Reference : Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Veh...
Scientific journals : Article
Engineering, computing & technology : Computer science
Security, Reliability and Trust
Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networks
Saputra, Yuris Mulya []
Nguyen, Diep N. []
Hoang, Dinh Thai []
Vu, Thang Xuan []
Dutkiewicz, Eryk []
Chatzinotas, Symeon mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
IEEE Transactions on Mobile Computing
Institute of Electrical and Electronics Engineers
Yes (verified by ORBilu)
United States
[en] In this paper, we propose novel approaches using state-of-the-art machine learning techniques, aiming at predicting energy demand for electric vehicle (EV) networks. These methods can learn and find the correlation of complex hidden features to improve the prediction accuracy. First, we propose an energy demand learning (EDL)-based prediction solution in which a charging station provider (CSP) gathers information from all charging stations (CSs) and then performs the EDL algorithm to predict the energy demand for the considered area. However, this approach requires frequent data sharing between the CSs and the CSP, thereby driving communication overhead and privacy issues for the EVs and CSs. To address this problem, we propose a federated energy demand learning (FEDL) approach which allows the CSs sharing their information without revealing real datasets. Specifically, the CSs only need to send their trained models to the CSP for processing. In this case, we can significantly reduce the communication overhead and effectively protect data privacy for the EV users. To further improve the effectiveness of the FEDL, we then introduce a novel clustering-based EDL approach for EV networks by grouping the CSs into clusters before applying the EDL algorithms. Through experimental results, we show that our proposed approaches can improve the accuracy of energy demand prediction up to 24.63 baseline machine learning algorithms.

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