Federated learning enables intelligent reflecting surface in fog-cloud enabled cellular network

The intelligent reflecting surface (IRS) is a ground-breaking technology that can boost the efficiency of wireless data transmission systems. Specifically, the wireless signal transmitting environment is reconfigured by adjusting a large number of small reflecting units simultaneously. Therefore, in...

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Autores principales:	Abdullah Lakhan, Mazin Abed Mohammed, Seifedine Kadry, Karrar Hameed Abdulkareem, Fahad Taha AL-Dhief, Ching-Hsien Hsu
Formato:	article
Lenguaje:	EN
Publicado:	PeerJ Inc. 2021
Materias:	IRSTS Offloading ML Objectives Energy Delay Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/8621ca1b5f6f4e65ac76c2f59988801e
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id	oai:doaj.org-article:8621ca1b5f6f4e65ac76c2f59988801e
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spelling	oai:doaj.org-article:8621ca1b5f6f4e65ac76c2f59988801e2021-11-24T15:05:07ZFederated learning enables intelligent reflecting surface in fog-cloud enabled cellular network10.7717/peerj-cs.7582376-5992https://doaj.org/article/8621ca1b5f6f4e65ac76c2f59988801e2021-11-01T00:00:00Zhttps://peerj.com/articles/cs-758.pdfhttps://peerj.com/articles/cs-758/https://doaj.org/toc/2376-5992The intelligent reflecting surface (IRS) is a ground-breaking technology that can boost the efficiency of wireless data transmission systems. Specifically, the wireless signal transmitting environment is reconfigured by adjusting a large number of small reflecting units simultaneously. Therefore, intelligent reflecting surface (IRS) has been suggested as a possible solution for improving several aspects of future wireless communication. However, individual nodes are empowered in IRS, but decisions and learning of data are still made by the centralized node in the IRS mechanism. Whereas, in previous works, the problem of energy-efficient and delayed awareness learning IRS-assisted communications has been largely overlooked. The federated learning aware Intelligent Reconfigurable Surface Task Scheduling schemes (FL-IRSTS) algorithm is proposed in this paper to achieve high-speed communication with energy and delay efficient offloading and scheduling. The training of models is divided into different nodes. Therefore, the trained model will decide the IRSTS configuration that best meets the goals in terms of communication rate. Multiple local models trained with the local healthcare fog-cloud network for each workload using federated learning (FL) to generate a global model. Then, each trained model shared its initial configuration with the global model for the next training round. Each application’s healthcare data is handled and processed locally during the training process. Simulation results show that the proposed algorithm’s achievable rate output can effectively approach centralized machine learning (ML) while meeting the study’s energy and delay objectives.Abdullah LakhanMazin Abed MohammedSeifedine KadryKarrar Hameed AbdulkareemFahad Taha AL-DhiefChing-Hsien HsuPeerJ Inc.articleIRSTSOffloadingMLObjectivesEnergyDelayElectronic computers. Computer scienceQA75.5-76.95ENPeerJ Computer Science, Vol 7, p e758 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	IRSTS Offloading ML Objectives Energy Delay Electronic computers. Computer science QA75.5-76.95
spellingShingle	IRSTS Offloading ML Objectives Energy Delay Electronic computers. Computer science QA75.5-76.95 Abdullah Lakhan Mazin Abed Mohammed Seifedine Kadry Karrar Hameed Abdulkareem Fahad Taha AL-Dhief Ching-Hsien Hsu Federated learning enables intelligent reflecting surface in fog-cloud enabled cellular network
description	The intelligent reflecting surface (IRS) is a ground-breaking technology that can boost the efficiency of wireless data transmission systems. Specifically, the wireless signal transmitting environment is reconfigured by adjusting a large number of small reflecting units simultaneously. Therefore, intelligent reflecting surface (IRS) has been suggested as a possible solution for improving several aspects of future wireless communication. However, individual nodes are empowered in IRS, but decisions and learning of data are still made by the centralized node in the IRS mechanism. Whereas, in previous works, the problem of energy-efficient and delayed awareness learning IRS-assisted communications has been largely overlooked. The federated learning aware Intelligent Reconfigurable Surface Task Scheduling schemes (FL-IRSTS) algorithm is proposed in this paper to achieve high-speed communication with energy and delay efficient offloading and scheduling. The training of models is divided into different nodes. Therefore, the trained model will decide the IRSTS configuration that best meets the goals in terms of communication rate. Multiple local models trained with the local healthcare fog-cloud network for each workload using federated learning (FL) to generate a global model. Then, each trained model shared its initial configuration with the global model for the next training round. Each application’s healthcare data is handled and processed locally during the training process. Simulation results show that the proposed algorithm’s achievable rate output can effectively approach centralized machine learning (ML) while meeting the study’s energy and delay objectives.
format	article
author	Abdullah Lakhan Mazin Abed Mohammed Seifedine Kadry Karrar Hameed Abdulkareem Fahad Taha AL-Dhief Ching-Hsien Hsu
author_facet	Abdullah Lakhan Mazin Abed Mohammed Seifedine Kadry Karrar Hameed Abdulkareem Fahad Taha AL-Dhief Ching-Hsien Hsu
author_sort	Abdullah Lakhan
title	Federated learning enables intelligent reflecting surface in fog-cloud enabled cellular network
title_short	Federated learning enables intelligent reflecting surface in fog-cloud enabled cellular network
title_full	Federated learning enables intelligent reflecting surface in fog-cloud enabled cellular network
title_fullStr	Federated learning enables intelligent reflecting surface in fog-cloud enabled cellular network
title_full_unstemmed	Federated learning enables intelligent reflecting surface in fog-cloud enabled cellular network
title_sort	federated learning enables intelligent reflecting surface in fog-cloud enabled cellular network
publisher	PeerJ Inc.
publishDate	2021
url	https://doaj.org/article/8621ca1b5f6f4e65ac76c2f59988801e
work_keys_str_mv	AT abdullahlakhan federatedlearningenablesintelligentreflectingsurfaceinfogcloudenabledcellularnetwork AT mazinabedmohammed federatedlearningenablesintelligentreflectingsurfaceinfogcloudenabledcellularnetwork AT seifedinekadry federatedlearningenablesintelligentreflectingsurfaceinfogcloudenabledcellularnetwork AT karrarhameedabdulkareem federatedlearningenablesintelligentreflectingsurfaceinfogcloudenabledcellularnetwork AT fahadtahaaldhief federatedlearningenablesintelligentreflectingsurfaceinfogcloudenabledcellularnetwork AT chinghsienhsu federatedlearningenablesintelligentreflectingsurfaceinfogcloudenabledcellularnetwork
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Federated learning enables intelligent reflecting surface in fog-cloud enabled cellular network

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