2025/03/06 by Borazjani, Kasra, Kianfar, Kiarash, Kiarash Kianfar +3
Computer Science · #Adversarial Robustness in Machine Learning #FOS: Electrical engineering #Internet Traffic Analysis and Secure E-voting #Signal Processing (eess.SP) #Wireless Signal Modulation Classification #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2503.04136
openalex publication_date 2025/03/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Authorization systems are increasingly relying on processing radio frequency (RF) waveforms at receivers to fingerprint (i.e., determine the identity of) the corresponding transmitter. Federated learning (FL) has emerged as a popular paradigm to perform RF fingerprinting in networks with multiple access points (APs), as they allow effective deep learning-based device identification without requiring the centralization of locally collected RF signals stored at multiple APs. Yet, FL algorithms that operate merely on in-phase and quadrature (I/Q) time samples incur high convergence rates, resulting in excessive training rounds and inefficient training times. In this work, we propose FLAME: an FL approach for multi-modal RF fingerprinting. Our framework consists of simultaneously representing received RF waveforms in multiple complementary modalities beyond I/Q samples in an effort to reduce training times. We theoretically demonstrate the feasibility and efficiency of our methodology and derive a convergence bound that incurs lower loss and thus higher accuracies in the same training round in comparison to single-modal FL-based RF fingerprinting. Extensive empirical evaluations validate our theoretical results and demonstrate the superiority of FLAME in comparison to multiple considered baselines.