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cs.LG2022
Dropout is NOT All You Need to Prevent Gradient Leakage
Daniel Scheliga, Patrick Mäder, Marco Seeland
Gradient inversion attacks on federated learning systems reconstruct client training data from exchanged gradient information. To defend against such attacks, a variety of defense…
cs.LG2022★ 2 cited
Combining Stochastic Defenses to Resist Gradient Inversion: An Ablation Study
Daniel Scheliga, Patrick Mäder, Marco Seeland
Gradient Inversion (GI) attacks are a ubiquitous threat in Federated Learning (FL) as they exploit gradient leakage to reconstruct supposedly private training data. Common defense…