Lightweight Spiking Federated Learning-Based Detector for Cognitive Vehicular Networking

Interweave Cognitive radio (CR) technologies enables support of infotainment and multimedia services in vehicular ad hoc networks (VANETs), especially during accidents or traffic congestion. The dynamic nature of VANET topology and fluctuating traffic volumes makes machine learning (ML) techniques promising for spectrum sensing; however, centralized training of ML models can lead to significant drawbacks, such as high communication overhead and data privacy concerns due to the transfer of large amounts of raw data. While adaptation of federated learning (FL) can address this problem, on-device training of deep learning with a federated approach poses computational challenges for vehicles. To address these limitations, this paper proposes a novel lightweight spectrum detection framework integrating spiking neural networks (SNNs) and FL. The SNN's event-driven nature reduces computational complexity, while FL enables decentralized training across vehicles, preserving data privacy and minimizing communication overhead by transmitting model updates rather than raw data. Validation using field-collected LTE subsystem reservations of 5G Non-Standalone (NSA) confirms that the proposed approach achieves fast inference times, enhancing spectrum sensing efficiency and expanding spectral resources for vehicular communication.