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Fed-mRMR: A lossless federated feature selection method
dc.contributor.author | Hermo González, Jorge | |
dc.contributor.author | Bolón-Canedo, Verónica | |
dc.contributor.author | Ladra, Susana | |
dc.date.accessioned | 2024-04-25T09:36:12Z | |
dc.date.available | 2024-04-25T09:36:12Z | |
dc.date.issued | 2024-05 | |
dc.identifier.citation | Jorge 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.2024.120609 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/36349 | |
dc.description | Implementation can be found at: https://github.com/jorgehermo9/fed-mrmr | es_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-mrmr | es_ES |
dc.description.sponsorship | This 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.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2021/53 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2022/44 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PDC2021-121239-C31/ES/FLATCITY-POC | es_ES |
dc.relation | info: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 ESPACIAL | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109238GB-C22/ES/APRENDIZAJE AUTOMATICO ESCALABLE Y EXPLICABLE | es_ES |
dc.relation | info: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 MEDIOAMBIENTALES | es_ES |
dc.relation | info: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 RÁPIDOS | es_ES |
dc.relation | info: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 VERDES | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.ins.2024.120609 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Edge computing | es_ES |
dc.subject | Feature selection | es_ES |
dc.subject | Federated learning | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Non-IID data | es_ES |
dc.subject | Privacy preservation | es_ES |
dc.title | Fed-mRMR: A lossless federated feature selection method | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Information Sciences | es_ES |
UDC.volume | 669 | es_ES |
dc.identifier.doi | 10.1016/j.ins.2024.120609 |
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