Federated Learning for Multi-Disease Ophthalmic Diagnostics using OCTA

Federated learning enables collaborative model training across multiple institutions while preserving patient data privacy. This study evaluates five different aggregation strategies (FedAvg, FedAdagrad, FedYogi, FedProx, and FedMRI) for federated learning in the context of multi-disease retinal disease classification using optical coherence tomography angiography (OCTA). We tested these approaches on a diverse dataset combining public OCTA-500 and private data provided by the University of Illinois Chicago (UIC) across seven distinct retinal pathologies, comparing performance against centralized and standalone models in three experimental scenarios of varying class complexity. Our results demonstrate that federated approaches can match or even exceed centralized training performance, with FedMRI achieving 60.87% accuracy in the comprehensive seven-class scenario and all three primary federated methods (FedAvg, FedProx, FedMRI) outperforming centralized training in simplified class scenarios (72.09% vs 69.77%). We observed that different aggregation strategies excel in different performance metrics--FedMRI consistently demonstrated superior ROC-AUC performance while FedAvg showed stronger F1-scores, suggesting better class balance management. These findings provide practical insights for implementing privacy-preserving collaborative AI systems in OCTA-based ophthalmic diagnostics.