Specific emitter identification (SEI) enhances wireless communication security by identifying specific devices or signals to monitor anomalies effectively. However, data scarcity and heterogeneity challenge traditional centralized methods and few-shot learning (FSL), which depend on centralized data. We propose a personalized decentralized federated FSL method (FedDePF) for SEI. FedDePF organizes edge devices into clusters, enabling local aggregation within clusters and global collaboration between cluster centers. This reduces server communication overhead and addresses data heterogeneity in distributed environments. Experiments show FedDePF significantly improves SEI performance in data-scarce scenarios, outperforming traditional decentralized methods, providing a secure and efficient solution.

Artificial intelligence (AI) has emerged as the most promising solution expected to overcome the high degree of abstraction of radio signals and achieve accurate automatic modulation classification (AMC). To further improve the classification performance of the AMC model and enhance its interpretability, the network output layer is modeled as a decision space into which the input data is projected. In this paper, we expand the inter-class angle between the classes with the largest confusion rate to increase the decision space. In addition, we extend the perspective to the softmax layer and evaluate the negative impact of the output distribution range on the confidence difference in the AMC problem. We further propose constraining the norm of the input data to the output layer in combination with prior knowledge of the distribution of modulation signal data. Combining the above two aspects, a Maximum Focal Inter-Class Angular Loss with Norm Constraint (MFICAL-NC) scheme is proposed. The experimental results show that the method can guide the model to obtain a better fitting state and a stronger generalization ability.