Federated Radio Frequency Fingerprinting with Model Transfer and Adaptation

Zhang, Chuanting, Dang, Shuping, Zhang, Junqing ORCID: 0000-0002-3502-2926, Zhang, Haixia and Beach, Mark A (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 or Workshop Item (Unspecified)
Additional Information:	(c) 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Uncontrolled Keywords:	Bioengineering
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	20 Mar 2023 09:43
Last Modified:	17 Mar 2024 16:20
DOI:	10.1109/infocomwkshps57453.2023.10226112
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3169153