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.

The authors have declared no competing interest.

This study is supported by NEI R15EY035804, R21EY035271 (MNA), UNC Charlotte Faculty Research Grant (MNA), NC Diabetes Research Center P30DK124723 (SSO), Research to Prevent Blindness (TL) and NIH Grant P30EY026877 (TL).

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

UIC dataset was approved by the institutional review board of the University of Illinois at Chicago and complied with the ethical standards stated in the Declaration of Helsinki

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Second B. Author Jr. was with Rice University, Houston, TX 77005 USA. He is now with the Department of Physics, Colorado State University, Fort Collins, CO 80523 USA (e-mail: authorlamar.colostate.edu).

Third C. Author is with the Electrical Engineering Department, University of Colorado, Boulder, CO 80309 USA, on leave from the National Research Institute for Metals, Tsukuba, Japan (e-mail: authornrim.go.jp).