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Mobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things.pdf (799.66 kB)

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

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journal contribution
posted on 2023-05-16, 14:53 authored by Xiao Zheng, Syed Bilal Hussain Shah, Xiaojun Ren, Fengqi Li, Liqaa NawafLiqaa Nawaf, Chinmay Chakraborty, Muhammad Fayaz

 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. 

Funding

Shandong National Science Foundation of China (Grant No. ZR202103040468)

History

Published in

Wireless Communications and Mobile Computing

Publisher

Hindawi / Wiley

Version

  • VoR (Version of Record)

Citation

Zheng, X., Shah, S.B.H., Ren, X., Li, F., Nawaf, L., Chakraborty, C. and Fayaz, M. (2021) 'Mobile edge computing enabled efficient communication based on federated learning in internet of medical things', Wireless Communications and Mobile Computing, 2021, pp.1-10. doi: 10.1155/2021/4410894

Print ISSN

1530-8669

Electronic ISSN

1530-8677

Cardiff Met Affiliation

  • Cardiff School of Technologies

Cardiff Met Authors

Liqaa Nawaf

Copyright Holder

  • © The Authors

Language

  • en

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