用联邦学习保护充电桩安全，兼顾隐私与检测精度。
Anomaly Detection in Electric Vehicle Charging Stations Using Federated Learning
- 在本地训练模型，数据不离开设备，保护用户隐私。
- 非独立同分布数据下，FedAvgM比FedAvg准确率高且收敛快。
- 适合部署在异构充电桩网络中，提升整体安全性。
联邦学习（FL）是一种去中心化训练框架，广泛应用于物联网生态系统中，通过将原始数据保留在本地来保护隐私，特别适用于具备感知与通信功能的智能电网、联网自动驾驶车辆及电动汽车充电站等工业系统。随着电动车基础设施快速扩张，保障这些基于物联网的充电站免受网络攻击变得至关重要。集中式入侵检测系统因涉及敏感网络与用户数据，引发隐私担忧，而联邦学习成为可行替代方案。然而，现有基于联邦学习的入侵检测评估忽略了实际挑战，如系统异构性和非独立同分布（non-IID）数据。为此，本文针对电动汽车充电站中的异常检测任务，在系统与数据异构条件下评估了联邦学习性能。采用广为研究的FedAvg与FedAvgM优化方法，分析其有效性。在独立同分布（IID）设置下，FedAvg性能优于使用相同神经网络的集中式模型。但在非独立同分布数据和系统异构性下，性能下降；而FedAvgM在异构环境下持续优于FedAvg，展现出更优的收敛性与更高的异常检测准确率。结果表明，联邦学习可在保持较低性能损失的前提下应对物联网型充电站的异构性，其中FedAvgM是实现鲁棒、隐私保护的电动汽车充电站安全的有力候选方案。
原文摘要 · Abstract (English)
Federated Learning (FL) is a decentralized training framework widely used in IoT ecosystems that preserves privacy by keeping raw data local, making it ideal for IoT-enabled cyber-physical systems with sensing and communication like Smart Grids (SGs), Connected and Automated Vehicles (CAV), and Electric Vehicle Charging Stations (EVCS). With the rapid expansion of electric vehicle infrastructure, securing these IoT-based charging stations against cyber threats has become critical. Centralized Intrusion Detection Systems (IDS) raise privacy concerns due to sensitive network and user data, making FL a promising alternative. However, current FL-based IDS evaluations overlook practical challenges such as system heterogeneity and non-IID data. To address these challenges, we conducted experiments to evaluate the performance of federated learning for anomaly detection in EV charging stations under system and data heterogeneity. We used FedAvg and FedAvgM, widely studied optimization approaches, to analyze their effectiveness in anomaly detection. Under IID settings, FedAvg achieves superior performance to centralized models using the same neural network. However, performance degrades with non-IID data and system heterogeneity. FedAvgM consistently outperforms FedAvg in heterogeneous settings, showing better convergence and higher anomaly detection accuracy. Our results demonstrate that FL can handle heterogeneity in IoT-based EVCS without significant performance loss, with FedAvgM as a promising solution for robust, privacy-preserving EVCS security.
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