Zheng, Guhan and Ni, Qiang and Navaie, Keivan and Pervaiz, Haris and Min, Geyong and Kaushik, Aryan and Zarakovitis, Charilaos (2024) Mobility-Aware Split-Federated With Transfer Learning for Vehicular Semantic Communication Networks. IEEE Internet of Things Journal, 11 (10). pp. 17237-17248. DOI https://doi.org/10.1109/jiot.2024.3360230
Zheng, Guhan and Ni, Qiang and Navaie, Keivan and Pervaiz, Haris and Min, Geyong and Kaushik, Aryan and Zarakovitis, Charilaos (2024) Mobility-Aware Split-Federated With Transfer Learning for Vehicular Semantic Communication Networks. IEEE Internet of Things Journal, 11 (10). pp. 17237-17248. DOI https://doi.org/10.1109/jiot.2024.3360230
Zheng, Guhan and Ni, Qiang and Navaie, Keivan and Pervaiz, Haris and Min, Geyong and Kaushik, Aryan and Zarakovitis, Charilaos (2024) Mobility-Aware Split-Federated With Transfer Learning for Vehicular Semantic Communication Networks. IEEE Internet of Things Journal, 11 (10). pp. 17237-17248. DOI https://doi.org/10.1109/jiot.2024.3360230
Abstract
Machine learning-based semantic communication is a promising enabler for future-generation wireless network systems such as 6G networks. In practice, effective semantic communication requires online training for unknown content. In highly mobile vehicular networks, however, reliable, and efficient model training becomes significantly challenging. The existing distributed learning approaches are also unable to effectively operate in highly dynamic vehicular semantic communication networks. To address these challenges, we propose a novel mobility-aware split-federated with transfer learning (MSFTL) framework based on vehicle task offloading scenarios in this paper. To enable adaptation to the complex vehicle semantic communication, the proposed framework divides the training of the model into four parts and uses the proposed new splitfederated learning. Furthermore, to improve training efficiency, model accuracy, and the ability to adapt in highly mobile environments, we also present a new transfer learning approach integrated into the proposed framework. Particularly, we propose a high-mobility training resource optimisation mechanism based on a Stackelberg game for MSFTL to further reduce training costs and adapt vehicle mobility scenarios. We also investigate the performance of the proposed schemes through extensive simulations. The results validate the proposed approach and indicate its superiority compared to the conventional learning frameworks for semantic communication in vehicular networks.
Item Type: | Article |
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Uncontrolled Keywords: | Vehicle semantic communication networks; split federated learning; transfer learning; Stackelberg game; resource optimisation |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 15 Apr 2024 14:18 |
Last Modified: | 30 Oct 2024 21:38 |
URI: | http://repository.essex.ac.uk/id/eprint/38208 |
Available files
Filename: Author_accepted_final_version.pdf