FedDePF: Decentralized Personalized Federated Few-Shot Learning for Specific Emitter Identification

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.