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…