FORESEEN: Towards Differentially Private Deep Inference for Intelligent Internet of Things

Abstract

In state-of-the-art deep learning, centralized deep learning forces end devices to pool their data in the cloud in order to train a global model on the joint data, while distributed deep learning requires a parameter server to mediate the training process among multiple end devices. However, none of these architectures scale gracefully to large-scale privacy and time-sensitive IoT applications. Therefore, we are motivated to propose a FOg-based pRivacy prEServing dEep lEarNing framework named FORESEEN, so as to achieve scalable, accurate yet private analytics. In FORESEEN, the intermediate fog nodes and the cloud collaboratively perform noisy training of deep neural networks (DNNs), while ea..