Federated Radio Frequency Fingerprint Identification Powered by Unsupervised Contrastive Learning
Guanxiong Shen, Junqing Zhang, Xuyu Wang, Shiwen Mao
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.