Combining Variational Modeling with Partial Gradient Perturbation to Prevent Deep Gradient Leakage. (arXiv:2208.04767v1 [cs.LG])

Exploiting gradient leakage to reconstruct supposedly private training data,
gradient inversion attacks are an ubiquitous threat in collaborative learning
of neural networks. To prevent gradient leakage without suffering from severe
loss in model performance, recent work proposed a PRivacy EnhanCing mODulE
(PRECODE) based on variational modeling as extension for arbitrary model
architectures. In this work, we investigate the effect of PRECODE on gradient
inversion attacks to reveal its underlying working principle. We show that
variational modeling induces stochasticity on PRECODE's …

lg modeling