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Fast deep autoencoder for federated learning
dc.contributor.author | Novoa-Paradela, David | |
dc.contributor.author | Fontenla-Romero, Óscar | |
dc.contributor.author | Guijarro-Berdiñas, Bertha | |
dc.date.accessioned | 2023-11-27T12:25:22Z | |
dc.date.available | 2023-11-27T12:25:22Z | |
dc.date.issued | 2023-11 | |
dc.identifier.citation | D. Novoa-Paradela, Óscar Fontenla-Romero, and B. Guijarro-Berdiñas, "Fast deep autoencoder for federated learning", Pattern Recognition, Vol. 143, Nov. 2023, 109805, doi: https://doi.org/10.1016/j.patcog.2023.109805 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/34337 | |
dc.description | Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG | es_ES |
dc.description.abstract | [Abstract]: This paper presents a novel, fast and privacy preserving implementation of deep autoencoders. DAEF (Deep AutoEncoder for Federated learning), unlike traditional neural networks, trains a deep autoencoder network in a non-iterative way, which drastically reduces training time. Training can be performed incrementally, in parallel and distributed and, thanks to its mathematical formulation, the information to be exchanged does not endanger the privacy of the training data. The method has been evaluated and compared with other state-of-the-art autoencoders, showing interesting results in terms of accuracy, speed and use of available resources. This makes DAEF a valid method for edge computing and federated learning, in addition to other classic machine learning scenarios. | es_ES |
dc.description.sponsorship | This work was supported in part by grant Machine Learning on the Edge - Ayudas Fundación BBVA a Equipos de Investigación Científica 2019; the Spanish National Plan for Scientific and Technical Research and Innovation (PID2019-109238GB-C22 and TED2021-130599A-I00); the Xunta de Galicia (ED431C 2022/44) and ERDF funds. CITIC is funded by Xunta de Galicia and ERDF funds. Funding for open access charge: Universidade da Coruña/CISUG. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2022/44 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier Ltd | 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/TED2021-130599A-I00/ES/ALGORITMOS DE SELECCIÓN DE CARACTERÍSTICAS VERDES Y RÁPIDOS | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.patcog.2023.109805 | es_ES |
dc.rights | Atribución 4.0 Internacional (CC BY 4.0) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Anomaly detection | es_ES |
dc.subject | Deep autoencoder | es_ES |
dc.subject | Edge computing | es_ES |
dc.subject | Federated learning | es_ES |
dc.subject | Machine learning | es_ES |
dc.title | Fast deep autoencoder for federated learning | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Pattern Recognition | es_ES |
UDC.volume | 143 | es_ES |
UDC.issue | article number 109805 | es_ES |
dc.identifier.doi | 10.1016/j.patcog.2023.109805 |
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