  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.
