A CNN-Based Adaptive Federated Learning Approach for Communication Jamming Recognition

Abstract

The effective and accurate recognition of communication jamming is crucial for enhancing the anti-jamming capability of wireless communication systems. At present, a significant portion of jamming data is decentralized, stored in local nodes, and cannot be uploaded directly for network training due to its sensitive nature. To address this challenge, we introduce a novel distributed jamming recognition method. This method leverages a distributed recognition framework to achieve global optimization through federated learning. Each node independently trains its local model and contributes to the comprehensive global model. We have devised an adaptive adjustment mechanism for the mixed weight parameters of both local and global models, ensuring an automatic balance between the global model and the aggregated insights from local data across devices. Simulations indicate that our personalization strategy yields a 30% boost in accuracy, and the adaptive weight parameters further enhance the recognition accuracy by 1.1%.