重庆理工大学学报（自然科学版）2025，Vol.39Issue(3)：113-119,7.DOI:10.3969/j.issn.1674-8425(z).2025.02.014
面向联邦学习的油电混合电动汽车站点推荐算法研究
Research on hybrid electric vehicle station recommendation algorithm based on federated learning
摘要
Abstract
As a means of low-carbon and environmentally friendly transportation,Hybrid Electric Vehicle(HEV)is developing rapidly.To guarantee the privacy and security of users in the recommendation process,we propose a HEV station recommendation algorithm based on vertical federated learning algorithm.Through the model training mechanism of local training and central aggregation,the local training model is updated under the premise of user privacy data security.Blockchain technology is integrated with cloud computing to provide a secure and trustworthy cloud service network responsible for transmitting locally computed training parameters through the use of cryptographic algorithms and distributed storage.A flexible and scalable cloud network is created by using a decentralized data aggregator instead of a centralized architecture which is prone to a single point of failure.Our experimental results show the decentralized algorithm with 10 cloud nodes is 5.2 s faster than the traditional centralized algorithm.Also,the recommendation algorithm based on longitudinal federated learning not only ensures the accuracy of the recommendation,but also fully mobilizes the idle sites to effectively improve the recommendation efficiency.关键词
油电混合电动汽车/推荐算法/纵向联邦学习/充电站推荐/云计算/区块链Key words
hybrid electric vehicle/recommendation algorithm/vertical federation learning/charging station recommendation/cloud computing/blockchain分类
动力与电气工程引用本文复制引用
蒋灵慧,冯霞,崔凯平,王亚茹..面向联邦学习的油电混合电动汽车站点推荐算法研究[J].重庆理工大学学报（自然科学版）,2025,39(3):113-119,7.基金项目
国家自然科学基金项目(62272203) （62272203）
