Resource Allocation of NOMA Communication Systems for Federated Learning

Federated learning is a new communication and computing concept that allows naturally distributed data sets (e.g., as in data acquisition sensors) to be used to train global models, and therefore successfully addresses privacy, power and bandwidth limitations in wireless networks. In this paper, we...

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Vydané v:IEEE communications letters Ročník 27; číslo 8; s. 1
Hlavní autori: Poposka, Marija, Jovanovski, Borche, Rakovic, Valentin, Denkovski, Daniel, Hadzi-Velkov, Zoran
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Communications systems
Computational modeling
Data acquisition
Data models
Exact solutions
Federated learning
latency minimization
Machine learning
Mathematical models
Maximum power
Network latency
NOMA
non-orthogonal multiple access
Nonorthogonal multiple access
Numerical models
Optimization
Parameters
Resource allocation
Resource management
Uplink
wireless federated learning
Wireless networks
Wireless sensor networks
ISSN:1089-7798, 1558-2558
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Popis
Shrnutí:Federated learning is a new communication and computing concept that allows naturally distributed data sets (e.g., as in data acquisition sensors) to be used to train global models, and therefore successfully addresses privacy, power and bandwidth limitations in wireless networks. In this paper, we study the communications problem of latency minimization of a multi-user wireless network used to train a decentralized machine learning model. To facilitate low latency, the wireless stations (WSs) employ non-orthogonal multiple access (NOMA) for simultaneous transmission of local model parameters to the base station, subject to the users' maximum CPU frequency, maximum transmit power, and maximum available energy. The proposed resource allocation scheme guarantees fair resource sharing among WSs by enforcing only a single WS to spend the maximum allowable energy or transmit at maximum power, whereas the rest of the WSs transmit at lower power and spend less energy. The closed-form analytical solution for the optimal values of resource allocation parameters is used for efficient online implementation of the proposed scheme with low computational complexity.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2023.3286909