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…