Federated Radio Frequency Fingerprinting with Model Transfer and Adaptation

Zhang, C, Dang, S, Zhang, J ORCID: 0000-0002-3502-2926, Zhang, H and Beach, MA (2023) Federated Radio Frequency Fingerprinting with Model Transfer and Adaptation In: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2023-5-20 - 2023-5-20.

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Abstract

The Radio frequency (RF) fingerprinting technique makes highly secure device authentication possible for future networks by exploiting hardware imperfections introduced during manufacturing. Although this technique has received considerable attention over the past few years, RF fingerprinting still faces great challenges of channel-variation-induced data distribution drifts between the training phase and the test phase. To address this fundamental challenge and support model training and testing at the edge, we propose a federated RF fingerprinting algorithm with a novel strategy called model transfer and adaptation (MTA). The proposed algorithm introduces dense connectivity among convolutional layers into RF fingerprinting to enhance learning accuracy and reduce model complexity. Besides, we implement the proposed algorithm in the context of federated learning, making our algorithm communication efficient and privacy-preserved. To further conquer the data mismatch challenge, we transfer the learned model from one channel condition and adapt it to other channel conditions with only a limited amount of information, leading to highly accurate predictions under environmental drifts. Experimental results on real-world datasets demonstrate that the proposed algorithm is model-agnostic and also signal-irrelevant. Compared with state-of-the-art RF fingerprinting algorithms, our algorithm can improve prediction performance considerably with a performance gain of un to 15%.

Item Type:	Conference Item (Unspecified)
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Uncontrolled Keywords:	4605 Data Management and Data Science, 46 Information and Computing Sciences, Bioengineering, Networking and Information Technology R&D (NITRD)
Divisions:	Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	20 Mar 2023 09:43
Last Modified:	24 Jan 2026 04:09
DOI:	10.1109/INFOCOMWKSHPS57453.2023.10226112
Related Websites:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3169153
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