
The proposed DiceSGD algorithm uses a clipped error-feedback mechanism to eliminate the constant clipping bias in differentially private stochastic gradient descent (DPSGD) while maintaining the same level of privacy guarantee.


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differentially-private-stochastic-gradient-descent-with-error-feedback-approach-to-eliminate-clipping-bias


Differentially Private Stochastic Gradient Descent with Error-Feedback Approach to Eliminate Clipping Bias


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Larger subsampling rates (batch sizes) in differentially private stochastic gradient descent (DP-SGD) reduce the effective total gradient variance, explaining the empirical benefits of using large batch sizes.


why-large-batch-sizes-work-for-differentially-private-stochastic-optimization


Why Large Batch Sizes Work for Differentially Private Stochastic Optimization
