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

Tzu-Ming Harry Hsu, Hang Qi, Matthew Brown

Abstract:

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