Advancing federated domain generalization in ophthalmology: Vision enhancement and consistency assurance for multicenter fundus image segmentation

Abstract

Federated learning has transformed privacy-preserving medical image analysis, but the diversity of imaging equipment and conditions poses significant challenges in creating models that generalize effectively across domains. Current federated domain generalization (FedDG) methods often require partial information sharing, which may compromise privacy standards. To address this, we introduce the Federated Domain-Generalization Vision Enhancement and Consistency Assurance (FedDG-VECA) approach. This method enhances the generalization ability of federated learning by independently strengthening local node, integrating a Federated Vision Feature Extractor (FVFE) for global data capture and local fine-tuning, a Federated Vision Augmentation Strategy (FVAS) to simulate diverse image distributions, and a Federated Bootstrapped Consistency Assurance (FBCA) mechanism using a dual MLP network for stable, consistent model performance across varied data sources. Initial experiments confirm that FedDG-VECA significantly improves model generalization without compromising privacy, ensuring robust and consistent diagnostic capabilities across multiple institutions.