In a world where data privacy is both a legal requirement and a competitive differentiator, federated learning is emerging as a game-changer for computer vision. It allows organizations to train AI models across decentralized image data — without ever moving or exposing sensitive files. From retail shelf analytics and medical imaging to defect detection and autonomous driving, this privacy-first approach is enabling faster, safer innovation. In this post, we explore how federated learning works, where it’s delivering real ROI, and how C-level leaders can adopt it using a blend of ready-made APIs and custom solutions to stay ahead in the AI race.