Federated learning enabled link scheduling in D2D wireless networks
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.
Pervasive Wireless Intelligence Beyond the Generations (PerCom)
Engineering and Physical Sciences Research CouncilFind out more...
UK EPSRC under grant number EP/X04047X/1
Royal Society IECnNSFCn223152 and IECnNSFCn211209
ESA Project 12893212
Digital Nottingham Project
- Loughborough University London
Published inIEEE Wireless Communications Letters
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
- AM (Accepted Manuscript)
Rights holder© IEEE
Publisher statementFor the purpose of open access, the author(s) has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.