weighting factor of α , followed by integration with the output of the LLM to yield the final result h ′ = θh + ((1 -α ) · ∆ θ g h + α · ∆ θ l m h ) . Therefore, the learning of the local adapter ∆ θ l m for these two methods can be unified as:

4.3 LLM-enhanced Instance-wise Dynamic Weighting Mechanism

As discussed in previous test-time FL methods [16], a dynamic combination of global components and personalized components can improve generalization while reducing the cost of hyper-parameter 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 to determine the appropriate weight balance for the global and local adapter combination. To facilitate this, the representation of each input instance is essential. Leveraging the robust capability of pre-trained LLMs to abstract input sentences, we utilize the hidden states from the final layer of the LLM as the representation. Given that the LLM is decoder-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 a client, we randomly sample S instances { x 0 , x 1 , ..., x s } from the local training dataset. These instances are then fed into the LLM, augmented with the global adapter, to obtain the last token's embeddings from the final layer, denoted as w x and { w x 0 , w x 1 , ..., w x s } respectively. Subsequently, we calculate the similarity between the input representation w x and each sampled local representation in { w x 0 , w x 1 , ..., w x s } , resulting in a score range of [0 , 1] . Finally, we average all scores to obtain the final result, represented as α t = λ · ∑ S i =0 1 S Sim( w x , w x i ) , where Sim represents the function to calculate the similarity, and λ is a scale factor in (0 , 1] to restrict the maximum similarity score (especially for FedDPA-T).

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 specific needs but also benefits from the aggregated model's generic knowledge across test-time tasks.

5 Experiment

5.1 Experiment Setting

Datasets. We construct two federated datasets from Flan [31], which is a 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.1. ROGUE-1 is taken as a metric.

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 clients.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 2 is adapted as the base model initialized with LLaMA-7B. 3 The updating weight of local LoRA training (FedDPA-T) is α = 0 . 5 ( λ = 0 . 5 ) for federated dataset 1 and α = 0 . 3 ( λ = 0 . 3 ) for federated dataset

2. We set S = 5 and choose cosine similarity for instance-wise dynamic weighting mechanism. More details are in Appendix A.2.

5.2 Main Results

We compare FedDPA with other baselines on two main evaluation facets: personalization (scores on targeted local tasks) and test-time personalization (average scores on all tasks including test-time tasks). As evidenced in Table 1 and Table 2, our proposed dual-personalizing adapter methods (both fine-tuning and training) exhibit superior performance in personalization compared to 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 as 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 a 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, it is noteworthy that while centralized or global models may yield higher average

performances across all tasks, they fall short in excelling at specific tasks for personalization, aligning with the conclusions of the previous study [30].

6 Analysis

6.1 Convergence Analysis

We present the convergence analysis of our methods in Figure 3. Figure 3 (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 a more rapid convergence compared to FedIT and achieve notable performance enhancements after five communication rounds. Despite sharing similar trends with FedLoRA, our approaches, particularly the FedDPA-T, ultimately outperform in personalization. For a 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.

(a)Personalization(Dataset1)

(b)TTP (Dataset 1)

79

78

74

Accuracy (%)

73

Accuracy (%)

69

63

68

58

53

48

FedLoRA

FedDPA-F

FedIT

FedDPA-T

49

44

64

59

54

FedDPA-T

FedDPA-F

FedLoRA

2

4

68101214161820

2

4

68101214161820

Communication Rounds

Communication Rounds

6.2 Ablation Study

Impact of Instance-Wise Dynamic Weighting Mechanism. To explore the impact of the instancewise dynamic weighting mechanism, we implemented experiments with FedDPA methods on different datasets. As shown in Table 3, the incorporation of an instance-wise dynamic weighting mechanism contributes significantly to enhancing performance in both personalization and test-time personalization scenarios. More ablation studies are in Appendix B.2.

Impact of Updating Weight α . In this study, we investigated the influence of the updating weight α during FedDPA-T training with its value α ∈ { 03 , 0 . 5 , 0 . 7 } . As can be seen in Table 4, for test-time personalization, increasing updating weight α will decrease the performance due to the increased proportion of the local adapter in the model, while for personalization, different updating weights α 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 in Appendix A.1). As shown in Figure 4, as the client participant rates increase, model accuracy also increases as more participating clients provide more data for knowledge learning. Besides,

(a)Personalization(Dataset 1)

(b) TTP (Dataset 1)

80

76

Accuracy (%)

78

Accuracy (%)

74

72

76

74

FedDPA-FFedDPA-T

FedIT

FedLoRA

68

72

70

FedDPA-T

FedLoRA

FedDPA-F

8

16

24

32

40

8

16

24

32

40

Client Number

Client Number

FedDPA-F outperforms all baselines, whereas FedDPA-T exhibits somewhat inferior performance, potentially due to 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 a promising direction to enhance existing Foundation Models, e.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 dualpersonalizing adapter for the FedFM framework. The method is 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 a 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, we have developed two federated datasets derived from the Flan [31], 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 a 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 a 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 5.

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 are provided in Table 6. For the input of the LLM, we adopted a simple template, the details of which are delineated in Table 7.

Dataset Partitioning for Ablation Study. In our ablation study in section 6.2 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 is associated with a single task, this division results in a total of 40 clients, with each client possessing a local dataset of 60 training examples. To mimic real-world FL communication dynamics, we employed a 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 a 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 personalization setting involve multiple tasks during training and testing, multi-task learning operates in a centralized setting, whereas our setting is based on federated learning, a distributed setting. More importantly, our setting accounts for test-time distribution shifts, a challenge that is typically overlooked in conventional multi-task learning. Additionally, fine-tuning on the combination of multi-task data from a centralized foundation model serves as a strong baseline for multi-task learning. In foundation models, all tasks are standardized into a uniform format, and the model benefits from task-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 [40]. We have included this baseline, referred to as "Centralized," in our experimental comparisons.

