  Personalized Federated Learning (FL) algorithms collaboratively train
customized models for each client, enhancing the accuracy of the learned models
on the client's local data (e.g., by clustering similar clients, or by
fine-tuning models locally). In this paper, we investigate the impact of such
personalization techniques on the group fairness of the learned models, and
show that personalization can also lead to improved (local) fairness as an
unintended benefit. We begin by illustrating these benefits of personalization
through numerical experiments comparing two classes of personalized FL
algorithms (clustering and fine-tuning) against a baseline FedAvg algorithm,
elaborating on the reasons behind improved fairness using personalized FL, and
then providing analytical support. Motivated by these, we further propose a
new, Fairness-aware Federated Clustering Algorithm, Fair-FCA, in which clients
can be clustered to obtain a (tuneable) fairness-accuracy tradeoff. Through
numerical experiments, we demonstrate the ability of Fair-FCA to strike a
balance between accuracy and fairness at the client level.
