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Standard methods for differentially private training of deep neural networksreplace back-propagated mini-batch gradients with biased and noisyapproximations to the gradient. These modifications to training often result ina privacy-preserving model that is significantly less accurate than itsnon-private counterpart. We hypothesize that alternative training algorithmsmay be more amenable to differential privacy. Specifically, we examine thesuitability of direct feedback alignment (DFA). We propose the firstdifferentially private method for training deep neural networks with DFA andshow that it achieves significant gains in accuracy (often by 10-20%) comparedto backprop-based differentially private training on a variety of architectures(fully connected, convolutional) and datasets.

Differentially Private Deep Learning with Direct Feedback Alignment