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research-article

Privacy protection in intelligent vehicle networking: : A novel federated learning algorithm based on information fusion

Authors:

Ghulam Muhammad,

Prayag TiwariAuthors Info & Claims

Volume 98, Issue C

https://doi.org/10.1016/j.inffus.2023.101824

Published: 01 October 2023 Publication History

Abstract

Federated learning is an effective technique to solve the problem of information fusion and information sharing in intelligent vehicle networking. However, most of the existing federated learning algorithms generally have the risk of privacy leakage. To address this security risk, this paper proposes a novel personalized federated learning with privacy preservation (PDP-PFL) algorithm based on information fusion. In the first stage of its execution, the new algorithm achieves personalized privacy protection by grading users’ privacy based on their privacy preferences and adding noise that satisfies their privacy preferences. In the second stage of its execution, PDP-PFL performs collaborative training of deep models among different in-vehicle terminals for personalized learning, using a lightweight dynamic convolutional network architecture without sharing the local data of each terminal. Instead of sharing all the parameters of the model as in standard federated learning, PDP-PFL keeps the last layer local, thus adding another layer of data confidentiality and making it difficult for the adversary to infer the image of the target vehicle terminal. It trains a personalized model for each vehicle terminal by “local fine-tuning”. Based on experiments, it is shown that the accuracy of the proposed new algorithm for PDP-PFL calculation can be comparable to or better than that of the FedAvg algorithm and the FedBN algorithm, while further enhancing the protection of data privacy.

Highlights

•

A new personalized differential privacy algorithm (PDP) is proposed.

•

A new Personalized Federated Learning (PFL) with PDP based on information fusion is proposed.

•

The PDP uses a lightweight dynamic convolutional network for greater practicability.

•

It provides balance and effective use of computing load among various in-vehicle terminals.

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Cited By

Chen JYan HLiu ZZhang MXiong HYu S(2024)When Federated Learning Meets Privacy-Preserving ComputationACM Computing Surveys10.1145/367901356:12(1-36)Online publication date: 22-Jul-2024
https://dl.acm.org/doi/10.1145/3679013
Wu LLiu ZPu BWei KCao HYao S(2024)DGGIInformation Fusion10.1016/j.inffus.2024.102620113:COnline publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102620
Zhou FLiu SFujita HHu XZhang YWang BWang K(2024)Fault diagnosis based on federated learning driven by dynamic expansion for model layers of imbalanced clientExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121982238:PDOnline publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121982

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Information & Contributors

Information

Published In

cover image Information Fusion

Information Fusion Volume 98, Issue C

Oct 2023

286 pages

ISSN:1566-2535

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 October 2023

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Cited By

Chen JYan HLiu ZZhang MXiong HYu S(2024)When Federated Learning Meets Privacy-Preserving ComputationACM Computing Surveys10.1145/367901356:12(1-36)Online publication date: 22-Jul-2024
https://dl.acm.org/doi/10.1145/3679013
Wu LLiu ZPu BWei KCao HYao S(2024)DGGIInformation Fusion10.1016/j.inffus.2024.102620113:COnline publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102620
Zhou FLiu SFujita HHu XZhang YWang BWang K(2024)Fault diagnosis based on federated learning driven by dynamic expansion for model layers of imbalanced clientExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121982238:PDOnline publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121982

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