Regularized Mutual Learning for Personalized Federated Learning

Ruihong Yang (Southern University of Science and Technology)*; Junchao Tian (Southern University of Science and Technology); Yu Zhang (Southern University of Science and Technology)


Federated Learning (FL) is a privacy-protected learning paradigm, which allows many clients to jointly train a model under the coordination of a server without the local data leakage. In real-world scenarios, data in different clients usually cannot satisfy the independent and identically distributed (i.i.d.) assumption adopted widely in machine learning. Traditionally training a single global model may cause performance degradation and difficulty in ensuring convergence in such a non-i.i.d. case. To handle this case, various models can be trained for each client to capture the personalization in each client. In this paper, we propose a new personalized FL framework, called Personalized Federated Mutual Learning (PFML), to use the non-i.i.d. characteristics to generate specific models for clients. Specifically, the PFML method integrates mutual learning into the local update process in each client to not only improve the performance of both the global and personalized models but also speed up the convergence compared with state-of-the-art methods. Moreover, the proposed PFML method can help maintain the heterogeneity of client models and protect the information of personalized models. Experiments on benchmark datasets show the effectiveness of the proposed PFML model.