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.

本研究针对联邦学习中客户端数据分布存在异质性的问题，该问题导致模型在某些客户端的数据上表现不佳。研究提出了一种个性化微调的方法，分析了对抗性客户端对全合作性能的影响，结果表明通过减少合作水平可显著提高个性化模型的表现，尤其是在存在对抗性客户端的情况下。

联邦学习中的个性化微调以减轻对抗性客户端的影响