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Found 2 papers, 1 papers with code
Differentially Private Federated Learning: A Systematic Review
To rectify this gap, we propose a new taxonomy of differentially private federated learning based on definition and guarantee of differential privacy and federated scenarios.
DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release
Motivated by this, this paper proposes DPSUR, a Differentially Private training framework based on Selective Updates and Release, where the gradient from each iteration is evaluated based on a validation test, and only those updates leading to convergence are applied to the model.
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