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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
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
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页码：155 / 167
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