A Novel Hybrid NOMA-TDMA Scheme for Wireless Federated Learning Networks

To address the straggler problem in wireless federated learning (WFL) networks, a novel hybrid non-orthogonal multiple access (NOMA) - time division multiple access (TDMA) scheme is proposed, where not only local model training and uploading of different users are performed in parallel, but also the...

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Vydáno v:IEEE transactions on vehicular technology Ročník 74; číslo 5; s. 8448 - 8453
Hlavní autor: Xu, Ding
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.05.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Broadcasting
Computational modeling
Data models
Decoding
Energy consumption
Federated learning
Interference cancellation
latency
Network latency
NOMA
non-orthogonal multiple access (NOMA)
Nonorthogonal multiple access
Optimization
Resource management
Robot learning
Simulation
Time Division Multiple Access
time division multiple access (TDMA)
Training
Wireless federated learning (WFL)
Wireless networks
ISSN:0018-9545, 1939-9359
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Abstract To address the straggler problem in wireless federated learning (WFL) networks, a novel hybrid non-orthogonal multiple access (NOMA) - time division multiple access (TDMA) scheme is proposed, where not only local model training and uploading of different users are performed in parallel, but also the model uploading of different users is performed in a hybrid NOMA-TDMA manner. Particularly, users that complete their local training form dynamic NOMA groups across different time slots in a TDMA fashion to upload their models efficiently. The latency per training round minimization problem is formulated to jointly optimize the user scheduling, time allocation, computing frequency allocation, and energy allocation. An efficient algorithm, combining one-dimensional search and successive convex approximation, is developed. Simulation results demonstrate that the proposed scheme can outperform the state-of-the-art NOMA and TDMA schemes under various system parameter setups.
AbstractList To address the straggler problem in wireless federated learning (WFL) networks, a novel hybrid non-orthogonal multiple access (NOMA) - time division multiple access (TDMA) scheme is proposed, where not only local model training and uploading of different users are performed in parallel, but also the model uploading of different users is performed in a hybrid NOMA-TDMA manner. Particularly, users that complete their local training form dynamic NOMA groups across different time slots in a TDMA fashion to upload their models efficiently. The latency per training round minimization problem is formulated to jointly optimize the user scheduling, time allocation, computing frequency allocation, and energy allocation. An efficient algorithm, combining one-dimensional search and successive convex approximation, is developed. Simulation results demonstrate that the proposed scheme can outperform the state-of-the-art NOMA and TDMA schemes under various system parameter setups.
Author Xu, Ding
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SubjectTerms Algorithms
Broadcasting
Computational modeling
Data models
Decoding
Energy consumption
Federated learning
Interference cancellation
latency
Network latency
NOMA
non-orthogonal multiple access (NOMA)
Nonorthogonal multiple access
Optimization
Resource management
Robot learning
Simulation
Time Division Multiple Access
time division multiple access (TDMA)
Training
Wireless federated learning (WFL)
Wireless networks
Title A Novel Hybrid NOMA-TDMA Scheme for Wireless Federated Learning Networks
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