  Fair prediction across protected groups is an important constraint for many
federated learning applications. However, prior work studying group fair
federated learning lacks formal convergence or fairness guarantees. In this
work we propose a general framework for provably fair federated learning. In
particular, we explore and extend the notion of Bounded Group Loss as a
theoretically-grounded approach for group fairness. Using this setup, we
propose a scalable federated optimization method that optimizes the empirical
risk under a number of group fairness constraints. We provide convergence
guarantees for the method as well as fairness guarantees for the resulting
solution. Empirically, we evaluate our method across common benchmarks from
fair ML and federated learning, showing that it can provide both fairer and
more accurate predictions than baseline approaches.
