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.

通过引入 EquiFL，增强联邦学习环境中的本地和全局公平性，同时确保准确性和公平性之间的良好平衡，并通过协调机制防止偏见在合作阶段间的传播。在多个基准测试中进行了广泛的实验证明 EquiFL 不仅在每个客户端实现了准确性和公平性之间更好的平衡，还实现了全局公平性，并确保了客户端之间的性能公平分布。此外，我们还展示了 EquiFL 在一个真实的分布式医疗应用数据集中的好处，特别是在预测各个医院地点对患者治疗效果方面。