FedCapD: Federated class-incremental learning via capsule distillation and diffusion replay

Federated Class-Incremental Learning faces critical challenges including catastrophic forgetting, semantic drift, and privacy risks under non-IID data distributions. To address these, we propose FedCapD, a novel framework that unifies unsupervised task boundary detection via Bayesian nonparametric modeling, hierarchical semantic distillation through capsule alignment and GNN-based class propagation, secure gradient communication using homomorphic encryption and differential privacy, and diffusion-based generative replay for memory-efficient adaptation. By integrating structural reasoning with privacy-preserving learning, FedCapD achieves state-of-the-art performance across four diverse datasets-CheXpert, MIMIC-CXR-JPG, BraTS2021, and PHM2012-in terms of semantic consistency, encrypted distillation fidelity, cold-start accuracy, and utility efficiency under privacy constraints. The framework eliminates reliance on real-data storage and supports scalable, lifelong learning in regulated, resource-constrained environments such as healthcare AI and edge computing. Code is available at FCIL .