  In this paper, we identify a new phenomenon called activation-divergence
which occurs in Federated Learning (FL) due to data heterogeneity (i.e., data
being non-IID) across multiple users. Specifically, we argue that the
activation vectors in FL can diverge, even if subsets of users share a few
common classes with data residing on different devices. To address the
activation-divergence issue, we introduce a prior based on the principle of
maximum entropy; this prior assumes minimal information about the per-device
activation vectors and aims at making the activation vectors of same classes as
similar as possible across multiple devices. Our results show that, for both
IID and non-IID settings, our proposed approach results in better accuracy (due
to the significantly more similar activation vectors across multiple devices),
and is more communication-efficient than state-of-the-art approaches in FL.
Finally, we illustrate the effectiveness of our approach on a few common
benchmarks and two large medical datasets.
