Mitigating cybersecurity risk in electric vehicle (EV) charging demand forecasting plays a crucial role in the safe operation of collective EV chargings, the stability of the power grid, and the cost-effective infrastructure expansion. However, existing methods either suffer from the data privacy issue and the susceptibility to cyberattacks or fail to consider the spatial correlation among different stations. To address these challenges, a federated graph learning approach involving multiple charging stations is proposed to collaboratively train a more generalized deep learning model for demand forecasting while capturing spatial correlations among various stations and enhancing robustness against potential attacks. Firstly, for better model performance, a Graph Neural Network (GNN) model is leveraged to characterize the geographic correlation among different charging stations in a federated manner. Secondly, to ensure robustness and deal with the data heterogeneity in a federated setting, a message passing that utilizes a global attention mechanism to aggregate personalized models for each client is proposed. Thirdly, by concerning cyberattacks, a special credit-based function is designed to mitigate potential threats from malicious clients or unwanted attacks. Extensive experiments on a public EV charging dataset are conducted using various deep learning techniques and federated learning methods to demonstrate the prediction accuracy and robustness of the proposed approach.

翻译：缓解电动汽车充电需求预测中的网络安全风险，对于保障集体充电设施的安全运行、电网稳定性以及经济高效的基础设施扩建至关重要。然而，现有方法要么存在数据隐私问题且易受网络攻击影响，要么未能考虑不同充电站之间的空间关联性。为解决这些挑战，提出了一种涉及多个充电站的联邦图学习方法，通过协同训练一个更具泛化能力的深度学习模型进行需求预测，同时捕捉各站点的空间关联性并增强对潜在攻击的鲁棒性。首先，为提升模型性能，利用图神经网络（GNN）以联邦方式表征不同充电站间的空间关联性。其次，为确保鲁棒性并应对联邦环境中的数据异质性，提出一种利用全局注意力机制为每个客户端聚合个性化模型的的消息传递方法。第三，针对网络攻击问题，设计了一种特殊的基于信用度的函数，以缓解恶意客户端或非预期攻击带来的潜在威胁。基于公开的电动汽车充电数据集，采用多种深度学习技术和联邦学习方法开展了大量实验，验证了所提方法在预测精度和鲁棒性方面的优势。