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

arXiv:2405.00742v1 Announce Type: cross
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 …

abstract arxiv charging collective cost cs.cr cs.lg cyberattacks cybersecurity data data privacy demand demand forecasting electric electric vehicle ev charging expansion forecasting graph graph learning grid however infrastructure issue personalization power privacy risk role safe stability stat.ml type