Abstract In medical knowledge systems, federated learning provides a promising paradigm for collaborative knowledge extraction while preserving data privacy. However, inherent heterogeneity in medical information—stemming from variations in disease distribution, imaging protocols, and patient demographics—severely degrades the performance of traditional federated frameworks. To address this challenge, we propose FedCKD, a knowledge-driven federated framework tailored for heterogeneous medical information systems. FedCKD introduces three key innovations: (1) a label-driven knowledge clustering mechanism that partitions medical nodes based on disease-specific knowledge representations, ensuring intra-cluster semantic consistency; (2) a two-stage adaptive aggregation strategy for knowledge-oriented model fusion within each cluster, balancing local specialization and cluster-level consistency; (3) a cross-cluster knowledge distillation protocol that enables privacy-preserving transfer of complementary knowledge across specialized medical domains via weighted teacher ensembles. By simulating interoperability in distributed medical systems, FedCKD achieves cross-domain knowledge integration while respecting statistical heterogeneity. Comprehensive experiments on multiple datasets demonstrate that FedCKD significantly outperforms state-of-the-art methods, establishing it as an effective solution for knowledge extraction and integration in privacy-sensitive, heterogeneous medical ecosystems.
Xu et al. (Fri,) studied this question.