  Federated learning (FL) can help promote data privacy by training a shared
model in a de-centralized manner on the physical devices of clients. In the
presence of highly heterogeneous distributions of local data, personalized FL
strategy seeks to mitigate the potential client drift. In this paper, we
present the group personalization approach for applications of FL in which
there exist inherent partitions among clients that are significantly distinct.
In our method, the global FL model is fine-tuned through another FL training
process over each homogeneous group of clients, after which each group-specific
FL model is further adapted and personalized for any client. The proposed
method can be well interpreted from a Bayesian hierarchical modeling
perspective. With experiments on two real-world datasets, we demonstrate this
approach can achieve superior personalization performance than other FL
counterparts.
