  Federated learning (FL) allows multiple clients to collaboratively train a
deep learning model. One major challenge of FL is when data distribution is
heterogeneous, i.e., differs from one client to another. Existing personalized
FL algorithms are only applicable to narrow cases, e.g., one or two data
classes per client, and therefore they do not satisfactorily address FL under
varying levels of data heterogeneity. In this paper, we propose a novel
framework, called DisTrans, to improve FL performance (i.e., model accuracy)
via train and test-time distributional transformations along with a
double-input-channel model structure. DisTrans works by optimizing
distributional offsets and models for each FL client to shift their data
distribution, and aggregates these offsets at the FL server to further improve
performance in case of distributional heterogeneity. Our evaluation on multiple
benchmark datasets shows that DisTrans outperforms state-of-the-art FL methods
and data augmentation methods under various settings and different degrees of
client distributional heterogeneity.