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 at a single centralized center for training purposes, with 50 epochs to optimize.

Local-finetuned model: This model trains independently without any external communication 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.

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 [38] 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 set to λ = 1 in the instance-wise dynamic weighting mechanism.

FedDPA-T: FedDPA-T is 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, it 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 λ is set equal to the updating weight α used in the local adapter training.

All models are implemented using LoRA to enhance learning efficiency, with the rank of LoRA set as r = 8 and only applied to W q and W v . For FL methods, each client conducts 10 local epochs

with a batch size of 32. We implement all the methods using PyTorch and conduct all experiments on NVIDIA Quadro RTX 6000 GPU.

A.3 Algorithm

Algorithm 1: FedDPA-F

B Additional Experiments

B.1 Adaptability Analysis

To enhance applicability across diverse non-IID environments, our method is meticulously designed with a high degree of flexibility for its adaption across various global learning frameworks, backbones 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-based foundation models and adapter-based PEFT methods during the training. In our experiment, we employ FedAvg, LLM and LoRA as representative examples, demonstrating our methods' superior performance compared to other baselines as indicated in Table 1 and Table 2. To

further validate the effectiveness and versatility of our approach within different federated learning contexts, we adapt our methods to include the FedProx[20] framework and also implement other baselines (FedIT and FedLoRA) within FedProx to maintain a fair comparison.

Results presented in Table 8 indicate that our methods, both training and fine-tuning methods, outperform competing approaches. Specifically, FedDPA-T excels in personalization, while FedDPAF 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-IID scenarios.

Table 8: Personalization and test-time personalization results of different models with FedProx framework on federated dataset 1. 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 CLS represents question classification task.

B.2 Instance-Wise Dynamic Weighting Mechanism Analysis

In this section, we further examine the impact of the instance-wise dynamic weighting mechanism, including the similarity metric, the selected local instance number and the type of instance representation.

Impact of Similarity Metric. In Section 4.3, we employ the similarity metric to calculate the average similarity scores of each input instance, which serves as the weight α t to dynamically balance the global and local adapters. For this purpose, cosine similarity is selected in our 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, we conducted an ablation study comparing other metrics like L2-norm and Pearson correlation, and the results in Table 9 demonstrate that cosine similarity outperforms other similarity metrics.

Impact of Instance Number S . 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 a series of experiments employing distinct local instance numbers, specifically S ∈ { 1 , 3 , 5 , 7 , 9 } . The accuracy results, as depicted in Figure 5, illustrate the dependency of model performance on different instance numbers S . As demonstrated in Figure 5 (a), in the context of personalization, it is observed that our models attain a plateau in accuracy when the instance number exceeds 5. This indicates a stabilization in model performance beyond this threshold of local instances. Furthermore, Figure 5 (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 Representation. In Section 4.3, our method entails utilizing the embedding of the final token from the last hidden layer of 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 as 'A VG'. The comparative analysis, as presented

(a) Personalization (Dataset 1)

(b) TTP (Dataset 1)

80.5

77.5

80

77

Accuracy (%)

79.5

76.5

79

76

FedDPA-T

FedDPA-F

A

78.5

FedDPA-T

75.5

FedDPA-F

78

75

1

3

5

7

9

1

3

5

7

9

in 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 performance can be attributed to the decoder structure inherent to LLMs, wherein the final token is capable of attending to all preceding tokens, thereby encapsulating comprehensive sentence-level information.

B.3 Model Scalability Analysis

In order to examine the effectiveness of model scalability, we conduct experiments based on a larger model, LLaMA-13B. The outcomes, as presented in Table 11, 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 10: Ablation study of instance representations (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 hidden layer of LLM as instance representation.

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 12, 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 a minimal communication overhead. Regarding the computation overhead, the LoRA architecture permits the training of both local and global LoRAs in parallel, resulting in a 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 13, it 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 a minimal impact on computational efficiency during inference.

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, we will discuss our methods under alternative scenarios.

In practical applications, it 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, 29], generic features learned through FL are robust to distribution shifts, even those originating from unseen test-time tasks. Consequently, our method can be directly adapted to this scenario. We conducted experiments to evaluate our methods against other baselines on three unseen test-time tasks. All methods and baselines are trained on our constructed Federated Dataset 1 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 a comprehensive comparison.

As shown in Table 14, FedDPA-T outperforms all other models, indicating the effectiveness of our method on 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 [29]. 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

then be used as the initial model for the new clients, providing a more effective starting point for further training.

C.2 Limitations

The proposed dual-personalizing adapter architecture is limited by 1) 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 to access and utilize data stored on them, and the whole process does not suffer from any attacks.

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Justification: Since our paper proposes a new framework for federated learning and the models/datasets we use are publicly available, there are no such risks.

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Answer: [Yes]

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Peer reviews:
comments: 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.

STRENGTHS:
- The paper is generally easy to read.
- 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. 

- 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?

- 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.
 
[1] Exploiting shared representations for personalized federated learning

[2] Perada: Parameter-efficient and generalizable federated learning personalization with guarantees

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
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 2
DATE: 2024-06-24

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS:\n- 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\n\nWEAKNESSES:\nOverall 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).\n\nQUESTIONS:\nSee Above\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS:\n1. 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.\n\nWEAKNESSES:\n1. 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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nFederated 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.\n\nSTRENGTHS:\n1) 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.\n\nWEAKNESSES:\n1) 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?\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-13"}