Neuro-symbolic federated learning over heterogeneous data-views: a structured approach to distributive EHR modelling

Conference item

Federated learning (FL) enables privacy-preserving model training across distributed Electronic Health Records (EHRs), but its deployment remains limited by data-view heterogeneity, where institutions maintain incompatible local schemas. Most existing methods address this by enforcing flat, aligned data views, which require extensive cross-site preprocessing and manual harmonisation that often discards clientspecific features, or by projecting inputs into a shared latent space, which sacrifices interpretability. We propose a modelling shift from conventional FL with vectorised inputs to a symbolic, relation-centric framework, where each client organises its EHR data as a structured, type-aware relational graph. This enables client-specific inference without requiring schema alignment and supports FL across heterogeneous data views. To model over these symbolic structures, we introduce an architecture that combines relation-aware message passing with a learnable feature relevance mechanism, jointly enabling accurate local predictions and client-specific interpretability while supporting parameter sharing across clients. Beyond strong performance on three real-world EHR datasets exhibiting data-view heterogeneity, we further show that our framework supports multimodal FL under modalitylevel heterogeneity. Using MC-MED, a publicly available multimodal emergency department dataset, we demonstrate that our method accommodates clients with partially missing modalities, highlighting its robustness and scalability in realworld clinical settings.

Authors: Molaei, S; Fatemi, B; Thakur, A; Soltan, A; Rabbi, F

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Association for the Advancement of Artificial Intelligence
• Host title: Proceedings of the 40th Annual AAAI Conference on Artificial Intelligence
• Volume: 40
• Issue: 29
• Pages: 24422-24430
• Publication date: 2026-03-14
• Acceptance date: 2025-11-09
• Event title: 40th Annual AAAI Conference on Artificial Intelligence (AAAI 2026)
• Event location: Singapore
• Event website: https://aaai.org/conference/aaai/aaai-26/
• Event start date: 2026-01-20
• Event end date: 2026-01-27
• DOI: 10.1609/aaai.v40i29.39624
• EISSN: 2374-3468
• ISSN: 2159-5399
• ISBN-10: 1577359062
• ISBN-13: 9781577359067
• Language: English