Federated Learning (FL) has emerged as a means of performing distributed learning using local data stored at clients and a coordinating server. But, recent studies showed that FL can suffer from poor performance when client training data are not independently and identically distributed (non-IID). This paper proposes a new complementary approach to mitigating this performance degradation when the server has access to a small dataset, on which it can perform auxiliary learning. Our analysis and experiments show that incorporating server learning with FL in an incremental fashion can provide significant benefits when the distribution of server data is similar to that of the aggregate samples of all clients, even when the server dataset is small, and improve the convergence rate considerably at the beginning of the learning process.

翻译：联邦学习(FL)已成为利用客户和协作服务器存储的当地数据进行分布式学习的一种手段,但最近的研究表明,如果客户培训数据不是独立和同样分布的(非二维)客户培训数据(非二维),那么FL可能表现不佳。本文件建议采取新的补充办法,在服务器能够进入一个小数据集并进行辅助学习时,减少这种性能退化。我们的分析和实验表明,如果服务器数据的分配与所有客户的汇总样本相似,即使服务器数据集很小,将服务器与FL的学习逐步结合起来,那么,如果服务器数据的分配与所有客户的汇总样本相似,即使服务器数据集很小,并且在学习过程开始时大大提高了趋同率,那么将服务器与FL的学习逐步纳入可以带来巨大的好处。