Personalized Federated Graph Learning for Heterogeneous Incomplete EHRs

Liping Chen, Guangqing Bai, Zhuoyang Han, Jing Ren, Mujie Liu, Tao Tang, Shuo Yu, Ivan Lee

Abstract: Real-world electronic health records (EHRs) are decentralized and typically exhibit incomplete information across both modalities and features. This heterogeneous missingness poses a major challenge for federated graph learning, which aims to collaboratively model distributed medical data while preserving privacy. Existing methods often assume uniform data completeness or apply imputation strategies, which struggle to maintain consistent predictive performance under non-uniform and severe data incompleteness. To address this issue, we propose MissPCL, a personalized federated graph contrastive learning framework tailored to heterogeneous client-level missingness. Each client estimates its missingness rate and constructs a bipartite patient-modality graph from observed features. Clients with low missingness participate in global training using contrastive learning on local graphs, while high-missingness clients are excluded from aggregation and instead perform personalized fine-tuning. Experiments on two real-world EHR datasets under diverse missingness scenarios demonstrate that MissPCL consistently outperforms state-of-the-art baselines in classification accuracy and robustness. Notably, our framework achieves stable performance across varying degrees of data incompleteness, showing its practical utility in realistic federated clinical settings. Code implementations and the supplementary materials are available at https://github.com/TutaResearch/MISSPCL.

External IDs:dblp:conf/ausai/ChenBHRLTYL25