Federating for Learning Group Fair Models

Understand

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.