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Privacy protection in intelligent vehicle networking: A novel federated learning algorithm based on information fusion
Nanjing University of Information Science and Technology, Nanjing, China.
Nanjing University of Information Science and Technology, Nanjing, China.
King Saud University, Riyadh, Saudi Arabia.ORCID iD: 0000-0002-9781-3969
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-2851-4260
2023 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 98, article id 101824Article in journal (Refereed) Published
Abstract [en]

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. © 2023 Elsevier B.V.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2023. Vol. 98, article id 101824
Keywords [en]
Connected cars, Differential privacy, Dynamic convolution, Federated learning, Information fusion, Personalization
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-51400DOI: 10.1016/j.inffus.2023.101824ISI: 001001456000001Scopus ID: 2-s2.0-85159115052OAI: oai:DiVA.org:hh-51400DiVA, id: diva2:1788033
Note

Funding: This work was supported by the National Natural Science Foundation of China (No. 61373131, 62071240), Open Foundation of State Key Laboratory of Networking and Switching Technology, China (Beijing University of Posts and Telecommunications) (SKLNST-2020-1-17), PAPD, China and CICAEET funds. The authors also acknowledge the Researchers Supporting Project number (RSP2023R34), King Saud University, Riyadh, Saudi Arabia .

Available from: 2023-08-15 Created: 2023-08-15 Last updated: 2023-08-16Bibliographically approved

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Tiwari, Prayag

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