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Mobility-Aware Split-Federated With Transfer Learning for Vehicular Semantic Communication Networks

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Mobility-Aware Split-Federated With Transfer Learning for Vehicular Semantic Communication Networks. / Zheng, Guhan; Ni, Qiang; Navaie, Keivan et al.
In: IEEE Internet of Things Journal, Vol. 11, No. 10, 10, 15.05.2024, p. 17237-17248.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Zheng G, Ni Q, Navaie K, Pervaiz H, Min G, Kaushik A et al. Mobility-Aware Split-Federated With Transfer Learning for Vehicular Semantic Communication Networks. IEEE Internet of Things Journal. 2024 May 15;11(10):17237-17248. 10. Epub 2024 Jan 30. doi: 10.1109/jiot.2024.3360230

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Bibtex

@article{1b293478c79f470090db0838278e6566,
title = "Mobility-Aware Split-Federated With Transfer Learning for Vehicular Semantic Communication Networks",
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.",
keywords = "Computer Networks and Communications, Computer Science Applications, Hardware and Architecture, Information Systems, Signal Processing",
author = "Guhan Zheng and Qiang Ni and Keivan Navaie and Haris Pervaiz and Geyong Min and Aryan Kaushik and Charilaos Zarakovitis",
year = "2024",
month = may,
day = "15",
doi = "10.1109/jiot.2024.3360230",
language = "English",
volume = "11",
pages = "17237--17248",
journal = "IEEE Internet of Things Journal",
issn = "2327-4662",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "10",

}

RIS

TY - JOUR

T1 - Mobility-Aware Split-Federated With Transfer Learning for Vehicular Semantic Communication Networks

AU - Zheng, Guhan

AU - Ni, Qiang

AU - Navaie, Keivan

AU - Pervaiz, Haris

AU - Min, Geyong

AU - Kaushik, Aryan

AU - Zarakovitis, Charilaos

PY - 2024/5/15

Y1 - 2024/5/15

N2 - 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.

AB - 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.

KW - Computer Networks and Communications

KW - Computer Science Applications

KW - Hardware and Architecture

KW - Information Systems

KW - Signal Processing

U2 - 10.1109/jiot.2024.3360230

DO - 10.1109/jiot.2024.3360230

M3 - Journal article

VL - 11

SP - 17237

EP - 17248

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

SN - 2327-4662

IS - 10

M1 - 10

ER -