  Limited training data and severe class imbalance pose significant challenges
to developing clinically robust deep learning models. Federated learning (FL)
addresses the former by enabling different medical clients to collaboratively
train a deep model without sharing privacy-sensitive data. However, class
imbalance worsens due to variation in inter-client class distribution. We
propose federated learning with non-parametric regularization (FedNPR and
FedNPR-Per, a personalized version of FedNPR) to regularize the feature
extractor and enhance useful and discriminative signal in the feature space.
Our extensive experiments show that FedNPR outperform the existing state-of-the
art FL approaches in class imbalance skin lesion classification and
intracranial hemorrhage identification. Additionally, the non-parametric
regularization module consistently improves the performance of existing
state-of-the-art FL approaches. We believe that NPR is a valuable tool in FL
under clinical settings.
