Swarm Dynamic Spectrum Access for Internet-of-Things

With the rapid advancement of wireless communication technologies, the scarcity of available spectrum resources has become increasingly pronounced. Dynamic Spectrum Access (DSA) emerges as a promising solution to address this challenge. Traditional DSA methods based on Q-learning emphasize autonomous learning by individual nodes, whereas more recent approaches incorporating Federated Learning (FL) introduce collaborative learning among nodes but remain reliant on a central server. In this paper, we propose a novel DSA scheme based on Swarm Learning (SL), which enables a fully decentralized, distributed machine learning paradigm by establishing a blockchain-based peer-to-peer network. This approach capitalizes on the strengths of SL, facilitating cooperative learning among multiple nodes to enhance DSA performance. By allowing IoT terminals to share model parameters within a blockchain framework, the proposed scheme mitigates the vulnerabilities associated with centralized servers. Simulation results demonstrate that the SL-based DSA scheme not only surpasses the access efficiency of FL-based methods but also obviates the necessity of a central aggregation server. Furthermore, the fully decentralized architecture enhances the auditability of system data, thereby bolstering user privacy protection.