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

Wuwei Huang; Yang Yang; Mingzhe Chen; Chuanhong Liu; Chunyan Feng; H. Vincent Poor

doi:10.3390/e23111413

Entropy (Oct 2021)

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

Wuwei Huang,
Yang Yang,
Mingzhe Chen,
Chuanhong Liu,
Chunyan Feng,
H. Vincent Poor

Affiliations

Wuwei Huang: Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Yang Yang: Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Mingzhe Chen: Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA
Chuanhong Liu: Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Chunyan Feng: Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
H. Vincent Poor: Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA

DOI: https://doi.org/10.3390/e23111413
Journal volume & issue: Vol. 23, no. 11
p. 1413

Abstract

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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.

Published in Entropy

ISSN: 1099-4300 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science: Astronomy: Astrophysics; Science: Physics
Website: http://www.mdpi.com/journal/entropy

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