  Federated learning is an increasingly popular paradigm that enables a large
number of entities to collaboratively learn better models. In this work, we
study minmax group fairness in paradigms where different participating entities
may only have access to a subset of the population groups during the training
phase. We formally analyze how this fairness objective differs from existing
federated learning fairness criteria that impose similar performance across
participants instead of demographic groups. We provide an optimization
algorithm -- FedMinMax -- for solving the proposed problem that provably enjoys
the performance guarantees of centralized learning algorithms. We
experimentally compare the proposed approach against other methods in terms of
group fairness in various federated learning setups.
