Safeguarding Federated Learning From Data Reconstruction Attacks via Gradient Dropout

Summary

Researchers propose Gradient Dropout, a defense for federated learning that protects local training data from gradient inversion attacks. It randomly scales a subset of gradient components and replaces the rest with Gaussian noise across all model layers. Experiments show attack methods then produce low-quality reconstructed images, with less than 2% accuracy reduction relative to the baseline.

Mitigation

Apply Gradient Dropout: randomly scale a subset of gradient components and replace the remainder with Gaussian noise across all layers of the model.

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