Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients

TL;DR

This paper investigates how to effectively personalize federated learning models in the presence of adversarial clients, analyzing when full collaboration harms performance and proposing strategies to mitigate such issues.

Contribution

It provides a theoretical analysis of personalized federated learning with adversarial clients and offers guidelines for adjusting collaboration levels based on data heterogeneity and adversarial fraction.

Findings

Full collaboration can be worse than personalization under certain adversarial conditions.

Scaling down collaboration improves model robustness against adversarial clients.

Empirical results validate the theoretical analysis on synthetic and real datasets.

Abstract

Federated learning (FL) is an appealing paradigm that allows a group of machines (a.k.a. clients) to learn collectively while keeping their data local. However, due to the heterogeneity between the clients' data distributions, the model obtained through the use of FL algorithms may perform poorly on some client's data. Personalization addresses this issue by enabling each client to have a different model tailored to their own data while simultaneously benefiting from the other clients' data. We consider an FL setting where some clients can be adversarial, and we derive conditions under which full collaboration fails. Specifically, we analyze the generalization performance of an interpolated personalized FL framework in the presence of adversarial clients, and we precisely characterize situations when full collaboration performs strictly worse than fine-tuned personalization. Our analysis determines how much we should scale down the level of collaboration, according to data heterogeneity and the tolerable fraction of adversarial clients. We support our findings with empirical results on mean estimation and binary classification problems, considering synthetic and benchmark image classification datasets.