SecDD: Efficient and Secure Method for Remotely Training Neural Networks (Student Abstract)

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, 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.

Authors

• Ilia Sucholutsky
• Matthias Schonlau

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