A Knowledge Distillation Enhanced Semi-Supervised Federated Learning Framework for Intrusion Detection in EV Charging Networks

The rapid growth of electric vehicle (EV) charging networks has exposed critical vulnerabilities to cybersecurity threats, while traditional intrusion detection systems (IDS) struggle with privacy requirements and distributed data challenges. This article presents a novel knowledge distillation enhanced semi-supervised federated learning framework (KDSS-FLIDS) for intrusion detection in EV charging networks. By integrating federated learning with semi-supervised techniques and a knowledge distillation mechanism, our framework effectively utilizes both labeled and unlabeled data distributed across EV charging stations while preserving data privacy. Our specialized knowledge distillation mechanism employs temperature scaling and soft label generation to improve knowledge transfer between clients with heterogeneous data distributions. Extensive experiments on the CICEVSE2024 dataset demonstrate that our method consistently outperforms traditional approaches across various Non-IID settings. In particular, the framework exhibits accelerated convergence rates and substantially lower communication overhead, making it practical for large-scale deployment. This research presents a significant advancement in securing EV charging networks while effectively balancing privacy protection, computational efficiency, and detection accuracy.