A visual comparison of original, reconstructed, and noise-injected medical images under federated learning to illustrate privacy risks and shadow defense impact.

🔒7 Alarming Privacy Risks of Federated Learning—and the Breakthrough Shadow Defense Fix You Need

Introduction Federated Learning (FL) has been heralded as the privacy-preserving future of AI, especially in sensitive domains like healthcare. But behind its collaborative promise lies a serious vulnerability: gradient inversion attacks (GIA). These attacks can reconstruct original training images from shared gradients—exposing confidential patient data. Enter the breakthrough: Shadow Defense. In this article, we dive […]

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