FedKPer: Tackling Generalization and Personalization in Medical Federated Learning via Knowledge Personalization

ArXi:2605.00698v1 Announce Type: cross Federated learning (FL) holds great potential for medical applications. However, statistical heterogeneity across healthcare institutions poses a major challenge for FL, as the global model struggles both to generalize across unseen patient populations and to adapt to the unique data distributions of individual hospitals. This heterogeneity also exacerbates forgetting at both the global and local level, resulting in previous learned patient patterns to be misclassified after model updates.