Personalized federated learning is aimed at allowing numerous clients to
train personalized models while participating in collaborative training in a
communication-efficient manner without exchanging private data. However, many
personalized federated learning algorithms assume that clients have the same
neural network architecture, and those for heterogeneous models remain
understudied. In this study, we propose a novel personalized federated learning
method called federated classifier averaging (FedClassAvg). Deep neural
networks for supervised learning tasks consist of feature extractor and
classifier layers. FedClassAvg aggregates classifier weights as an agreement on
decision boundaries on feature spaces so that clients with not independently
and identically distributed (non-iid) data can learn about scarce labels. In
addition, local feature representation learning is applied to stabilize the
decision boundaries and improve the local feature extraction capabilities for
clients. While the existing methods require the collection of auxiliary data or
model weights to generate a counterpart, FedClassAvg only requires clients to
communicate with a couple of fully connected layers, which is highly
communication-efficient. Moreover, FedClassAvg does not require extra
optimization problems such as knowledge transfer, which requires intensive
computation overhead. We evaluated FedClassAvg through extensive experiments
and demonstrated it outperforms the current state-of-the-art algorithms on
heterogeneous personalized federated learning tasks.

本研究提出一种名为联邦分类器平均（FedClassAvg）的个性化联邦学习方法，通过聚合分类器权重以增强特征空间的决策边界来使得有不同神经网络架构、实现非独立同分布数据（non-iid）学习任务的客户端可以学习稀缺标签，同时应用本地特征表示学习来稳定决策边界并利用局部特征提取能力提升客户端的表现，相较于已有方法仅要求客户端通信少量全连接层，因此高度通信效率，而且不需要额外的优化问题和计算开销，且在异构性个性化联邦学习任务方面表现优于现有最先进算法。