A Secure Dynamic Spectrum Access Scheme for Internet of Things With Swarm Learning

With the advancement of wireless communication technologies, available spectrum resources are becoming increasingly scarce. Dynamic Spectrum Access (DSA) is one of the effective approaches to address the challenge. Traditional Q-learning DSA relies on node self-learning, while recent Federated Learning (FL) DSA introduces node collaboration but still depends on a central server. This paper proposes a DSA scheme based on Swarm Deep Reinforcement Learning (SDRL), achieving a fully decentralized distributed machine learning through the construction of a blockchain-based peer-to-peer network. This scheme leverages the advantages of swarm learning (SL), utilizing collaborative learning among multiple nodes to enhance DSA performance. IoT terminals share model parameters, utilizing the benefits of blockchain networks to mitigate the risks associated with centralized servers. Simulation results demonstrate that the SDRL scheme not only improves DSA access efficiency compared to FL-based schemes but also eliminates the need for a central aggregation server. The fully decentralization architecture enhances the auditablity of the data in the system which further preserves each user’s privacy.