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A Federated Interac...
A Federated Interactive Learning IoT-Based Health Monitoring Platform
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- Alawadi, Sadi, 1983- (author)
- Uppsala universitet,Avdelningen för beräkningsvetenskap
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- Kebande, Victor R. (author)
- Luleå tekniska universitet,Digitala tjänster och system,Umeå University, Sweden,Luleå Univ Technol, Dept Comp Sci Elect & Space Engn, Luleå, Sweden.
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- Dong, Yuji (author)
- School of Internet of Things, Xi’an Jiaotong-Liverpool University, Suzhou, China,School of Internet of ThingsXi’an Jiaotong-Liverpool UniversitySuzhouChina,Xian Jiaotong Liverpool Univ, Sch Internet Things, Suzhou, Peoples R China.
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- Bugeja, Joseph (author)
- Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT),Department of Computer Science, Malmö University, Malmö, Sweden,Malmö Univ, Dept Comp Sci, Malmö, Sweden.
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- Persson, Jan A. (author)
- Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT),Department of Computer Science, Malmö University, Malmö, Sweden,Malmö Univ, Dept Comp Sci, Malmö, Sweden.
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- Olsson, Carl Magnus (author)
- Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT),Department of Computer Science, Malmö University, Malmö, Sweden,Malmö Univ, Dept Comp Sci, Malmö, Sweden.
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(creator_code:org_t)
- 2021-07-17
- 2021
- English.
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In: New Trends in Database and Information Systems. - Cham : Springer. ; , s. 235-246, s. 235-246
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Abstract
Subject headings
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- Remote health monitoring is a trend for better health management which necessitates the need for secure monitoring and privacy-preservation of patient data. Moreover, accurate and continuous monitoring of personal health status may require expert validation in an active learning strategy. As a result, this paper proposes a Federated Interactive Learning IoT-based Health Monitoring Platform (FIL-IoT-HMP) which incorporates multi-expert feedback as ‘Human-in-the-loop’ in an active learning strategy in order to improve the clients’ Machine Learning (ML) models. The authors have proposed an architecture and conducted an experiment as a proof of concept. Federated learning approach has been preferred in this context given that it strengthens privacy by allowing the global model to be trained while sensitive data is retained at the local edge nodes. Also, each model’s accuracy is improved while privacy and security of data has been upheld.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Keyword
- IoT
- Healthcare
- Federated
- Machine learning
- Information systems
- Informationssystem
Publication and Content Type
- ref (subject category)
- kon (subject category)
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