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.

翻译：联邦学习是一种日益流行的模式,它使许多实体能够合作学习更好的模式。在这项工作中,我们研究了不同参与实体在培训阶段可能只能接触一部分人口群体的模式中的最小群体公平性。我们正式分析了这种公平性目标如何不同于现有的联邦学习公平性标准,这些标准对参与者而不是人口群体规定了类似的业绩。我们提供了一种优化算法 -- -- FedMinmax -- -- 以解决拟议中的问题,因为后者可以明显地享有集中学习算法的绩效保障。我们实验性地比较了拟议方法,在各种联邦学习组合中,在群体公平性方面与其他方法进行比较。