A Heterogeneity-Aware Federated Learning Framework for Graph Neural Networks in Multi-Hospital Medical Systems
Year: 2026
Type: article
Abstract: Medical institutions increasingly hold data that is relational rather than tabular: patients linked by medical history, diagnostics by dependency, and doctors by consultations. Graph neural networks (GNNs) are a natural fit for such data, but hospitals cannot pool it directly, since records remain confined by law to the collecting hospital. Federated learning lets a central server train a shared model by aggregating parameter updates instead of raw graphs. GNN parameters, however, define a graph-dependent message-passing operator, so when hospitals differ in topology, degree distribution, and homophily, standard update-averaging no longer produces a coherent update for any hospital’s actual operator, a mismatch accuracy metrics can mask, hitting structurally atypical hospitals hardest. We introduce the Global Geometric Reference Structure (GGRS), a server-side layer that regulates updates before aggregation: directional soft-weighting down-weights updates that disagree with the hospital consensus, subspace projection keeps only shared directions, and sensitivity clipping stops any one hospital from dominating the aggregate. Across six benchmark graphs, two architectures, and four federated optimizers, GGRS improves accuracy by up to 5.1% for structurally under-represented hospitals, with the best-performing combination, SCAFFOLD paired with GGRS, also reaching target accuracy up to 12 rounds faster than unregulated aggregation. These results suggest geometry-aware aggregation helps federated GNNs serve heterogeneous hospitals more equitably, without requiring any hospital to change how it trains locally.
Authors: Chethana Prasad Kabgere, Shylaja S S
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7204704964