  Federated learning has emerged as an important distributed learning paradigm,
where a server aggregates a global model from many client-trained models while
having no access to the client data. Although it is recognized that statistical
heterogeneity of the client local data yields slower global model convergence,
it is less commonly recognized that it also yields a biased federated global
model with a high variance of accuracy across clients. In this work, we aim to
provide federated learning schemes with improved fairness. To tackle this
challenge, we propose a novel federated learning system that employs zero-shot
data augmentation on under-represented data to mitigate statistical
heterogeneity and encourage more uniform accuracy performance across clients in
federated networks. We study two variants of this scheme, Fed-ZDAC (federated
learning with zero-shot data augmentation at the clients) and Fed-ZDAS
(federated learning with zero-shot data augmentation at the server). Empirical
results on a suite of datasets demonstrate the effectiveness of our methods on
simultaneously improving the test accuracy and fairness.
