FedVaccine: Robust Federated Learning in Noisy and Non-IID Wireless Network Environments

Federated Learning (FL)-based modulation decoding enables collaborative learning across distributed Artificial Intelligence (AI)-powered modulational signal decoding models in large-scale wireless communication networks. Despite significant advancements, FL-based modulation decoding faces two primary challenges: (1) Vulnerability to noisy environments - Current studies often prioritize high-quality datasets during training to ensure model performance, which often results in overfitting since real-world signals involve a wide range of noise levels from various sources. (2) Model divergence in non-Independent and Identically Distributed ( non-IID) environments - The aggregation step in a conventional FL leads to divergence on a large federated scale, resulting in the loss of local-specific knowledge. Moreover, existing FL-based modulation decoding studies typically focus on a single non-IID issue, which limits their ability to generalize across large-scale heterogeneous settings. To address these challenges, we propose FedVaccine, a framework that enhances model generalization across diverse noise conditions and non-IID data environments. Instead of relying on high-quality datasets that risk overfitting, FedVaccine employs a harmonic noise resilience approach—akin to a vaccination effect—that deliberately exposes the model to controlled noise, identifying noise tolerance, regularizing training, and ensuring robust decoding performance across varying noise levels. Additionally, FedVaccine incorporates a Federated Split Learning approach to cumulatively update the global model, ensuring adaptive recalibration and mitigating the loss of local knowledge during aggregation. Furthermore, a localized support set is used to ensure effective personalization. Experimental results demonstrate that FedVaccine outperforms existing FL-based modulation classification models and other learning paradigms in both IID and non-IID scenarios using public datasets, showcasing its effectiveness in practical deployment scenarios across wireless networks.