Charging Station Recommendation for Electric Vehicle Based on Federated Learning
Xiaohui Wang, Xiaokun Zheng, Liang Xiao
Abstract
Abstract At present, the usage of EV charging facilities is unbalanced. The accuracy of the charging station recommendation does not meet the demand. Due to the limitation of user privacy protection, charge point operators and vehicle enterprises cannot provide data to each other for joint analysis. Therefore, we proposed recommendation method of EV charge point based on federated learning. The federated factorization machine is implemented to make use of data features in both sides and cross features between them. We build the model by encrypted entity alignment, secure federated training and predicting. The experimental results show that the federated model improves the AUC of the model by 6% over those built with features only from the charge point operators. The model is superior to centralized LR-based and RF-based models. While the data does not need to leave the original platform, the model realizes the secure and precise federated charging point recommendation based on more comprehensive features.