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Lagrange coded federated learning (L-CoFL) model for Internet of Vehicles

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conference contribution
posted on 2024-10-16, 15:35 authored by Weiquan Ni, Shaoliang Zhu, Md Monjurul Karim, Alia AsheralievaAlia Asheralieva, Jiawen Kang, Zehui Xiong, Carsten Maple
In Internet-of-Vehicles (IoV), smart vehicles can efficiently process various sensing data through federated learning (FL) - a privacy-preserving distributed machine learning (ML) approach that allows collaborative development of the shared ML model without any data exchange. However, traditional FL approaches suffer from poor security against the system noise, e.g., due to low-quality trained data, wireless channel errors, and malicious vehicles generating erroneous results, which affects the accuracy of the developed ML model. To address this problem, we propose a novel FL model based on the concept of Lagrange coded computing (LCC) - a coded distributed computing (CDC) scheme that enables enhancing the system security. In particular, we design the first L-CoFL (Lagrange coded FL) model to improve the accuracy of FL computations in the presence of lowquality trained data and wireless channel errors, and guarantee the system security against malicious vehicles. We apply the proposed L-CoFL model to predict the traffic slowness in IoV and verify the superior performance of our model through extensive simulations.

Funding

Academic Centre of Excellence in Cyber Security Research - University of Warwick

Engineering and Physical Sciences Research Council

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The Alan Turing Institute

Engineering and Physical Sciences Research Council

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PETRAS 2

UK Research and Innovation

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AutoTrust: Designing a Human-Centered Trusted, Secure, Intelligent and Usable Internet of Vehicles

UK Research and Innovation

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Guangdong Provincial Department of Education, Characteristic Innovation Project No. 2021KTSCX110

NSFC: grant no. 62102099

Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China . grant no. ICT2022B12

SUTD: grant no. SRG-ISTD-2021-165

SUTD-ZJU IDEA grant (SUTD-ZJU (VP) 202102)

SUTD-ZJU IDEA Seed grant (SUTD-ZJU (SD) 202101)

History

School

  • Science

Department

  • Computer Science

Published in

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)

Volume

2022

Pages

864 - 872

Source

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)

Publisher

IEEE

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publication date

2022-10-13

Copyright date

2022

ISBN

9781665471770; 9781665471787

ISSN

1063-6927

eISSN

2575-8411

Language

  • en

Event dates

10th July 2022 - 13th July 2022

Depositor

Dr Alia Asheralieva. Deposit date: 29 May 2024

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