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Información - Fair Federated Learning
Adaptive Aggregation for Fair Federated Learning: Achieving Client-Level Fairness through Sequential Decision Making
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.
WassFFed: Achieving Fairness in Federated Learning using Wasserstein Barycenters
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.
FedMABA: Using Multi-Armed Bandits to Improve Fairness in Federated Learning
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.
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