Explore federated learning, generalization, and personalization in medical AI through this comprehensive lecture by Karan Singhal from Stanford University. Delve into two recent works on generalization in federated learning and federated reconstruction, examining their applications in medical settings. Learn about challenges in generalizing to unseen patients and hospitals, both in federated and centralized environments. Discover novel approaches to improve generalization across multiple local data distributions, and gain insights into representation learning techniques that can lead to wider adoption of beneficial AI in healthcare. Engage with concepts such as participation gaps, semantic partitioning, and client-specific embeddings, while understanding their implications for medical data analysis and AI deployment in clinical settings.