  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.
