Distributed IoT Device Identification Based on Federated Learning

被引：0
|
作者：
Zou, Xuxi [1 ]
Zhou, Zhongran [1 ]
Wang, Honglan [2 ]
Li, Fei [2 ]
Gu, Yalin [1 ]
Wei, Xunhu [1 ]
Li, Jing [2 ]
机构：
[1] Nanjing Nari Information and Communication Technology Co., Ltd., Nanjing,211106, China
[2] College of Computer Science and Technology, College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing,211106, China
来源：
Computer Engineering and Applications | 2024年 / 60卷 / 23期
关键词：
Traditional IoT device identification methods usually use centralized training; where private traffic from edge devices is deployed in a central server for learning fingerprint extraction and identification. But centralized training suffers from data privacy issues as well as single-point-of-failure problems. To address these issues; a distributed IoT device identification method based on federated learning is proposed. For edge devices; a lightweight device fingerprint identification model is proposed; which extracts temporal information as well as inter-feature information from network traffic sessions to generate distinguishable fingerprints; and trains an efficient classifier to achieve fingerprint identification. For central servers; a heterogeneous federated learning algorithm based on generative knowledge distillation is designed; which integrates local information in an agentless data manner by training a variation generator and uses the integrated knowledge to guide local models; thus solving the statistical heterogeneity problem in distributed scenarios. Extensive experiments are conducted on four publicly available benchmark datasets; comparing it with state-of-the-art federated learning methods and device fingerprinting methods. It validates the effectiveness of the proposed method for improving distributed IoT device recognition accuracy and efficiency. © 2024 Journal of Computer Engineering and Applications Beijing Co; Ltd; Science Press. All rights reserved;
D O I：
10.3778/j.issn.1002-8331.2402-0140
中图分类号：
学科分类号：
摘要：
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页码：155 / 167
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