  Federated Learning (FL) enables data owners to train a shared global model
without sharing their private data. Unfortunately, FL is susceptible to an
intrinsic fairness issue: due to heterogeneity in clients' data distributions,
the final trained model can give disproportionate advantages across the
participating clients. In this work, we present Equal and Equitable Federated
Learning (E2FL) to produce fair federated learning models by preserving two
main fairness properties, equity and equality, concurrently. We validate the
efficiency and fairness of E2FL in different real-world FL applications, and
show that E2FL outperforms existing baselines in terms of the resulting
efficiency, fairness of different groups, and fairness among all individual
clients.
