We study the recent emerging personalized federated learning (PFL) that aims at dealing with the challenging problem of Non-I.I.D. data in the federated learning (FL) setting. The key difference between PFL and conventional FL lies in the training target, of which the personalized models in PFL usually pursue a trade-off between personalization (i.e., usually from local models) and generalization (i.e., usually from the global model) on trained models. Conventional FL methods can hardly meet this target because of their both well-developed global and local models. The prevalent PFL approaches usually maintain a global model to guide the training process of local models and transfer a proper degree of generalization to them. However, the sole global model can only provide one direction of generalization and may even transfer negative effects to some local models when rich statistical diversity exists across multiple local datasets. Based on our observation, most real or synthetic data distributions usually tend to be clustered to some degree, of which we argue different directions of generalization can facilitate the PFL. In this paper, we propose a novel concept called clustered generalization to handle the challenge of statistical heterogeneity in FL. Specifically, we maintain multiple global (generalized) models in the server to associate with the corresponding amount of local model clusters in clients, and further formulate the PFL as a bi-level optimization problem that can be solved efficiently and robustly. We also conduct detailed theoretical analysis and provide the convergence guarantee for the smooth non-convex objectives. Experimental results on both synthetic and real datasets show that our approach surpasses the state-of-the-art by a significant margin.

本文提出了一种名为上下文化泛化（CG）的新概念，旨在为每个客户端提供更好地适合本地数据分布和更快模型收敛的细粒度上下文知识。作者还提出了CGPFL框架，在多个现实世界的数据集上进行了实验，证明了该方法在测试精度上显著优于最先进的方法。

个性化联合学习与背景化泛化