FedKPer: Tackling Generalization and Personalization in Medical Federated Learning via Knowledge Personalization
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. While prior work has largely treated generalization and personalization as separate challenges, we show that a better balance between the two can be achieved through selective alignment with the global ...
| Authors | Zoe Fowler, Ghassan AlRegib |
|---|---|
| Primary category | eess.IV |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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