Privacy-Preserving AI Across Institutions: Federated Learning Lessons for Medical Imaging

How do we build useful AI across institutions when raw data cannot be centralized? In this talk, I present a federated learning case study in glaucoma imaging using 5,550 color fundus photographs from nine datasets across seven countries. I show how site-specific fine-tuning preserved the privacy advantages of federated learning while matching central-model performance for cup segmentation across all sites and for disc segmentation in most sites, with clear gains in cross-site generalizability over local and standard federated baselines. Beyond the model results, I will discuss what this teaches us about privacy-preserving AI deployment in practice: the tradeoff between local adaptation and global robustness, the operational challenges of multi-site learning, and where secure infrastructure can complement federated approaches. Attendees will leave with a concrete research example and practical lessons for designing robust, privacy-aware AI systems in regulated settings.