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Charging Station Recommendation for Electric Vehicle Based on Federated Learning
Open accessAbstract
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.
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“A cross-platform FL framework in [150] combined recommendation models with encryption techniques to balance privacy and real-time prediction accuracy.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [150]
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Research works printed but not linked: 10
- Bu, 2017, Recommendation method for electric vehicle charging based on collaborative filtering, Science & Technology Review, 35, 61
- Yan, 2015, Optimal Charging Route Recommendation Method Based on Transportation and Distribution Information, Proceedings of the CSEE, 35, 310
- Zhang, 2014, The Forecasting Method of Electric Vehicle Charging Load Considering Temporal and Spatial Distribution, Automation of Electric Power Systems, 38, 13
- Su, 2014, Charging Route Planning for Electric Vehicles Considering Real-time Dynamic Energy Consumption, Automation of Electric Systems, 38, 13
- Cao, 2019, A Decentralized Deadline-Driven Electric Vehicle Charging Recommendation, IEEE Systems Journal, 13, 3410, 10.1109/JSYST.2018.2851140 · DOI
- Wen, 2012, Decentralized plug-in electric vehicle charging selection algorithm in power systems, IEEE Transactions on Smart Grid, 3, 1779, 10.1109/TSG.2012.2217761 · DOI
- Yang, 2019, Federated machine learning: Concept and applications, ACM Transactions on Intelligent Systems and Technology (TIST), 10, 1, 10.1145/3339474 · DOI
- Liu, 2020, Survey on Privacy-Preserving Machine Learning, Journal of Computer Research and Development, 57, 346
- Jia, 2020, The Application of Federated Learning Model in Confidential Data Processing, Journal of CAEIT, 15, 43
- Zhao, 2019, A Summary of Research on Factorization Machine Model, Journal of Software, 3, 19
Cited by
Sorted by citations
Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 9
Metadata
- Authors
- Wang, X., Zheng, X., & Liang, X.
- Affiliations
- Global Energy Interconnection Research Institute (China) (CN)
- Corresponding author
- Wang, Xiaohui
- Venue
- Journal of Physics: Conference Series
- Publisher
- IOP Publishing
- Published
- 2021-02-01
- Volume
- 1792
- Issue
- 1
- Pages
- 012055
- ISSN
- 1742-6588 (ISSN-L)
- 1742-6596
- OpenAlex
- W3130033995
- Cited by
- 15
- References
- 0
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 7 · 2023: 3 · 2022: 1 · 2021: 1
- FWCI
- 4.71
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Journal of Physics Conference Series (publishedVersion, cc-by)
- Funders
- State Grid Corporation of China (5418-201958524A-0-0-00)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- federated learning, user privacy protection
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