As healthcare data grows in volume and complexity, traditional models of research and innovation are constrained by privacy regulations and fragmented data systems. This panel explores how federated learning is transforming the landscape — enabling institutions to collaborate on AI model development without sharing sensitive patient data. Experts will discuss real-world applications, governance challenges, and the technical and ethical frameworks needed to make large-scale, privacy-preserving research a reality. Join us to discover how this approach is breaking down silos and accelerating clinical insights across institutions and borders.