  Data heterogeneity across clients in federated learning (FL) settings is a
widely acknowledged challenge. In response, personalized federated learning
(PFL) emerged as a framework to curate local models for clients' tasks. In PFL,
a common strategy is to develop local and global models jointly - the global
model (for generalization) informs the local models, and the local models (for
personalization) are aggregated to update the global model. A key observation
is that if we can improve the generalization ability of local models, then we
can improve the generalization of global models, which in turn builds better
personalized models. In this work, we consider class imbalance, an overlooked
type of data heterogeneity, in the classification setting. We propose FedNH, a
novel method that improves the local models' performance for both
personalization and generalization by combining the uniformity and semantics of
class prototypes. FedNH initially distributes class prototypes uniformly in the
latent space and smoothly infuses the class semantics into class prototypes. We
show that imposing uniformity helps to combat prototype collapse while infusing
class semantics improves local models. Extensive experiments were conducted on
popular classification datasets under the cross-device setting. Our results
demonstrate the effectiveness and stability of our method over recent works.
