1.
Chen, Tianrui; Zhang, Xinruo; You, Minglei; Zheng, Gan; Lambotharan, Sangarapillai
Federated Learning Enabled Link Scheduling in D2D Wireless Networks Journal Article
In: IEEE Wireless Communications Letters, vol. 13, no. 1, pp. 89–92, 2024, ISSN: 2162-2345.
Abstract | Links | BibTeX | Tags: Computational modeling, device-to-device (D2D), Device-to-device communication, Federated learning, link scheduling, Scheduling, Servers, Training, Wireless networks
@article{chen_federated_2024,
title = {Federated Learning Enabled Link Scheduling in D2D Wireless Networks},
author = {Tianrui Chen and Xinruo Zhang and Minglei You and Gan Zheng and Sangarapillai Lambotharan},
url = {https://ieeexplore.ieee.org/document/10268986},
doi = {10.1109/LWC.2023.3321500},
issn = {2162-2345},
year = {2024},
date = {2024-01-01},
urldate = {2025-10-08},
journal = {IEEE Wireless Communications Letters},
volume = {13},
number = {1},
pages = {89–92},
abstract = {Centralized machine learning methods for device-to-device (D2D) link scheduling may lead to a computing burden for a central server, transmission latency for decisions, and privacy issues for D2D communications. To mitigate these challenges, a federated learning (FL) based method is proposed to solve the link scheduling problem, where a global model is distributedly trained at local devices, and a server is used for aggregating model parameters instead of training samples. Specially, a more realistic scenario with limited channel state information (CSI) is considered instead of full CSI. Despite a decentralized implementation, simulation results demonstrate that the proposed FL based approach with limited CSI performs close to the conventional optimization algorithm. In addition, the FL based solution achieves almost the same performance as that of the centralized training.},
keywords = {Computational modeling, device-to-device (D2D), Device-to-device communication, Federated learning, link scheduling, Scheduling, Servers, Training, Wireless networks},
pubstate = {published},
tppubtype = {article}
}
Centralized machine learning methods for device-to-device (D2D) link scheduling may lead to a computing burden for a central server, transmission latency for decisions, and privacy issues for D2D communications. To mitigate these challenges, a federated learning (FL) based method is proposed to solve the link scheduling problem, where a global model is distributedly trained at local devices, and a server is used for aggregating model parameters instead of training samples. Specially, a more realistic scenario with limited channel state information (CSI) is considered instead of full CSI. Despite a decentralized implementation, simulation results demonstrate that the proposed FL based approach with limited CSI performs close to the conventional optimization algorithm. In addition, the FL based solution achieves almost the same performance as that of the centralized training.