
This paper proposes AAggFF, a novel framework leveraging online convex optimization (OCO) to improve client-level fairness in federated learning by adaptively adjusting mixing coefficients based on client performance feedback.


coremsg

adaptive-aggregation-for-fair-federated-learning-achieving-client-level-fairness-through-sequential-decision-making


Adaptive Aggregation for Fair Federated Learning: Achieving Client-Level Fairness through Sequential Decision Making


title_rewrite


WassFFed is a novel framework that enhances fairness in federated learning by minimizing discrepancies in model outputs for different sensitive groups across clients using Wasserstein barycenters and optimal transport.


wassffed-achieving-fairness-in-federated-learning-using-wasserstein-barycenters


WassFFed: Achieving Fairness in Federated Learning using Wasserstein Barycenters



FedMABA is a novel federated learning algorithm that leverages multi-armed bandits to improve fairness by explicitly constraining performance disparities among clients with diverse data distributions, without compromising the server model's performance.


fedmaba-using-multi-armed-bandits-to-improve-fairness-in-federated-learning


FedMABA: Using Multi-Armed Bandits to Improve Fairness in Federated Learning
