Learning from history for personalized federated learning.

Abstract

Personalized Federated Learning (pFL) has received extensive attentions, due to its ability to effectively process non-IID data distributed among different clients. However, most of the existing pFL methods focus on the collaboration between global and local models to enrich the personalization process, but ignoring a lot of valuable historical information, which represents the unique learning trajectory of each client. In this paper, we propose a pFL method called FedLFH, which introduces a tracking variable that allows each client to preserve historical information to facilitate personalization. We set up a global feature extractor and a personalized feature extractor for each client, to achieve the effective transfer of knowledge between the global model and the personalized model integrated with historical information. To evaluate the effectiveness, we set up exhaustive experiments on various benchmark datasets. The results show that our method outperforms twelve state-of-the-art methods with different experimental settings.