May 21, 2022 · Large convolutional neural networks (CNN) can be difficult to train in the differentially private (DP) regime, since the optimization algorithms ...
Oct 31, 2022 · We propose a new implementation of differentially private training for CNN, that substantially improves speed and reduces memory burden.
Large convolutional neural networks (CNN) can be difficult to train in the differen- tially private (DP) regime, since the optimization algorithms require a ...
An efficient and scalable implementation of this clipping on convolutional layers, termed as the mixed ghost clipping, is proposed that significantly eases ...
Large convolutional neural networks (CNN) can be difficult to train in the differentially private (DP) regime, since the optimization algorithms require a ...
Nov 29, 2022 · Differential privacy (DP) has become the standard approach to provide privacy guarantee for modern machine learning models. The privacy level is ...
Fast Differential Privacy (fastDP) is a library that allows differentially private optimization of PyTorch models, with a few additional lines of code.
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Explore all code implementations available for Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy.
My research interests include optimization algorithms, algorithmic efficiency, differential privacy, deep learning theory, distributed learning, and high- ...
This collective mechanism makes data parallel training simple because all workers always have the same variable values, thus there are no synchronization ...
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