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A Novel Energy-Efficient FL Resource Allocation Scheme Based on NOMA

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Ruijie Li
Ruijie Li
Ruijie Li
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Guoping Zhang
Guoping Zhang
Guoping Zhang
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H. Xu at Central China Normal University
H. Xu
Yun Chen
Yun Chen
Yun Chen
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Abstract and Figures

Federated learning (FL) is an emerging artificial intelligence (AI) basic technology. It is essentially a distributed machine learning (ML) that allows the client to perform model training locally and then upload the trained model parameters to the server while leaving the original data locally, which guarantees the client’s privacy and significantly reduces communication pressure. This paper combines non-orthogonal multiple access (NOMA) for optimizing bandwidth allocation and FL to study a novel energy-efficient FL system which can effectively reduce energy consumption under the premise of ensuring user privacy. The considered model uses clustering for transmission between clients and the base station (BS). NOMA is used inside the cluster to transmit information to BS, and frequency division multiple access (FDMA) is used between the clusters to eliminate the interference between the user clusters caused by the clustering. We combine communication and computing design to minimize the system’s total energy consumption. Since the optimization problem is non-convex, it is first transformed into a Lagrangian function, and the original problem is divided into three sub-problems. Then the Karush–Kuhn–Tucker (KKT) conditions and Successive Convex Approximation (SCA) method are used to solve each sub-problem. Simulation analysis shows that our proposed novel energy-efficient FL method design has significantly improved the performance compared with other benchmarks.
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Wireless Personal Communications (2023) 132:2023–2040
https://doi.org/10.1007/s11277-023-10696-7
1 3
A Novel Energy‑Efficient FL Resource Allocation Scheme
Based onNOMA
RuijieLi1· GuopingZhang1· HongboXu1· YunChen1· XueChen1
Accepted: 22 July 2023 / Published online: 24 August 2023
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023
Abstract
Federated learning (FL) is an emerging artificial intelligence (AI) basic technology. It is
essentially a distributed machine learning (ML) that allows the client to perform model
training locally and then upload the trained model parameters to the server while leaving
the original data locally, which guarantees the client’s privacy and significantly reduces
communication pressure. This paper combines non-orthogonal multiple access (NOMA)
for optimizing bandwidth allocation and FL to study a novel energy-efficient FL system
which can effectively reduce energy consumption under the premise of ensuring user pri-
vacy. The considered model uses clustering for transmission between clients and the base
station (BS). NOMA is used inside the cluster to transmit information to BS, and frequency
division multiple access (FDMA) is used between the clusters to eliminate the interference
between the user clusters caused by the clustering. We combine communication and com-
puting design to minimize the system’s total energy consumption. Since the optimization
problem is non-convex, it is first transformed into a Lagrangian function, and the original
problem is divided into three sub-problems. Then the Karush–Kuhn–Tucker (KKT) condi-
tions and Successive Convex Approximation (SCA) method are used to solve each sub-
problem. Simulation analysis shows that our proposed novel energy-efficient FL method
design has significantly improved the performance compared with other benchmarks.
Keywords Non-orthogonal multiple access (NOMA)· Federated learning (FL)· Energy
efficiency (EE)· Resource allocation
1 Introduction
With the rapid development of the Internet era, the incredible growth of smartphones and
multimedia applications, and the exponential growth in the demand for mobile network
traffic. The intelligent era’s ultimate goal is to realize the Internet of Things (IoT) [1–3].
However, the proliferation of communication devices has put enormous strain on spectrum
* Guoping Zhang
gpzhang@ccnu.edu.cn
1 College ofPhysical Science andTechnology, Central China Normal University, Wuhan430079,
China
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... • Resource Allocation for CFL: Resource allocation plays a critical role in CFL-based systems, as it directly impacts communication efficiency, energy efficiency, model convergence speed, and overall system performance. By strategically managing resources like bandwidth, power, and computational capacity, CFL can operate more effectively in centralized networks [52][53][54][55][56][57]. Ref. [55] incorporated a simultaneous wireless information and power transfer (SWIPT) system with multicarrier NOMA to improve CFL energy efficiency. ...
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