Achieving Fairness Across Local and Global Models in Federated Learning
This addresses fairness issues in federated learning for applications like healthcare, but it is incremental as it builds on existing methods with specific improvements.
The study tackled the challenge of achieving fairness across diverse clients in Federated Learning by introducing EquiFL, which enhanced both local and global fairness through a fairness term in local optimization and a coordination mechanism, demonstrating better balance between accuracy and fairness in experiments.
Achieving fairness across diverse clients in Federated Learning (FL) remains a significant challenge due to the heterogeneity of the data and the inaccessibility of sensitive attributes from clients' private datasets. This study addresses this issue by introducing \texttt{EquiFL}, a novel approach designed to enhance both local and global fairness in federated learning environments. \texttt{EquiFL} incorporates a fairness term into the local optimization objective, effectively balancing local performance and fairness. The proposed coordination mechanism also prevents bias from propagating across clients during the collaboration phase. Through extensive experiments across multiple benchmarks, we demonstrate that \texttt{EquiFL} not only strikes a better balance between accuracy and fairness locally at each client but also achieves global fairness. The results also indicate that \texttt{EquiFL} ensures uniform performance distribution among clients, thus contributing to performance fairness. Furthermore, we showcase the benefits of \texttt{EquiFL} in a real-world distributed dataset from a healthcare application, specifically in predicting the effects of treatments on patients across various hospital locations.