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

Summary

The paper examines gradient leakage attacks in federated learning, which reconstruct sensitive training data from transmitted gradients. The authors find that privacy information is unevenly distributed across neural network layers, so uniform gradient perturbation is suboptimal. They propose Layer-Specific Gradient Protection (LSGP), which scales protection intensity per layer, and report that it achieves stronger defense than uniform baselines with comparable model utility.

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