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 up to 15\%.

翻译：射频指纹识别技术通过利用制造过程中引入的硬件缺陷，为未来网络实现高安全性的设备认证提供了可能。尽管该技术在过去几年中受到广泛关注，但射频指纹识别仍面临巨大挑战：信道变化导致训练阶段与测试阶段之间的数据分布偏移。为应对这一根本性挑战并支持边缘模型训练与测试，我们提出了一种联邦射频指纹识别算法，并采用名为模型迁移与自适应（MTA）的新策略。所提算法在射频指纹识别中引入卷积层间的密集连接，以提升学习精度并降低模型复杂度。此外，我们在联邦学习框架下实现了该算法，使其具备通信高效性与隐私保护能力。为进一步克服数据失配问题，我们将已学习模型从一种信道条件迁移至其他信道条件，并仅利用有限信息进行自适应，从而在环境偏移下实现高精度预测。在真实数据集上的实验结果表明，该算法具有模型无关性与信号无关性。与现有最先进的射频指纹识别算法相比，本算法的预测性能显著提升，性能增益高达15%。