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dc.contributor.authorHermo González, Jorge
dc.contributor.authorBolón-Canedo, Verónica
dc.contributor.authorLadra, Susana
dc.date.accessioned2024-04-25T09:36:12Z
dc.date.available2024-04-25T09:36:12Z
dc.date.issued2024-05
dc.identifier.citationJorge Hermo, Verónica Bolón-Canedo, and Susana Ladra, "Fed-mRMR: A lossless federated feature selection method", Information Sciences, Vol. 669, 120609, May 2024, doi: 10.1016/j.ins.2024.120609es_ES
dc.identifier.urihttp://hdl.handle.net/2183/36349
dc.descriptionImplementation can be found at: https://github.com/jorgehermo9/fed-mrmres_ES
dc.description.abstract[Abstract]: Feature selection has become a mandatory task in data mining, due to the overwhelming amount of features in Big Data problems. To handle this high-dimensional data and avoid the well-known curse of dimensionality, we need to pre-select an optimal subset of features to reduce redundant computations. Federated learning is a machine learning technique based on training an algorithm over many decentralized edge devices holding local rather than global data on a centralized server. Application of this technique is extending to fields such as self-driving cars, medicine and health, and Industry 4.0, where data privacy is compulsory. Feature selection through federated learning is a complicated task since suboptimal features calculated by feature selection methods may be different in heterogeneous datasets from different nodes. In this paper, we propose a lossless federated version of the classic minimum redundancy maximum relevance (mRMR) feature selection algorithm, called federated mRMR (fed-mRMR), which, without losing any effectiveness of the original mRMR method, is applicable to federated learning approaches and capable of dealing with data that are not independent and identically distributed (non-IID data). Implementation can be found at: https://github.com/jorgehermo9/fed-mrmres_ES
dc.description.sponsorshipThis work was supported by CITIC, a Research Center accredited by the Galician University System, funded by the Xunta de Galicia Consellería de Cultura, Educación e Universidade, supported 80% by ERDF/FEDER funds (Operational Programme Information Sciences 669 (2024) 120609 14 J. Hermo, V. Bolón-Canedo and S. Ladra Galicia 2014-2020) and 20% by the Xunta de Galicia Secretaría Xeral de Universidades (Grant ED431G 2019/01). It was also partially funded by the Xunta de Galicia and ERDF/FEDER (Grants ED431C 2021/53; ED431C 2022/44), the Spanish Ministerio de Ciencia e Innovación (MCIN/AEI/10.13039/501100011033) and NextGenerationEU/PRTR (Grants PDC2021-121239-C31; PID2019-105221RB-C41; PID2019-109238GB-C22; PID2022-141027NB-C2; TED2021-130599A-I00; TSI-100925-2023-1). A department collaboration grant awarded by Universidade de A Coruña also supported this research.es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2021/53es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2022/44es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PDC2021-121239-C31/ES/FLATCITY-POCes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-105221RB-C41/ES/VISUALIZACIÓN Y EXPLORACIÓN BASADA EN FLUJOS Y ANALÍTICA DE BIG DATA ESPACIALes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109238GB-C22/ES/APRENDIZAJE AUTOMATICO ESCALABLE Y EXPLICABLEes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-141027NB-C2/ES/MODELADO, DESCUBRIMIENTO, EXPLORACION Y ANALISIS DE DATA LAKES MEDIOAMBIENTALESes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-130599A-I00/ES/ALGORITMOS DE SELECCIÓN DE CARACTERÍSTICAS VERDES Y RÁPIDOSes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TSI-100925-2023-1/ES/CÁTEDRA UDC-INDITEX DE INTELIGENCIA ARTIFICIAL EN ALGORITMOS VERDESes_ES
dc.relation.urihttps://doi.org/10.1016/j.ins.2024.120609es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectEdge computinges_ES
dc.subjectFeature selectiones_ES
dc.subjectFederated learninges_ES
dc.subjectMachine learninges_ES
dc.subjectNon-IID dataes_ES
dc.subjectPrivacy preservationes_ES
dc.titleFed-mRMR: A lossless federated feature selection methodes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleInformation Scienceses_ES
UDC.volume669es_ES
dc.identifier.doi10.1016/j.ins.2024.120609


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