2 citations · 4 across the 4 of their papers we have counts for
4 papers
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks
Daniel Scheliga, Patrick Mäder, Marco Seeland
Gradient inversion attacks are an ubiquitous threat in federated learning as they exploit gradient leakage to reconstruct supposedly private training data. Recent work has proposed…
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…
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…
PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage
Daniel Scheliga, Patrick Mäder, Marco Seeland
Collaborative training of neural networks leverages distributed data by exchanging gradient information between different clients. Although training data entirely resides with the…