Federated learning with multiple, intersectional and multiclass fairness guarantees under performance budgets
TL;DR
FedFairLAB is introduced, a FL method that enforces group, intersectional, and multiclass fairness simultaneously at both the local and global levels and a tunable performance budget allows practitioners to control how much predictive performance can be sacrificed to improve fairness.
Abstract
Machine learning increasingly drives decisions in domains such as finance and healthcare, where ethical considerations, such as fairness, are central. In such contexts, ensuring fairness is essential, especially when decisions impact individuals and social groups. Federated learning (FL) provides a decentralized training paradigm, yet client heterogeneity and demographic imbalance can amplify disparities across subpopulations. Existing fairness-aware FL methods remain limited, often focusing on group fairness in binary classification and lacking explicit control over the trade-off between fairness and predictive performance. We introduce FedFairLAB, a FL method that enforces group, intersectional, and multiclass fairness simultaneously at both the local and global levels. A tunable performance budget allows practitioners to control how much predictive performance can be sacrificed to improve fairness. Experiments on six real-world datasets show that FedFairLAB substantially improves fairness while keeping models accurate and usable in realistic FL settings.
