Secure-Enhanced Federated Learning for AI-Empowered Electric Vehicle Energy Prediction

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

47 Scopus Citations
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Author(s)

  • Fida Hussain Memon
  • Zhuotao Lian
  • Thippa Reddy Gadekallu
  • Quoc-Viet Pham
  • Kapal Dev
  • Chunhua Su

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)27-34
Journal / PublicationIEEE Consumer Electronics Magazine
Volume12
Issue number2
Online published30 Sept 2021
Publication statusPublished - Mar 2023

Abstract

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.

Research Area(s)

  • Authentication, Charging stations, Computational modeling, Data models, Predictive models, Security, Training

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Secure-Enhanced Federated Learning for AI-Empowered Electric Vehicle Energy Prediction. / Wang, Weizheng; Memon, Fida Hussain; Lian, Zhuotao et al.
In: IEEE Consumer Electronics Magazine, Vol. 12, No. 2, 03.2023, p. 27-34.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review