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Federated Learning enables visual models to be trained in aprivacy-preserving way using real-world data from mobile devices. Given theirdistributed nature, the statistics of the data across these devices is likelyto differ significantly. In this work, we look at the effect such non-identicaldata distributions has on visual classification via Federated Learning. Wepropose a way to synthesize datasets with a continuous range of identicalnessand provide performance measures for the Federated Averaging algorithm. We showthat performance degrades as distributions differ more, and propose amitigation strategy via server momentum. Experiments on CIFAR-10 demonstrateimproved classification performance over a range of non-identicalness, withclassification accuracy improved from 30.1% to 76.9% in the most skewedsettings.

Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification