  Federated learning (FL) has emerged as a prospective solution for
collaboratively learning a shared model across clients without sacrificing
their data privacy. However, the federated learned model tends to be biased
against certain demographic groups (e.g., racial and gender groups) due to the
inherent FL properties, such as data heterogeneity and party selection. Unlike
centralized learning, mitigating bias in FL is particularly challenging as
private training datasets and their sensitive attributes are typically not
directly accessible. Most prior research in this field only focuses on global
fairness while overlooking the local fairness of individual clients. Moreover,
existing methods often require sensitive information about the client's local
datasets to be shared, which is not desirable. To address these issues, we
propose GLOCALFAIR, a client-server co-design fairness framework that can
jointly improve global and local group fairness in FL without the need for
sensitive statistics about the client's private datasets. Specifically, we
utilize constrained optimization to enforce local fairness on the client side
and adopt a fairness-aware clustering-based aggregation on the server to
further ensure the global model fairness across different sensitive groups
while maintaining high utility. Experiments on two image datasets and one
tabular dataset with various state-of-the-art fairness baselines show that
GLOCALFAIR can achieve enhanced fairness under both global and local data
distributions while maintaining a good level of utility and client fairness.
