The Internet of Federated Things (IoFT)

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to the edge, allowing IoT devices to collaboratively extract knowl...

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Autores principales:	Raed Kontar, Naichen Shi, Xubo Yue, Seokhyun Chung, Eunshin Byon, Mosharaf Chowdhury, Jionghua Jin, Wissam Kontar, Neda Masoud, Maher Nouiehed, Chinedum E. Okwudire, Garvesh Raskutti, Romesh Saigal, Karandeep Singh, Zhi-Sheng Ye
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Internet of Things federated learning global model personalized model meta-learning future applications Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/062590f52ce0422fa225371fa898a329
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spelling	oai:doaj.org-article:062590f52ce0422fa225371fa898a3292021-12-01T00:01:01ZThe Internet of Federated Things (IoFT)2169-353610.1109/ACCESS.2021.3127448https://doaj.org/article/062590f52ce0422fa225371fa898a3292021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9611259/https://doaj.org/toc/2169-3536The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacy-preserving model training, coined as federated learning (FL). This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and discuss FL data-driven approaches, opportunities, and challenges that allow decentralized inference within three dimensions: (i) a global model that maximizes utility across all IoT devices, (ii) a personalized model that borrows strengths across all devices yet retains its own model, (iii) a meta-learning model that quickly adapts to new devices or learning tasks. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts. Those industries include manufacturing, transportation, energy, healthcare, quality & reliability, business, and computing.Raed KontarNaichen ShiXubo YueSeokhyun ChungEunshin ByonMosharaf ChowdhuryJionghua JinWissam KontarNeda MasoudMaher NouiehedChinedum E. OkwudireGarvesh RaskuttiRomesh SaigalKarandeep SinghZhi-Sheng YeIEEEarticleInternet of Thingsfederated learningglobal modelpersonalized modelmeta-learningfuture applicationsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 156071-156113 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Internet of Things federated learning global model personalized model meta-learning future applications Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Internet of Things federated learning global model personalized model meta-learning future applications Electrical engineering. Electronics. Nuclear engineering TK1-9971 Raed Kontar Naichen Shi Xubo Yue Seokhyun Chung Eunshin Byon Mosharaf Chowdhury Jionghua Jin Wissam Kontar Neda Masoud Maher Nouiehed Chinedum E. Okwudire Garvesh Raskutti Romesh Saigal Karandeep Singh Zhi-Sheng Ye The Internet of Federated Things (IoFT)
description	The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacy-preserving model training, coined as federated learning (FL). This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and discuss FL data-driven approaches, opportunities, and challenges that allow decentralized inference within three dimensions: (i) a global model that maximizes utility across all IoT devices, (ii) a personalized model that borrows strengths across all devices yet retains its own model, (iii) a meta-learning model that quickly adapts to new devices or learning tasks. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts. Those industries include manufacturing, transportation, energy, healthcare, quality & reliability, business, and computing.
format	article
author	Raed Kontar Naichen Shi Xubo Yue Seokhyun Chung Eunshin Byon Mosharaf Chowdhury Jionghua Jin Wissam Kontar Neda Masoud Maher Nouiehed Chinedum E. Okwudire Garvesh Raskutti Romesh Saigal Karandeep Singh Zhi-Sheng Ye
author_facet	Raed Kontar Naichen Shi Xubo Yue Seokhyun Chung Eunshin Byon Mosharaf Chowdhury Jionghua Jin Wissam Kontar Neda Masoud Maher Nouiehed Chinedum E. Okwudire Garvesh Raskutti Romesh Saigal Karandeep Singh Zhi-Sheng Ye
author_sort	Raed Kontar
title	The Internet of Federated Things (IoFT)
title_short	The Internet of Federated Things (IoFT)
title_full	The Internet of Federated Things (IoFT)
title_fullStr	The Internet of Federated Things (IoFT)
title_full_unstemmed	The Internet of Federated Things (IoFT)
title_sort	internet of federated things (ioft)
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/062590f52ce0422fa225371fa898a329
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The Internet of Federated Things (IoFT)

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