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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/968
DC FieldValueLanguage
dc.contributor.authorDuc Nguyenen_US
dc.date.accessioned2020-07-17T05:46:10Z-
dc.date.available2020-07-17T05:46:10Z-
dc.date.issued2019-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/968-
dc.description.abstractToday’s Artificial Intelligence is facing two major challenges which are the isolation of data between enterprises and industries, and the demand of data privacy protection in security regulations. In this document, I introduce the Federated Machine Learning as an alternative way to train a machine which strictly follows the rule of data privacy and can take down the isolation barrier between industries’ data. Furthermore, I present the learning framework of federated learning including Vertical and horizontal federated learning along with their architectures. Also, the applications and other related works regarding the federated machine learning are considered and discussed. Additionally, I also study and discuss the Federated Machine Learning Algorithms which includes the Federated Averaging and Federated Stochastic Variance Reduced Gradient. Finally, to support the ideas of federated machine learning, I have created a prototype of the Digits Recognition applying the Federated Averaging Alg-
dc.language.isoen-
dc.publisherVietnamese-German University-
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectArtificial intelligence-
dc.subjectFederated machine learning-
dc.titleConcept and applications of federated machine learning-
dc.typeThesis-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.languageiso639-1other-
Appears in Collections:Computer Science (CS)
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