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Secure and Reliable Transfer Learning Framework for 6G-Enabled Internet of Vehicles

Authors:

Dinh Thai Hoang,

Dong In KimAuthors Info & Claims

IEEE Wireless Communications, Volume 29, Issue 4

Pages 132 - 139

https://doi.org/10.1109/MWC.004.2100542

Published: 01 August 2022 Publication History

Abstract

In the coming 6G era, Internet of Vehicles (IoV) has been evolving towards 6G-enabled IoV with super-high data rate, seamless networking coverage, and ubiquitous intelligence by Artificial Intelligence (AI). Transfer Learning (TL) has great potential to empower promising 6G-enabled IoV, such as smart driving assistance, with its outstanding features including enhancing the quality and quantity of training data, speeding up learning processes, and reducing computing demands. Although TL had been widely adopted in wireless applications (e.g., spectrum management and caching), its reliability and security in 6G-enabled IoV were still not well investigated. For instance, malicious vehicles in source domains may transfer and share untrustworthy models (i.e., knowledge) about connection availability to target domains, thus adversely affecting the performance of learning processes. Therefore, it is important to select and also incentivize trustworthy vehicles to participate in TL. In this article, we first introduce the integration of TL and 6G-enabled IoV and provide TL applications for 6G-enabled IoV. We then design a secure and reliable transfer learning framework by using reputation to evaluate the reliability of pre-trained models and utilizing the consortium blockchain to achieve secure and efficient decentralized reputation management. Moreover, a deep learning-based auction scheme for the TL model market is designed to motivate high-reputation vehicles to participate in model sharing. Finally, the simulation results demonstrate that the proposed framework is secure and reliable with well-designed incentives for TL in 6G-enabled IoV.

References

[1]

B. Jiet al., “Survey on the Internet of Vehicles: Network Architectures and Applications,” IEEE Commun. Standards Mag., vol. 4, no. 1, Mar. 2020, pp. 34–41.

[2]

W. Saad, M. Bennis, and M. Chen, “A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems,” IEEE Network, vol. 34, no. 3, May/June 2020, pp. 134–42.

[3]

W. Zhanget al., “Optimizing Federated Learning in Distributed Industrial IoT: A Multiagent Approach,” IEEE JSAC, vol. 39, no. 12, 2021, pp. 3688–3703;.

[4]

X. Zhouet al., “Two-Layer Federated Learning With Heterogeneous Model Aggregation for 6G Supported Internet of Vehicles,” IEEE Trans. Vehicular Technology, vol. 70, no. 6, June 2021, pp. 5308–17.

[5]

C. T. Nguyenet al., “Transfer Learning for Future Wireless Networks: A Comprehensive Survey,” arXiv preprint arXiv:, 2021.

[6]

Q. Yanget al., Transfer Learning, Cambridge University Press, 2020.

[7]

M. Chenet al., “Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks,” IEEE Trans. Wireless Commun., vol. 19, no. 1, Jan. 2020, pp. 177–91.

[8]

Y. Luet al., “Low-Latency Federated Learning and Blockchain for Edge Association in Digital Twin Empowered 6G Networks,” IEEE Trans. Industrial Informatics, vol. 17, no. 7, July 2021, pp. 5098–5107.

[9]

N. Q. Hieuet al., “Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles With Joint Radar-Data Communications,” arXiv preprint arXiv:, 2021.

[10]

A. ul Haque, M. S. Ghani, and T. Mahmood, “Decentralized Transfer Learning Using Blockchain & IPFS for Deep Learning,” Proc. Int'l. Conf. Information Networking (ICOIN), Jan. 2020, pp. 170–77.

[11]

H. Linet al., “Toward Secure Data Fusion in Industrial IoT Using Transfer Learning,” IEEE Trans. Industrial Informatics, vol. 17, no. 10, Oct. 2021, pp. 7114–22.

[12]

J. Kanget al., “Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory,” IEEE Internet of Things J., vol. 6, no. 6, Dec. 2019, pp. 10700–14.

