Current approaches to group fairness in federated learning assume the
existence of predefined and labeled sensitive groups during training. However,
due to factors ranging from emerging regulations to dynamics and
location-dependency of protected groups, this assumption may be unsuitable in
many real-world scenarios. In this work, we propose a new approach to guarantee
group fairness that does not rely on any predefined definition of sensitive
groups or additional labels. Our objective allows the federation to learn a
Pareto efficient global model ensuring worst-case group fairness and it
enables, via a single hyper-parameter, trade-offs between fairness and utility,
subject only to a group size constraint. This implies that any sufficiently
large subset of the population is guaranteed to receive at least a minimum
level of utility performance from the model. The proposed objective encompasses
existing approaches as special cases, such as empirical risk minimization and
subgroup robustness objectives from centralized machine learning. We provide an
algorithm to solve this problem in federation that enjoys convergence and
excess risk guarantees. Our empirical results indicate that the proposed
approach can effectively improve the worst-performing group that may be present
without unnecessarily hurting the average performance, exhibits superior or
comparable performance to relevant baselines, and achieves a large set of
solutions with different fairness-utility trade-offs.

通过单一超参数在一组大小约束下，在不依赖事先定义敏感群组或额外标签的情况下，确保联邦学习的团体公平性，同时实现公平与效用之间的权衡。