A Study of Enhancing Federated Learning on Non-IID Data with Server Learning.

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 the clients are not independent and identically distributed (IID). Here, we consider auxiliary server learning as a approach to improving the performance of FL on non-IID data.