Mobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things

The rapid growth of the Internet of Medical Things (IoMT) has led to the ubiquitous home health diagnostic network. Excessive demand from patients leads to high cost, low latency, and communication overload. However, in the process of parameter updating, the communication cost of the system or netwo...

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Autores principales:	Xiao Zheng, Syed Bilal Hussain Shah, Xiaojun Ren, Fengqi Li, Liqaa Nawaf, Chinmay Chakraborty, Muhammad Fayaz
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/6a6250d046be4736a452c19e6c3cc59c
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id	oai:doaj.org-article:6a6250d046be4736a452c19e6c3cc59c
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spelling	oai:doaj.org-article:6a6250d046be4736a452c19e6c3cc59c2021-11-08T02:36:46ZMobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things1530-867710.1155/2021/4410894https://doaj.org/article/6a6250d046be4736a452c19e6c3cc59c2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4410894https://doaj.org/toc/1530-8677The rapid growth of the Internet of Medical Things (IoMT) has led to the ubiquitous home health diagnostic network. Excessive demand from patients leads to high cost, low latency, and communication overload. However, in the process of parameter updating, the communication cost of the system or network becomes very large due to iteration and many participants. Although edge computing can reduce latency to some extent, there are significant challenges in further reducing system latency. Federated learning is an emerging paradigm that has recently attracted great interest in academia and industry. The basic idea is to train a globally optimal machine learning model among all participating collaborators. In this paper, a gradient reduction algorithm based on federated random variance is proposed to reduce the number of iterations between the participant and the server from the perspective of the system while ensuring the accuracy, and the corresponding convergence analysis is given. Finally, the method is verified by linear regression and logistic regression. Experimental results show that the proposed method can significantly reduce the communication cost compared with the general stochastic gradient descent federated learning.Xiao ZhengSyed Bilal Hussain ShahXiaojun RenFengqi LiLiqaa NawafChinmay ChakrabortyMuhammad FayazHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 Xiao Zheng Syed Bilal Hussain Shah Xiaojun Ren Fengqi Li Liqaa Nawaf Chinmay Chakraborty Muhammad Fayaz Mobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things
description	The rapid growth of the Internet of Medical Things (IoMT) has led to the ubiquitous home health diagnostic network. Excessive demand from patients leads to high cost, low latency, and communication overload. However, in the process of parameter updating, the communication cost of the system or network becomes very large due to iteration and many participants. Although edge computing can reduce latency to some extent, there are significant challenges in further reducing system latency. Federated learning is an emerging paradigm that has recently attracted great interest in academia and industry. The basic idea is to train a globally optimal machine learning model among all participating collaborators. In this paper, a gradient reduction algorithm based on federated random variance is proposed to reduce the number of iterations between the participant and the server from the perspective of the system while ensuring the accuracy, and the corresponding convergence analysis is given. Finally, the method is verified by linear regression and logistic regression. Experimental results show that the proposed method can significantly reduce the communication cost compared with the general stochastic gradient descent federated learning.
format	article
author	Xiao Zheng Syed Bilal Hussain Shah Xiaojun Ren Fengqi Li Liqaa Nawaf Chinmay Chakraborty Muhammad Fayaz
author_facet	Xiao Zheng Syed Bilal Hussain Shah Xiaojun Ren Fengqi Li Liqaa Nawaf Chinmay Chakraborty Muhammad Fayaz
author_sort	Xiao Zheng
title	Mobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things
title_short	Mobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things
title_full	Mobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things
title_fullStr	Mobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things
title_full_unstemmed	Mobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things
title_sort	mobile edge computing enabled efficient communication based on federated learning in internet of medical things
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/6a6250d046be4736a452c19e6c3cc59c
work_keys_str_mv	AT xiaozheng mobileedgecomputingenabledefficientcommunicationbasedonfederatedlearningininternetofmedicalthings AT syedbilalhussainshah mobileedgecomputingenabledefficientcommunicationbasedonfederatedlearningininternetofmedicalthings AT xiaojunren mobileedgecomputingenabledefficientcommunicationbasedonfederatedlearningininternetofmedicalthings AT fengqili mobileedgecomputingenabledefficientcommunicationbasedonfederatedlearningininternetofmedicalthings AT liqaanawaf mobileedgecomputingenabledefficientcommunicationbasedonfederatedlearningininternetofmedicalthings AT chinmaychakraborty mobileedgecomputingenabledefficientcommunicationbasedonfederatedlearningininternetofmedicalthings AT muhammadfayaz mobileedgecomputingenabledefficientcommunicationbasedonfederatedlearningininternetofmedicalthings
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Mobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things

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