Federated Fairness without Access to Sensitive Groups
Afroditi Papadaki Natalia Martinez Martin Bertran Guillermo Sapiro Miguel Rodrigues
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.