SEP 9, 2026 · PREPRINT
OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis
arXiv
A controlled systems study using synthetic data and non-patient-matched pairing to benchmark federated learning architectures; descriptive proxy comparisons only, not diagnostic validation or deployment readiness.
Reported
Macro-F1 (local-only, K=5, α=0.1)0.297
Macro-F1 (FedAvg, K=5, α=0.1)0.662±0.074
Macro-F1 (FedProx, K=5, α=0.1)0.737±0.085