  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.
