A Novel Perspective on Gradient Defense: Layer-Specific Protection Against Privacy Leakage

Gradient leakage attacks (methods that steal private data by analyzing the mathematical updates sent between computers in federated learning, where AI training happens across multiple devices) pose privacy risks in federated learning systems. Researchers discovered that different layers of neural networks (sections that process information at different stages) leak different amounts of private information, so they created Layer-Specific Gradient Protection (LSGP), which applies stronger privacy protection to layers that leak more sensitive data rather than protecting all layers equally.