Robust Decentralized Federated Learning for Automatic Modulation Classification Under Impulsive Noise and Data Heterogeneity

Abstract

Automatic modulation classification (AMC) is essential for enhancing the spectral efficiency and noncooperative communication capabilities of Internet of Things (IoT) systems. IoT devices are widely deployed in untrusted intelligent scenarios where data is edge-distributed and follows a nonindependent and identically distributed (non-IID) pattern, making applying traditional deep learning models directly challenging. Moreover, impulsive noise is prevalent in industrial and intelligent scenarios and dramatically degrades recognition accuracy. Federated learning (FL) has been extensively applied to privacy-preserving AMC tasks in recent years. However, the reliance of existing centralized FL architectures on a central server poses inherent security risks. To address these challenges, we propose a novel fully decentralized FL (DFL)-based AMC framework, termed DeKDAMC, which enables collaborative model training across edge devices without a central coordinator. Specifically, the framework incorporates a hybrid loss function with knowledge distillation (KD)-based temporal self-distillation to enhance local training consistency and alleviate optimization instability under heterogeneous data distributions. Furthermore, to enhance robustness, we embed bounded nonlinear function (BNF) modules within the network architecture to suppress the detrimental effects of impulsive noise. Extensive experiments demonstrate that the proposed DeKDAMC consistently achieves superior classification accuracy and enhanced stability, particularly under varying client counts and intermittent connectivity, validating its practical suitability for complex and security-sensitive IoT environments.