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Byzantine-resilient decentralized learning

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TitleInfo
Title
Byzantine-resilient decentralized learning
Name (type = personal)
NamePart (type = family)
Yang
NamePart (type = given)
Zhixiong
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Zhixiong Yang
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author
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Bajwa
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Waheed U.
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Waheed U. Bajwa
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Advisory Committee
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chair
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Yuang
NamePart (type = given)
Bo
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Bo Yuang
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Advisory Committee
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internal member
Name (type = personal)
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Gurbuzbalaban
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Mert
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Mert Gurbuzbalaban
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
Yu
NamePart (type = given)
Jingjin
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Jingjin Yu
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
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NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
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school
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Text
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theses
OriginInfo
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2020
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2020-01
Language
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English
Abstract (type = abstract)
When datasets are distributed over a network and a central server is infeasible, machine learning has to be performed in a decentralized fashion. The dissertation introduces new methods that solve decentralized machine learning problems in the presence of Byzantine failures. Classic decentralized learning methods require nodes communicate with each other by communicating over the network. When a node engages in arbitrary or malicious behavior, it is termed as having Byzantine failure. Without any Byzantine-resilient modification, classic learning methods cannot complete machine learning tasks as intended in the presence of Byzantine failure. Byzantine-resilient decentralized learning methods are discussed in this dissertation. Both theoretical guarantees and experiments are given to justify the usefulness of the methods under Byzantine settings.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (authority = LCSH)
Topic
Machine learning
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
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ETD
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ETD_10549
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application/pdf
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Extent
1 online resource (iv, 72 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-817e-1d43
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Yang
GivenName
Zhixiong
Role
Copyright Holder
RightsEvent
Type
Permission or license
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2020-01-13 10:46:42
AssociatedEntity
Name
Zhixiong Yang
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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2020-01-13T15:35:44
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2020-01-13T15:35:44
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