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数据进行分布式学习，并通过协调服务器进行协作的方法。最新研究表明，当客户端训练数据非独立同分布时，联邦学习可能出现性能下降和收敛速度变慢的问题。本文提出一种互补性新方法，允许服务器利用少量数据集进行辅助学习，以缓解性能退化。我们的分析与实验表明，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，该新方法也能显著提升模型精度并缩短收敛时间。