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Secure and Reliable Transfer Learning Framework for 6G-enabled Internet of Vehicles by Xu, Minrui; Hoang, Dinh Thai; Kang, Jiawen; Niyato, Dusit; Yan, Qiang; Kim, Dong In is a scholarly article available to read on EtoBox.

What is Secure and Reliable Transfer Learning Framework for 6G-enabled Internet of Vehicles about?

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 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-enbaled IoV and provide TL applications for 6G-enabled IoV. We then design a secure and reliable transfer learning framework by using reputation to eva

Author

Xu, Minrui; Hoang, Dinh Thai; Kang, Jiawen; Niyato, Dusit; Yan, Qiang; Kim, Dong In

Published

2021

Language

EN