论文标题
SECDD：远程训练神经网络的高效且安全的方法
SecDD: Efficient and Secure Method for Remotely Training Neural Networks
论文作者
论文摘要
我们利用通常认为是深度学习算法的最差品质 - 高计算成本，对大数据的需求，无解释性，对超参数选择，过度拟合和对对抗性扰动的脆弱性的高度依赖 - 以创建一种对远程和高效培训的方法，以远程和高效的培训，以远程部署的神经网络越来越多。
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.
