Federated Continual Learning with Adaptive Differential Privacy and Client-Side Drift Detection for Evolving Medical Imaging Datasets

This paper presents a novel Federated Continual Learning framework for medical imaging that enables continuous model updates across multiple hospitals without central data sharing. Our approach addresses critical challenges in healthcare AI: data privacy, catastrophic forgetting, and distribution drift. We demonstrate the framework on OrganAMNIST with three hospitals learning sequential tasks. Results show successful federated collaboration but reveal significant catastrophic forgetting, highlighting the need for advanced continual learning techniques in privacy-constrained environments. The integrated drift detection and differential privacy mechanisms provide a foundation for practical clinical deployment.