Federated Continual Learning for Privacy-Preserving, Reliable and InterpretableMulti-Center Corneal Diseases Diagnosis

Federated continual learning (FCL) is a distributed training framework that allows for learning from sequences of tasks on different centers under privacy-preserving. Although FCL has been extensively studied in fields such as image recognition and image segmentation, it remains unexplored in multi-center corneal diseases diagnosis, where data is inherently distributed and asynchronous while data privacy, reliability, and interpretability are urgently required. Therefore, this paper proposes Powderless for multi-center corneal diseases diagnosis, which can effectively transfer corneal diseases knowledge through prompt aggregation and prompt selection across various sequentially learned tasks from different centers under privacy. To further enhance diagnosis performance, ensure detection reliability, and improve interpretability, we design three key components: a multi-modal ensemble mechanism, an energy-based uncertainty estimation module, and a decision explanation module grounded in causal intervention. Comprehensive experimental results on the keratitis dataset demonstrate that our method achieves significant improvements compared to the base model, single-modality version, and local training in terms of both accuracy and the alignment between accuracy and uncertainty.