[13]

M. R. Parvez and K.-W. Chang, “Evaluating the Values of Sources in Transfer Learning,” Proc. 2021 Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, (Online), Association for Computational Linguistics, June 2021, pp. 5084–5116.

[14]

D. Niyatoet al., Auction Theory for Computer Networks, Cambridge University Press, 2020.

[15]

P. Düttinget al., “Optimal Auctions Through Deep Learning,” Proc. Int'l. Conf. Machine Learning, PMLR, May 2019, pp. 1706–15.

Cited By

Xu MNiyato DWright BZhang HKang JXiong ZMao SHan Z(2024)EPViSA: Efficient Auction Design for Real-Time Physical-Virtual Synchronization in the Human-Centric MetaverseIEEE Journal on Selected Areas in Communications10.1109/JSAC.2023.334538342:3(694-709)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/JSAC.2023.3345383
Xu MDu HNiyato DKang JXiong ZMao SHan ZJamalipour AKim DShen XLeung VPoor H(2024)Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC ServicesIEEE Communications Surveys & Tutorials10.1109/COMST.2024.335326526:2(1127-1170)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1109/COMST.2024.3353265
Patel AMurugan RMaddikunta PYenduri GJhaveri RZhu YGadekallu T(2024)AI-powered trustable and explainable fall detection system using transfer learningImage and Vision Computing10.1016/j.imavis.2024.105164149:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.imavis.2024.105164
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Secure and Reliable Transfer Learning Framework for 6G-Enabled Internet of Vehicles
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cover image IEEE Wireless Communications

IEEE Wireless Communications Volume 29, Issue 4

August 2022

150 pages

ISSN:1536-1284

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Copyright © 2022.

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IEEE Press

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Published: 01 August 2022

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Cited By

Xu MNiyato DWright BZhang HKang JXiong ZMao SHan Z(2024)EPViSA: Efficient Auction Design for Real-Time Physical-Virtual Synchronization in the Human-Centric MetaverseIEEE Journal on Selected Areas in Communications10.1109/JSAC.2023.334538342:3(694-709)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/JSAC.2023.3345383
Xu MDu HNiyato DKang JXiong ZMao SHan ZJamalipour AKim DShen XLeung VPoor H(2024)Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC ServicesIEEE Communications Surveys & Tutorials10.1109/COMST.2024.335326526:2(1127-1170)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1109/COMST.2024.3353265
Patel AMurugan RMaddikunta PYenduri GJhaveri RZhu YGadekallu T(2024)AI-powered trustable and explainable fall detection system using transfer learningImage and Vision Computing10.1016/j.imavis.2024.105164149:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.imavis.2024.105164
Sedjelmaci HKaaniche NTourki K(2024)Secure and Resilient 6 G RAN Networks: A Decentralized Approach with Zero Trust ArchitectureJournal of Network and Systems Management10.1007/s10922-024-09807-x32:2Online publication date: 13-Mar-2024
https://dl.acm.org/doi/10.1007/s10922-024-09807-x
Qin XMa TTang ZZhang XZhou HZhao L(2023)Service-Aware Resource Orchestration in Ultra-Dense LEO Satellite-Terrestrial Integrated 6G: A Service Function Chain ApproachIEEE Transactions on Wireless Communications10.1109/TWC.2023.323908022:9(6003-6017)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TWC.2023.3239080
Liu ZSun HMarine GWu H(2023)6G IoV Networks Driven by RF Digital Twin ModelingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.329253325:3(2976-2986)Online publication date: 11-Aug-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3292533
Xu MNg WLim WKang JXiong ZNiyato DYang QShen XMiao C(2023)A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and ChallengesIEEE Communications Surveys & Tutorials10.1109/COMST.2022.322111925:1(656-700)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/COMST.2022.3221119
Quan WXu ZLiu MCheng NLiu GGao DZhang HShen XZhuang W(2023)AI-Driven Packet Forwarding With Programmable Data Plane: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2022.321761325:1(762-790)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/COMST.2022.3217613

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