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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
Secure-Enhanced Federated Learning for AI-Empowered Electric Vehicle Energy Prediction
IEEE Consumer Electronics Magazine, Volume 12, No. 2, Year 2023
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Description
Although AI-empowered schemes bring some sound solutions to stimulate more reasonable energy distribution schemes between charging stations (CSs) and CS providers, frequent data sharing between them is possible to incur many security and privacy breaches. To solve these problems, federated learning (FL) is an ideal solution that only requires CSs to upload local models instead of detailed data. Although the CSs' electricity consumption need not to be exposed to the server directly, FL-based schemes still have been excavated several security threats such as information exploiting attacks, data poisoning attacks, model poisoning attacks, and free-riding attacks. Hence, in this article, both the effectiveness of energy management and the potential risks of FL for electric vehicle infrastructures (EVIs) are considered, we propose a lightweight authentication FL-based energy demand prediction for EVIs with premium-penalty mechanism. Security analysis and performance evaluation prove that our proposed framework can generate an accurate electricity demand prediction framework to defend multiple FL attacks for EVIs. © 2021 IEEE.
Authors & Co-Authors
Wang, Weizheng
Hong Kong, Hong Kong
City University of Hong Kong
Memon, Fida Hussain
Pakistan, Sukkur
Sukkur Iba University
South Korea, Jeju
Jeju National University
Lian, Zhuotao
Japan, Aizuwakamatsu
The University of Aizu
Yin, Zhimeng
Hong Kong, Hong Kong
City University of Hong Kong
Gadekallu, Thippa Reddy
India, Vellore
Vellore Institute of Technology
Pham, Quoc Viet
South Korea, Busan
Pusan National University
Dev, Kapal
Unknown Affiliation
Su, Chunhua
Japan, Aizuwakamatsu
The University of Aizu
Statistics
Citations: 45
Authors: 8
Affiliations: 6
Identifiers
Doi:
10.1109/MCE.2021.3116917
ISSN:
21622248