Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients

Summary

This paper proposes a fundamental validation to understand the relationship between local models and the global model to mitigate the impact of adversarial clients. The level of collaboration needs to be chosen carefully because of the existence of adversarial clients. The theoretical analysis is provided to validate the statement, considering data heterogeneity, the fraction of adversarial clients, and data scarcity. Several simulated and open-source datasets are used to further demonstrate the effectiveness of the method.

Strengths

This paper proposes a simple yet easily applicable method to mitigate Byzantine adversaries in personalized FL, supported by thorough theoretical proof.

Weaknesses

1. Some typos, such as the missing space behind "Section 2" in Line 112. 2. Although the theoretical proof is thorough, more experiments on different datasets, Byzantine attack methods, and defense methods should be evaluated. 3. For the simulated datasets in Section 2.2, cross-device (n=600/f=100) is employed, but the experimental validation in Section 3.3 uses cross-silo (n=20/f=0,3,6,9). 4. The models used for each dataset are not mentioned. In summary, although theoretical proof is provided, the practical applicability of the method has not been sufficiently demonstrated.

Questions

Refer to Weaknesses.

Rating

5

Confidence

3

Soundness

3

Presentation

2

Contribution

3

Limitations

yes

Summary

This paper studies fine-tuning personalization in federated learning (FL) to mitigate the impact of adversarial clients. The authors leverage interpolation techniques for personalization, and they derive the closed-form approximation of the interpolation parameter $\lambda$. The study comprehensively considers both data heterogeneity and the presence of adversarial clients in the context of tailoring personalized FL.

Strengths

1. The authors consider that fine-tuning personalization in FL can mitigate the impact of adversarial clients, which extends existing Byzantine adversaries in FL. 2. They derive the closed -form approximation of the interpolation parameter $\lambda$, which can guide the fine-tuning procedure. 3. The theoretical analysis is comprehensive.

Weaknesses

1. The proposed fine-tuning personalization strategy requires each client should broadcast its model. Besides, each client should send the gradients to other clients. This is not efficient and may incur other privacy issues. 2. The prediction tasks in this paper are simple. Other issues: 1. Then then --> Then the in 142.

Questions

1. For more convincing, the authors should consider other more complicated datasets. 2. The suggested fine-tuning strategy for personalization necessitates that each client share its gradients with others, which could potentially raise privacy concerns. Moreover, clients might be able to identify adversarial clients through the gradients accumulated during communication.

Rating

5

Confidence

3

Soundness

3

Presentation

3

Contribution

2

Limitations

N/A.

Summary

This paper considers an FL setting where some clients can be adversarial, and we derive conditions under which full collaboration fails. Specifically, they analyze the generalization performance of an interpolated personalized FL framework in the presence of adversarial clients. The authors claim that they precisely characterize situations when full collaboration performs strictly worse than fine-tuned personalization.

Strengths

The idea is intuitive and easy to understand. With the presence of adversarial, less collaboration should work better. In addition, this paper proposed a new formulation for personalized FL, combining local loss and global loss.

Weaknesses

Section 2 doesn't make sense to me. Proposition 2 characterizes the difference between two variables, a local variable $\mu_i$, and a variable depending on collaboration (y^{\lambda}). This manuscript only considers deterministic cases. The assumption is strong, such as assumptions 2 and 4. The bound shown in the analysis is loose and the conclusion does not convince me.

Questions

In equation 9, when there is less data heterogeneity, \Psi() ->0 and G ->0, $lambda$ -> 1. Why do we need to collaborate when the data is homogeneous? I think we can train locally to avoid the adversary. Is assumption 3 necessary? Is it repetitive with Assumption 1? Is the model only effective for binary classification problems?

Rating

5

Confidence

3

Soundness

2

Presentation

3

Contribution

2

Limitations

Experiments are a bit simple. Assumptions are strong.

Summary

This paper presents theoretical analysis and experimental validation results of the allowed level of collaboration in personalized FL with the presence of a fraction of Byzantine adversaries.

Strengths

+ This paper targets a very important and challenging problem in the personalized FL settings. + The theoretical analysis and results are analytically rigorous and thorough. + The experimental validation results are also comprehensive and well complement with the theoretical analysis. + The results correlating the allowed level of collaboration and the tolerable fraction of adversaries are particularly appreciated.

Weaknesses

- The experimental validations can still be further improved from multiple aspects. For example, it may not be very convincing by using simulated datasets being generated by simple 1D sampling. The data heterogeneity settings should be more complicated and practical accordingly. More complicated models should also be used. - The analysis results only apply to the simple problem of binary classification. What's its generalizability to more practical multi-class classification?

Questions

See weakness above.

Rating

7

Confidence

3

Soundness

3

Presentation

3

Contribution

3

Limitations

See weakness above.

Rating

Given the authors’ response, I will maintain my rating.