Fuzzy Consensus With Federated Learning Method in Medical Systems

Large-scale group decision-making (LSGDM) is one of the main open problems where a decision is made by many different results. Moreover, there is also a problem with how to make the decision when there is no all information. This uncertainty can be very problematic for many different solutions in ar...

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Autor principal:	Dawid Poap
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Federated learning LSGDM fuzzy neural network healthcare industry 4.0 Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a9265130008c49fbaeaac4d02e265420
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id	oai:doaj.org-article:a9265130008c49fbaeaac4d02e265420
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spelling	oai:doaj.org-article:a9265130008c49fbaeaac4d02e2654202021-11-18T00:07:16ZFuzzy Consensus With Federated Learning Method in Medical Systems2169-353610.1109/ACCESS.2021.3125799https://doaj.org/article/a9265130008c49fbaeaac4d02e2654202021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9605671/https://doaj.org/toc/2169-3536Large-scale group decision-making (LSGDM) is one of the main open problems where a decision is made by many different results. Moreover, there is also a problem with how to make the decision when there is no all information. This uncertainty can be very problematic for many different solutions in artificial intelligence. In this paper, we propose to extend a federated learning (FL) approach to not only a training process but also for making a decision using many different classifiers. This solution is applied in LSGDM, where many different results are intended for the classification of various data and can be used for deciding, even when some of the data are missing. For this purpose, we propose a fuzzy consensus that can be used in these problems. The contribution of this paper is the new way of using FL and extending its operation to many different classifiers. Our proposition was described for medical purposes and evaluated to show the advantages of the proposal. The proposal obtained 89,12% of accuracy on HAM10000, which is one of the best results compared to state-of-art.Dawid PoapIEEEarticleFederated learningLSGDMfuzzyneural networkhealthcare industry 4.0Electrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150383-150392 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Federated learning LSGDM fuzzy neural network healthcare industry 4.0 Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Federated learning LSGDM fuzzy neural network healthcare industry 4.0 Electrical engineering. Electronics. Nuclear engineering TK1-9971 Dawid Poap Fuzzy Consensus With Federated Learning Method in Medical Systems
description	Large-scale group decision-making (LSGDM) is one of the main open problems where a decision is made by many different results. Moreover, there is also a problem with how to make the decision when there is no all information. This uncertainty can be very problematic for many different solutions in artificial intelligence. In this paper, we propose to extend a federated learning (FL) approach to not only a training process but also for making a decision using many different classifiers. This solution is applied in LSGDM, where many different results are intended for the classification of various data and can be used for deciding, even when some of the data are missing. For this purpose, we propose a fuzzy consensus that can be used in these problems. The contribution of this paper is the new way of using FL and extending its operation to many different classifiers. Our proposition was described for medical purposes and evaluated to show the advantages of the proposal. The proposal obtained 89,12% of accuracy on HAM10000, which is one of the best results compared to state-of-art.
format	article
author	Dawid Poap
author_facet	Dawid Poap
author_sort	Dawid Poap
title	Fuzzy Consensus With Federated Learning Method in Medical Systems
title_short	Fuzzy Consensus With Federated Learning Method in Medical Systems
title_full	Fuzzy Consensus With Federated Learning Method in Medical Systems
title_fullStr	Fuzzy Consensus With Federated Learning Method in Medical Systems
title_full_unstemmed	Fuzzy Consensus With Federated Learning Method in Medical Systems
title_sort	fuzzy consensus with federated learning method in medical systems
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/a9265130008c49fbaeaac4d02e265420
work_keys_str_mv	AT dawidpoap fuzzyconsensuswithfederatedlearningmethodinmedicalsystems
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Fuzzy Consensus With Federated Learning Method in Medical Systems

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