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, overfitting, and vulnerability to adversarial perturbations - in order to create a method for the secure and efficient training of remotely deployed neural networks over unsecured channels. ... We leverage what are typically considered the worst qualities of deep learning algorithms - high computational cost, requirement for large data, no explainability, high dependence on hyper-parameter choice ... Conclusion and future work We have proposed a method for producing synthetic data that can be used to securely and efficiently train remotely deployed neural networks over unsecured channels. ...arXiv:2009.09155v1 fatcat:6hnes7wpyzgvzjsclmc3z6tine