Differentially Private Stochastic Gradient Descent
PulseAugur coverage of Differentially Private Stochastic Gradient Descent — every cluster mentioning Differentially Private Stochastic Gradient Descent across labs, papers, and developer communities, ranked by signal.
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New DP-SGD method enhances privacy by obscuring batch sizes
Researchers have developed a new mechanism to enhance privacy in Differentially Private Stochastic Gradient Descent (DP-SGD) by privatizing batch sizes. This method uses a random number of dummy points to obscure the ac…
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New method optimizes privacy spending in machine learning training
Researchers have developed a new method for optimizing privacy allocation in machine learning, specifically for Differentially Private Stochastic Gradient Descent (DPSGD). The approach formulates the Gaussian noise-vari…
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New DP-MacAdam algorithm improves private ML training
Researchers have introduced DP-MacAdam, a new algorithm designed to enhance privacy in machine learning training. This method combines adaptive clipping and adaptive momentum techniques, using the same gradient variance…
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New algorithm enhances privacy guarantees in selective release machine learning
Researchers have identified a flaw in the privacy accounting of the Differentially Private Selective Update and Release (DPSUR) algorithm. The existing method overlooks variations in sampling probability introduced by i…