Federated learning (FL) is a decentralized and privacy-preserving machine learning technique in which a group of clients collaborate with a server to learn a global model without sharing clients' data. One challenge associated with FL is statistical diversity among clients, which restricts the global model from delivering good performance on each client's task. To address this, we propose an algorithm for personalized FL (pFedMe) using Moreau envelopes as clients' regularized loss functions, which help decouple personalized model optimization from the global model learning in a bi-level problem stylized for personalized FL. Theoretically, we show that pFedMe's convergence rate is state-of-the-art: achieving quadratic speedup for strongly convex and sublinear speedup of order 2/3 for smooth nonconvex objectives. Experimentally, we verify that pFedMe excels at empirical performance compared with the vanilla FedAvg and Per-FedAvg, a meta-learning based personalized FL algorithm.

该研究提出了一种基于Moreau envelopes的个性化联邦学习算法(pFedMe)，用于解决客户端之间的统计差异性问题并实现个性化模型优化和全局模型学习的分离，该算法在理论上已证明具有先进的收敛速度，在实验中也证明在表现上优于vanilla FedAvg和Per-FedAvg等算法。

个性化的Moreau信封联邦学习