  Federated Learning enables visual models to be trained in a
privacy-preserving way using real-world data from mobile devices. Given their
distributed nature, the statistics of the data across these devices is likely
to differ significantly. In this work, we look at the effect such non-identical
data distributions has on visual classification via Federated Learning. We
propose a way to synthesize datasets with a continuous range of identicalness
and provide performance measures for the Federated Averaging algorithm. We show
that performance degrades as distributions differ more, and propose a
mitigation strategy via server momentum. Experiments on CIFAR-10 demonstrate
improved classification performance over a range of non-identicalness, with
classification accuracy improved from 30.1% to 76.9% in the most skewed
settings.
