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.

电动汽车充电需求预测中减轻网络安全风险对于集体电动汽车充电的安全运营、电网稳定性和成本效益的基础建设至关重要。为了解决数据隐私和网络攻击易受影响的问题以及考虑不同充电站之间的空间相关性，本文提出了一种联邦图学习方法，涉及多个充电站来协同训练更通用的深度学习模型，用于需求预测，同时捕捉不同站点之间的空间相关性，并增强对潜在攻击的鲁棒性。