Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks

arXiv:2405.00742v1 Announce Type: new
Abstract: 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 …

arxiv charging cost cost-effective cs.cr cs.lg cyberattacks cybersecurity cybersecurity risk data data privacy demand electric electric vehicle ev charging expansion federated forecasting graph grid infrastructure issue personalization power power grid privacy risk role safe stability stat.ml vehicle