Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems

In this paper, the optimization of network performance to support the deployment of federated learning (FL) is investigated. In particular, in the considered model, each user owns a machine learning (ML) model by training through its own dataset, and then transmits its ML parameters to a base statio...

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Autores principales:	Wuwei Huang, Yang Yang, Mingzhe Chen, Chuanhong Liu, Chunyan Feng, H. Vincent Poor
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	federated learning model compression visible light communication Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/5cab96973b9248caa4e8c5f802714232
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spelling	oai:doaj.org-article:5cab96973b9248caa4e8c5f8027142322021-11-25T17:29:28ZWireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems10.3390/e231114131099-4300https://doaj.org/article/5cab96973b9248caa4e8c5f8027142322021-10-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1413https://doaj.org/toc/1099-4300In this paper, the optimization of network performance to support the deployment of federated learning (FL) is investigated. In particular, in the considered model, each user owns a machine learning (ML) model by training through its own dataset, and then transmits its ML parameters to a base station (BS) which aggregates the ML parameters to obtain a global ML model and transmits it to each user. Due to limited radio frequency (RF) resources, the number of users that participate in FL is restricted. Meanwhile, each user uploading and downloading the FL parameters may increase communication costs thus reducing the number of participating users. To this end, we propose to introduce visible light communication (VLC) as a supplement to RF and use compression methods to reduce the resources needed to transmit FL parameters over wireless links so as to further improve the communication efficiency and simultaneously optimize wireless network through user selection and resource allocation. This user selection and bandwidth allocation problem is formulated as an optimization problem whose goal is to minimize the training loss of FL. We first use a model compression method to reduce the size of FL model parameters that are transmitted over wireless links. Then, the optimization problem is separated into two subproblems. The first subproblem is a user selection problem with a given bandwidth allocation, which is solved by a traversal algorithm. The second subproblem is a bandwidth allocation problem with a given user selection, which is solved by a numerical method. The ultimate user selection and bandwidth allocation are obtained by iteratively compressing the model and solving these two subproblems. Simulation results show that the proposed FL algorithm can improve the accuracy of object recognition by up to 16.7% and improve the number of selected users by up to 68.7%, compared to a conventional FL algorithm using only RF.Wuwei HuangYang YangMingzhe ChenChuanhong LiuChunyan FengH. Vincent PoorMDPI AGarticlefederated learningmodel compressionvisible light communicationScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1413, p 1413 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	federated learning model compression visible light communication Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	federated learning model compression visible light communication Science Q Astrophysics QB460-466 Physics QC1-999 Wuwei Huang Yang Yang Mingzhe Chen Chuanhong Liu Chunyan Feng H. Vincent Poor Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems
description	In this paper, the optimization of network performance to support the deployment of federated learning (FL) is investigated. In particular, in the considered model, each user owns a machine learning (ML) model by training through its own dataset, and then transmits its ML parameters to a base station (BS) which aggregates the ML parameters to obtain a global ML model and transmits it to each user. Due to limited radio frequency (RF) resources, the number of users that participate in FL is restricted. Meanwhile, each user uploading and downloading the FL parameters may increase communication costs thus reducing the number of participating users. To this end, we propose to introduce visible light communication (VLC) as a supplement to RF and use compression methods to reduce the resources needed to transmit FL parameters over wireless links so as to further improve the communication efficiency and simultaneously optimize wireless network through user selection and resource allocation. This user selection and bandwidth allocation problem is formulated as an optimization problem whose goal is to minimize the training loss of FL. We first use a model compression method to reduce the size of FL model parameters that are transmitted over wireless links. Then, the optimization problem is separated into two subproblems. The first subproblem is a user selection problem with a given bandwidth allocation, which is solved by a traversal algorithm. The second subproblem is a bandwidth allocation problem with a given user selection, which is solved by a numerical method. The ultimate user selection and bandwidth allocation are obtained by iteratively compressing the model and solving these two subproblems. Simulation results show that the proposed FL algorithm can improve the accuracy of object recognition by up to 16.7% and improve the number of selected users by up to 68.7%, compared to a conventional FL algorithm using only RF.
format	article
author	Wuwei Huang Yang Yang Mingzhe Chen Chuanhong Liu Chunyan Feng H. Vincent Poor
author_facet	Wuwei Huang Yang Yang Mingzhe Chen Chuanhong Liu Chunyan Feng H. Vincent Poor
author_sort	Wuwei Huang
title	Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems
title_short	Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems
title_full	Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems
title_fullStr	Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems
title_full_unstemmed	Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems
title_sort	wireless network optimization for federated learning with model compression in hybrid vlc/rf systems
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5cab96973b9248caa4e8c5f802714232
work_keys_str_mv	AT wuweihuang wirelessnetworkoptimizationforfederatedlearningwithmodelcompressioninhybridvlcrfsystems AT yangyang wirelessnetworkoptimizationforfederatedlearningwithmodelcompressioninhybridvlcrfsystems AT mingzhechen wirelessnetworkoptimizationforfederatedlearningwithmodelcompressioninhybridvlcrfsystems AT chuanhongliu wirelessnetworkoptimizationforfederatedlearningwithmodelcompressioninhybridvlcrfsystems AT chunyanfeng wirelessnetworkoptimizationforfederatedlearningwithmodelcompressioninhybridvlcrfsystems AT hvincentpoor wirelessnetworkoptimizationforfederatedlearningwithmodelcompressioninhybridvlcrfsystems
_version_	1718412285137911808

Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems

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