Hybrid NOMA for Latency Minimization in Wireless Federated Learning for 6G Networks
Loading...
Date
2023-12
Authors
Kavitha, P.
Kavitha, K.
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Společnost pro radioelektronické inženýrství
Altmetrics
Abstract
Wireless Federated Learning (WFL) is an innovative machine learning paradigm enabling distributed devices to collaboratively learn without sharing raw data. WFL is particularly useful for mobile devices that generate massive amounts of data but have limited resources for training complex models. This paper highlights the significance of reducing delay for efficient WFL implementation through advanced multiple access protocols and joint optimization of communication and computing resources. We propose optimizing the WFL Compute-then-Transmit (CT) protocol using hybrid Non-Orthogonal Multiple Access (H-NOMA). To minimize and optimize latency for the transmission of local training data, we use the Successive Convex Optimization (SCA) method, which efficiently reduces the complexity of non-convex algorithms. Finally, the numerical results verify the effectiveness of H-NOMA in terms of delay reduction, compared to the benchmark that is based on Non-Orthogonal Multiple Acces (NOMA).
Description
Keywords
Citation
Radioengineering. 2023 vol. 32, č. 4, s. 594-602. ISSN 1210-2512
https://www.radioeng.cz/fulltexts/2023/23_04_0594_0602.pdf
https://www.radioeng.cz/fulltexts/2023/23_04_0594_0602.pdf
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en