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

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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.

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APA

Sucholutsky, I., & Schonlau, M. (2021). SecDD: Efficient and Secure Method for Remotely Training Neural Networks (Student Abstract). In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (Vol. 18, pp. 15897–15898). Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v35i18.17945

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