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most citedPrivacy Preserving Federated Learning with Convolutional Variational Bottlenecks

2 citations · 2 across the 3 of their papers we have counts for

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cs.LG2023★ 2 cited

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…

cs.LG2021

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…