A federal learning-driven artificial intelligence framework for fundus image myopia diagnosis.

Objective: Myopia has emerged as a critical global public health challenge. This study aims to develop a privacy-preserving federated learning (FL) framework for the triple classification of fundus images (normal, myopia, and pathological myopia), designed to generalize across institutions while addressing data heterogeneity and class imbalance. Methods: We propose a novel FL framework integrating a genetic algorithm-inspired dynamic aggregator (FedProx_GA), a distance-aware attention module (OptiFocus), and a class-frequency dynamic loss. It was trained and evaluated on 1,279 fundus images from three heterogeneous medical centers. Performance was compared against standard FL baselines using area under the curve (AUC), accuracy, sensitivity, and specificity. Results: Our framework achieved an AUC of 0.9889, performing close to the performance achievable when all data are centrally stored and processed (the non-federated approach) while significantly outperforming conventional FL methods. It demonstrated robust cross-center generalization, with high sensitivity (0.9346) and specificity (0.9673), effectively managing data heterogeneity and class imbalance without breaching data privacy. Conclusion: This work presents an effective, privacy-preserving FL solution for collaborative ophthalmic artificial intelligence, showing strong potential for multi-institutional clinical deployment. Future work should focus on prospective validation with larger, diverse cohorts. The implementation code is publicly available at: https://github.com/AngelaK-code/FL_Myopia-Diagnosis.