RIS-Assisted Federated Learning Algorithm Based on Device Selection and Weighted Averaging
2024 (English)In: 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING, Institute of Electrical and Electronics Engineers (IEEE) , 2024Conference paper, Published paper (Refereed)
Abstract [en]
To protect user privacy and improve the transmitting environment of wireless communication, federated learning (FL) and reconfigurable intelligent surface (RIS) are proposed as promising technologies for future communication. Meanwhile, studies have proved that the combination of FL and RIS guarantees better performance for system models. However, the combined model still has problems such as high communication overhead and slow convergence speed. Therefore, in this paper, we proposed a channel quality based device selection and weighted averaging algorithm in a RIS-assisted federated learning model. Simulation results proved that the proposed algorithm outperforms the classic federated averaging (FedAvg) algorithm in convergence speed, test accuracy, and training loss.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2024.
Series
IEEE Vehicular Technology Conference VTC, ISSN 1090-3038, E-ISSN 2577-2465
Keywords [en]
federated learning, reconfigurable intelligent surface, device selection, weighted averaging
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-358635DOI: 10.1109/VTC2024-SPRING62846.2024.10683452ISI: 001327706002098Scopus ID: 2-s2.0-85206130143OAI: oai:DiVA.org:kth-358635DiVA, id: diva2:1929357
Conference
IEEE 99th Vehicular Technology Conference (VTC-Spring), JUN 24-27, 2024, Singapore, SINGAPORE
Note
Part of ISBN 979-8-3503-8741-4
QC 20250120
2025-01-202025-01-202025-01-20Bibliographically approved