An Efficient and Dynamic Privacy-Preserving Federated Learning System for Edge Computing

• Published in: IEEE transactions on information forensics and security Vol. 19; pp. 207 - 220
• Main Authors: Tang, Xinyu, Guo, Cheng, Choo, Kim-Kwang Raymond, Liu, Yining
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; Cryptography; Cybersecurity; Data models; deep learning; Edge computing; Federated learning; Heterogeneity; Privacy; privacy-preserving; Security; Servers; Software; Training
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2023.3320611

Federated learning (FL) has been used to enhance privacy protection in edge computing systems. However, attacks on uploaded model gradients may lead to private data leakage, and edge devices frequently joining and leaving will impact the system running. In this paper, we propose a dynamic and flexible federated edge learning (FEL) scheme that can defend against malicious edge servers and edge devices to recover sensitive data and efficiently manage edge devices. A heterogeneity-aware scheduling strategy is designed to take into account the different impacts of heterogeneous edge devices on global model performance. The strategy determines the order of devices participation in each round based on the relative contribution level of the online edge device model, and the edge device with the highest contribution level is selected first. Numerical experiments show that our system improves test accuracy and time, and the security analyses show that our scheme meets the security requirements.