Resilient distributed machine learning using network reconfiguration

Machine learning (ML) is currently making great impact in our today's life. However computations may become excessively huge on big data scenarios, so recently distributed models have become an interesting field of study. This thesis presents a reconfiguration strategy over a network of distrib...

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Autores:: Ángel Imitola, Jesús Gabriel

Tipo de recurso:

Fecha de publicación:: 2020

Institución:: Universidad de los Andes

Repositorio:: Séneca: repositorio Uniandes

Idioma:: eng

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spelling	Al consultar y hacer uso de este recurso, está aceptando las condiciones de uso establecidas por los autores.https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Giraldo Trujillo, Luis Felipevirtual::10022-1Ángel Imitola, Jesús Gabrielc2e91105-3bd6-47ea-9930-fdf201ca3a03500Quijano Silva, NicanorBolívar Nieto, Edgar2021-08-10T18:05:44Z2021-08-10T18:05:44Z2020http://hdl.handle.net/1992/5099223556.pdfinstname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Machine learning (ML) is currently making great impact in our today's life. However computations may become excessively huge on big data scenarios, so recently distributed models have become an interesting field of study. This thesis presents a reconfiguration strategy over a network of distributed machine learning models, in order to boost its reliability against certain types of byzantine faults or attacks. The attacks considered are some attacks that are usually used against machine learning models or concerning consensus. Simulations on a distributed support vector machine (DSVM) network are made to evaluate our reconfiguration strategy under the previously considered attacks.El Aprendizaje de máquina (ML) está haciendo un impacto grande en nuestras vidas actualmente. Sin embargo, las operaciones pueden volverse excesivamente grande en escenarios de big data. Para combatirlo, los modelos distribuidos se han convertido en un campo interesante de estudio. Esta tesis presenta una estrategia de reconfiguración sobre una red de ML distribuida, para aumentar su resiliencia contra ciertas fallas bizantinas o ataques a la red. Los ataques considerados son ataques recurrentes en el estudio de machine learning y consensus. Se realizan algunas simulaciones sobre una máquina de soporte vectorial distribuido (DSVM) para evaluar el desempeño de la estrategia de reconfiguración bajo los ataques considerados previamente.Magíster en Ingeniería Electrónica y de ComputadoresMaestría31 hojasapplication/pdfengUniversidad de los AndesMaestría en Ingeniería Electrónica y de ComputadoresFacultad de IngenieríaDepartamento de Ingeniería Eléctrica y ElectrónicaResilient distributed machine learning using network reconfigurationTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesishttp://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/TMAprendizaje automático (Inteligencia artificial)Big DataSeguridad en computadoresIngeniería201413561Publicationhttps://scholar.google.es/citations?user=4TGvo8AAAAJvirtual::10022-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000802506virtual::10022-1eb386eec-3ec8-40c2-829d-ae8cbf0e384evirtual::10022-1eb386eec-3ec8-40c2-829d-ae8cbf0e384evirtual::10022-1THUMBNAIL23556.pdf.jpg23556.pdf.jpgIM Thumbnailimage/jpeg11565https://repositorio.uniandes.edu.co/bitstreams/7c0fc90e-0586-42cf-bef7-2a4c70f6e16c/download7e16aca15df29323b8eac0012f6493dbMD55TEXT23556.pdf.txt23556.pdf.txtExtracted texttext/plain40693https://repositorio.uniandes.edu.co/bitstreams/695f8058-dd3d-4ebf-8c11-69e13172050f/download7ada96bc579b2ded25935b3ca5fcb09aMD54ORIGINAL23556.pdfapplication/pdf579216https://repositorio.uniandes.edu.co/bitstreams/89d85b6f-5492-4307-a072-ee1f27235cd4/download908ca0008f45c93c4ad15fe739c89d5fMD511992/50992oai:repositorio.uniandes.edu.co:1992/509922024-03-13 14:04:54.305https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfopen.accesshttps://repositorio.uniandes.edu.coRepositorio institucional Sénecaadminrepositorio@uniandes.edu.co
dc.title.spa.fl_str_mv	Resilient distributed machine learning using network reconfiguration
title	Resilient distributed machine learning using network reconfiguration
spellingShingle	Resilient distributed machine learning using network reconfiguration Aprendizaje automático (Inteligencia artificial) Big Data Seguridad en computadores Ingeniería
title_short	Resilient distributed machine learning using network reconfiguration
title_full	Resilient distributed machine learning using network reconfiguration
title_fullStr	Resilient distributed machine learning using network reconfiguration
title_full_unstemmed	Resilient distributed machine learning using network reconfiguration
title_sort	Resilient distributed machine learning using network reconfiguration
dc.creator.fl_str_mv	Ángel Imitola, Jesús Gabriel
dc.contributor.advisor.none.fl_str_mv	Giraldo Trujillo, Luis Felipe
dc.contributor.author.none.fl_str_mv	Ángel Imitola, Jesús Gabriel
dc.contributor.jury.none.fl_str_mv	Quijano Silva, Nicanor Bolívar Nieto, Edgar
dc.subject.armarc.none.fl_str_mv	Aprendizaje automático (Inteligencia artificial) Big Data Seguridad en computadores
topic	Aprendizaje automático (Inteligencia artificial) Big Data Seguridad en computadores Ingeniería
dc.subject.themes.none.fl_str_mv	Ingeniería
description	Machine learning (ML) is currently making great impact in our today's life. However computations may become excessively huge on big data scenarios, so recently distributed models have become an interesting field of study. This thesis presents a reconfiguration strategy over a network of distributed machine learning models, in order to boost its reliability against certain types of byzantine faults or attacks. The attacks considered are some attacks that are usually used against machine learning models or concerning consensus. Simulations on a distributed support vector machine (DSVM) network are made to evaluate our reconfiguration strategy under the previously considered attacks.
publishDate	2020
dc.date.issued.none.fl_str_mv	2020
dc.date.accessioned.none.fl_str_mv	2021-08-10T18:05:44Z
dc.date.available.none.fl_str_mv	2021-08-10T18:05:44Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
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dc.type.content.spa.fl_str_mv	Text
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dc.identifier.pdf.none.fl_str_mv	23556.pdf
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dc.language.iso.none.fl_str_mv	eng
language	eng
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dc.format.extent.none.fl_str_mv	31 hojas
dc.format.mimetype.none.fl_str_mv	application/pdf
dc.publisher.none.fl_str_mv	Universidad de los Andes
dc.publisher.program.none.fl_str_mv	Maestría en Ingeniería Electrónica y de Computadores
dc.publisher.faculty.none.fl_str_mv	Facultad de Ingeniería
dc.publisher.department.none.fl_str_mv	Departamento de Ingeniería Eléctrica y Electrónica
publisher.none.fl_str_mv	Universidad de los Andes
institution	Universidad de los Andes
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Resilient distributed machine learning using network reconfiguration

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