FORESEEN: Towards Differentially Private Deep Inference for Intelligent Internet of Things
L Lyu, JC Bezdek, J Jin, Y Yang
IEEE Journal on Selected Areas in Communications | IEEE | Published : 2020
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..View full abstract
Awarded by National Key Research and Development Program of China
Awarded by National Natural Science Foundation of China (NSFC) Key Project Program
This work was supported in part by the National Key Research and Development Program of China under Grant YFB0102104 and in part by the National Natural Science Foundation of China (NSFC) Key Project Program under Grant 61932014.