Trustworthy Artificial Intelligence -based federated architecture for symptomatic disease detection

[EN]The recent viral outbreaks have had a significant impact on interpersonal relationships, particularly in enclosed spaces. Detecting and preventing the transmission of diseases such as COVID-19 has become a top priority. These diseases are typically identifiable through the symptoms they cause in...

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Detalhes bibliográficos
Autores: López-Blanco, Raúl, Alonso Rincón, Ricardo Serafín, Rodríguez González, Sara, Prieto Tejedor, Javier, Corchado, Juan Manuel
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Recursos:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/166193
Acesso em linha:http://hdl.handle.net/10366/166193
Access Level:acceso abierto
Palavra-chave:Trustworthy Artificial Intelligence
Federated learning
Internet of Things
Healthcare
COVID-19
1203.04 Inteligencia Artificial
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spelling Trustworthy Artificial Intelligence -based federated architecture for symptomatic disease detectionLópez-Blanco, RaúlAlonso Rincón, Ricardo SerafínRodríguez González, SaraPrieto Tejedor, JavierCorchado, Juan ManuelTrustworthy Artificial IntelligenceFederated learningInternet of ThingsHealthcareCOVID-191203.04 Inteligencia Artificial[EN]The recent viral outbreaks have had a significant impact on interpersonal relationships, particularly in enclosed spaces. Detecting and preventing the transmission of diseases such as COVID-19 has become a top priority. These diseases are typically identifiable through the symptoms they cause in humans. However, the collection of personal and health data for use in Artificial Intelligence models can give rise to ethical, security, and privacy issues. Therefore, it is necessary to have architectures that maintain the principles of Trustworthy Artificial Intelligence by design. This work proposes a decentralised architecture based on Federated Learning for symptomatic disease detection using the edge computing paradigm, storing the information in the device that collected it, and the foundations of Trustworthy Artificial Intelligence. The architecture is designed to be robust, secure, transparent, and responsible while maintaining data privacy. The proposed approach can be used with medical information capture systems with different user profiles.MCIN/AEI /10.13039/501100011033); European Union NextGenerationEU/PRTRElsevier B.V.202520252024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/166193reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésCNS2022-135101Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1661932026-06-07T06:28:51Z
dc.title.none.fl_str_mv Trustworthy Artificial Intelligence -based federated architecture for symptomatic disease detection
title Trustworthy Artificial Intelligence -based federated architecture for symptomatic disease detection
spellingShingle Trustworthy Artificial Intelligence -based federated architecture for symptomatic disease detection
López-Blanco, Raúl
Trustworthy Artificial Intelligence
Federated learning
Internet of Things
Healthcare
COVID-19
1203.04 Inteligencia Artificial
title_short Trustworthy Artificial Intelligence -based federated architecture for symptomatic disease detection
title_full Trustworthy Artificial Intelligence -based federated architecture for symptomatic disease detection
title_fullStr Trustworthy Artificial Intelligence -based federated architecture for symptomatic disease detection
title_full_unstemmed Trustworthy Artificial Intelligence -based federated architecture for symptomatic disease detection
title_sort Trustworthy Artificial Intelligence -based federated architecture for symptomatic disease detection
dc.creator.none.fl_str_mv López-Blanco, Raúl
Alonso Rincón, Ricardo Serafín
Rodríguez González, Sara
Prieto Tejedor, Javier
Corchado, Juan Manuel
author López-Blanco, Raúl
author_facet López-Blanco, Raúl
Alonso Rincón, Ricardo Serafín
Rodríguez González, Sara
Prieto Tejedor, Javier
Corchado, Juan Manuel
author_role author
author2 Alonso Rincón, Ricardo Serafín
Rodríguez González, Sara
Prieto Tejedor, Javier
Corchado, Juan Manuel
author2_role author
author
author
author
dc.subject.none.fl_str_mv Trustworthy Artificial Intelligence
Federated learning
Internet of Things
Healthcare
COVID-19
1203.04 Inteligencia Artificial
topic Trustworthy Artificial Intelligence
Federated learning
Internet of Things
Healthcare
COVID-19
1203.04 Inteligencia Artificial
description [EN]The recent viral outbreaks have had a significant impact on interpersonal relationships, particularly in enclosed spaces. Detecting and preventing the transmission of diseases such as COVID-19 has become a top priority. These diseases are typically identifiable through the symptoms they cause in humans. However, the collection of personal and health data for use in Artificial Intelligence models can give rise to ethical, security, and privacy issues. Therefore, it is necessary to have architectures that maintain the principles of Trustworthy Artificial Intelligence by design. This work proposes a decentralised architecture based on Federated Learning for symptomatic disease detection using the edge computing paradigm, storing the information in the device that collected it, and the foundations of Trustworthy Artificial Intelligence. The architecture is designed to be robust, secure, transparent, and responsible while maintaining data privacy. The proposed approach can be used with medical information capture systems with different user profiles.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/166193
url http://hdl.handle.net/10366/166193
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv CNS2022-135101
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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