Federated Radio Frequency Fingerprint Identification Powered by Unsupervised Contrastive Learning


Shen, Guanxiong, Zhang, Junqing ORCID: 0000-0002-3502-2926, Wang, Xuyu and Mao, Shiwen (2024) Federated Radio Frequency Fingerprint Identification Powered by Unsupervised Contrastive Learning. IEEE Transactions on Information Forensics and Security, 19 (99). pp. 9204-9215. ISSN 1556-6013, 1556-6021

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Abstract

Radio frequency fingerprint identification (RFFI) is a promising physical layer authentication technique that utilizes the unique impairments within the analog front-end of transmitters as distinct identifiers. State-of-the-art RFFI systems are frequently powered by deep learning, which requires extensive training data to ensure satisfactory performance. However, current RFFI studies suffer from a severe lack of training data, which poses challenges in achieving high identification accuracy. In this paper, we propose a federated RFFI system that is particularly suitable for Internet of Things (IoT) networks, which holds a high potential to address the data scarcity challenge in RFFI development. Specifically, all the receivers in an IoT network can pre-train a deep learning-driven feature extractor in a federated and unsupervised manner. Subsequently, a new client can perform fine-tuning on the basis of the pre-trained feature extractor to activate its RFFI functionality. Extensive experimental evaluation was carried out, involving 60 commercial off-the-shelf (COTS) LoRa transmitters and six software-defined radio (SDR) receivers. The experimental results demonstrate that the federated RFFI protocol can effectively improve the identification accuracy from 63% to 95%, and is robust to receiver hardware and location variations.

Item Type: Article
Uncontrolled Keywords: 4605 Data Management and Data Science, 4606 Distributed Computing and Systems Software, 46 Information and Computing Sciences, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD)
Divisions: Faculty of Science and Engineering
Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 26 Sep 2024 07:14
Last Modified: 11 Jul 2025 18:40
DOI: 10.1109/tifs.2024.3469820
Related Websites:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3184752
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