Authors: Qi Feng, Debiao He, Jian Shen, Min Luo, and Kim-Kwang Raymond Choo

 

Title: PpNNT: Multi-Party Privacy-Preserving Neural Network Training System

 

Journal: IEEE Transactions on Artificial Intelligence

 

Abstract: By leveraging smart devices (e.g., industrial Internet of Things (IIoT)) and real-time data analytics, organizations such as production plants can benefit from increased productivity, reduced costs, enhanced self-monitoring, and autonomous decisionmaking. In such a setting, machine learning plays an important role in data analytics, but the use of conventional centralized machine learning solutions may raise uncomfortable concerns about data privacy. Hence, one can explore the use of federated learning. In this paper, we propose the Privacy-preserving deep Neural Network Training (PpNNT), which is designed to support federated learning in the multi-party setting. To minimize the overall costs, we further design a hybrid architecture to fully maximize resource utilization. Our proposed design allows the PpNNT system to provide high security, efficiency, and scalability for IIoT data analytics, as evidenced by our theoretical security proof and experimental results on the CIFAR10 dataset.