Federated learning (FL) is a decentralized machine learning technique that enables multiple clients to collaboratively train models without requiring clients to reveal their raw data to each other. Although traditional FL trains a single global model with average performance among clients, statistical data heterogeneity across clients has resulted in the development of personalized FL (PFL), which trains personalized models with good performance on each client's data. A key challenge with PFL is how to facilitate clients with similar data to collaborate more in a situation where each client has data from complex distribution and cannot determine one another's distribution. In this paper, we propose a new PFL method (pFedMB) using multi-branch architecture, which achieves personalization by splitting each layer of a neural network into multiple branches and assigning client-specific weights to each branch. We also design an aggregation method to improve the communication efficiency and the model performance, with which each branch is globally updated with weighted averaging by client-specific weights assigned to the branch. pFedMB is simple but effective in facilitating each client to share knowledge with similar clients by adjusting the weights assigned to each branch. We experimentally show that pFedMB performs better than the state-of-the-art PFL methods using the CIFAR10 and CIFAR100 datasets.

翻译：联邦学习（FL）是一种去中心化机器学习技术，允许多个客户端在不暴露原始数据的情况下协同训练模型。传统FL训练的是在客户端间具有平均性能的单一全局模型，但客户端间统计数据的异质性催生了个性化联邦学习（PFL）的发展——该技术针对每个客户端的数据训练具有良好性能的个性化模型。PFL面临的关键挑战在于：当每个客户端都拥有复杂分布的数据且无法获知其他客户端的数据分布时，如何促进相似数据的客户端进行更深入协作。本文提出一种采用多分支架构的新型PFL方法（pFedMB），通过将神经网络的每层拆分为多个分支并为每个分支分配客户端特定权重实现个性化。我们还设计了一种聚合方法，通过客户端分配给各分支的权重对分支进行加权平均全局更新，从而提升通信效率与模型性能。pFedMB通过调整各分支的权重分配，能够简洁而有效地促进客户端与相似客户端进行知识共享。在CIFAR10和CIFAR100数据集上的实验表明，pFedMB的性能优于当前最先进的PFL方法。