Privacy Implications and Comparisons of Batch Sampling Methods in Differentially Private Stochastic Gradient Descent (DP-SGD)

Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for training machine learning models like neural networks while ensuring privacy. It modifies the standard gradient descent process by clipping individual gradients to a fixed norm and adding noise to the aggregated gradients of each mini-batch. This approach enables privacy by preventing sensitive information in […]

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