Federated Learning (FL) has emerged as a means of distributed learning using local data stored at clients with a coordinating server. Recent studies showed that FL can suffer from poor performance and slower convergence when training data at clients are not independent and identically distributed. Here we consider a new complementary approach to mitigating this performance degradation by allowing the server to perform auxiliary learning from a small dataset. Our analysis and experiments show that this new approach can achieve significant improvements in both model accuracy and convergence time even when the server dataset is small and its distribution differs from that of the aggregated data from all clients.

翻译：联邦学习（FL）是一种利用客户端本地存储数据并通过协调服务器进行分布式学习的方法。近期研究表明，当客户端训练数据不满足独立同分布条件时，联邦学习的性能可能下降且收敛速度变慢。本文提出一种新的互补方法，通过允许服务器利用小规模数据集进行辅助学习来缓解性能退化。分析与实验表明，即使服务器数据集规模较小且其分布与所有客户端聚合数据的分布存在差异，该方法仍能在模型准确率和收敛时间方面实现显著提升。