Federated Learning Enabled Link Scheduling in D2D Wireless Networks

Chen, Tianrui; Zhang, Xinruo; You, Minglei; Zheng, Gan; Lambotharan, Sangarapillai

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.

Citation

Chen, T., Zhang, X., You, M., Zheng, G., & Lambotharan, S. (2024). Federated Learning Enabled Link Scheduling in D2D Wireless Networks. IEEE Wireless Communications Letters, 13(1), 89-92. https://doi.org/10.1109/LWC.2023.3321500

• Journal Article Type: Article
• Acceptance Date: Sep 25, 2023
• Online Publication Date: Oct 2, 2023
• Publication Date: 2024-01
• Deposit Date: Sep 28, 2023
• Publicly Available Date: Oct 3, 2023
• Journal: IEEE Wireless Communications Letters
• Print ISSN: 2162-2337
• Electronic ISSN: 2162-2345
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 1
• Pages: 89-92
• DOI: https://doi.org/10.1109/LWC.2023.3321500
• Keywords: Device-to-device communication , Training , Servers , Wireless networks , Scheduling , Computational modeling , Federated learning, Federated learning , Device-to-device (D2D) , Link scheduling
• Public URL: https://nottingham-repository.worktribe.com/output/25385748
• Publisher URL: https://ieeexplore.ieee.org/document/10268986