Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms

The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the inc...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-07
Main Authors: Hallaji, Ehsan, Razavi-Far, Roozbeh, Saif, Mehrdad
Format: Article
Language:English
Subjects:
Data exchange
Machine learning
Privacy
Security
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Hallaji, Ehsan
Razavi-Far, Roozbeh
Saif, Mehrdad
description The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.
doi_str_mv 10.48550/arxiv.2207.02337
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2685820905</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2685820905</sourcerecordid><originalsourceid>FETCH-LOGICAL-a525-abba2024626ee6307538ee7ff082d99dff87a16959f721df28ad613a093e28a53</originalsourceid><addsrcrecordid>eNotjc1KAzEURoMgWGofwF3A9Yx3bpo_d6VaFUa6sPtya250imY06RR9ewd1db7F4TtCXDRQz53WcEX5qzvWiGBrQKXsiZiMaCo3RzwTs1L2AIDGotZqItYrDpzpwEFSCnKTKZXIWbZMOXXp5Vou5NOQj_wt-yQX4ci5UO64_Oo3HDkVlo_8_EqpK-_lXJxGeis8--dUbFa3m-V91a7vHpaLtiKNuqLdjhBwbtAwGwVWK8dsYwSHwfsQo7PUGK99tNiEiI6CaRSBVzxurabi8u_2I_efA5fDdt8POY3FLRqnHYIHrX4AG75PSA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2685820905</pqid></control><display><type>article</type><title>Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms</title><source>Publicly Available Content Database</source><creator>Hallaji, Ehsan ; Razavi-Far, Roozbeh ; Saif, Mehrdad</creator><creatorcontrib>Hallaji, Ehsan ; Razavi-Far, Roozbeh ; Saif, Mehrdad</creatorcontrib><description>The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2207.02337</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Data exchange ; Machine learning ; Privacy ; Security</subject><ispartof>arXiv.org, 2022-07</ispartof><rights>2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2685820905?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>780,784,25753,27925,37012,44590</link.rule.ids></links><search><creatorcontrib>Hallaji, Ehsan</creatorcontrib><creatorcontrib>Razavi-Far, Roozbeh</creatorcontrib><creatorcontrib>Saif, Mehrdad</creatorcontrib><title>Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms</title><title>arXiv.org</title><description>The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.</description><subject>Data exchange</subject><subject>Machine learning</subject><subject>Privacy</subject><subject>Security</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNotjc1KAzEURoMgWGofwF3A9Yx3bpo_d6VaFUa6sPtya250imY06RR9ewd1db7F4TtCXDRQz53WcEX5qzvWiGBrQKXsiZiMaCo3RzwTs1L2AIDGotZqItYrDpzpwEFSCnKTKZXIWbZMOXXp5Vou5NOQj_wt-yQX4ci5UO64_Oo3HDkVlo_8_EqpK-_lXJxGeis8--dUbFa3m-V91a7vHpaLtiKNuqLdjhBwbtAwGwVWK8dsYwSHwfsQo7PUGK99tNiEiI6CaRSBVzxurabi8u_2I_efA5fDdt8POY3FLRqnHYIHrX4AG75PSA</recordid><startdate>20220705</startdate><enddate>20220705</enddate><creator>Hallaji, Ehsan</creator><creator>Razavi-Far, Roozbeh</creator><creator>Saif, Mehrdad</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20220705</creationdate><title>Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms</title><author>Hallaji, Ehsan ; Razavi-Far, Roozbeh ; Saif, Mehrdad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a525-abba2024626ee6307538ee7ff082d99dff87a16959f721df28ad613a093e28a53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Data exchange</topic><topic>Machine learning</topic><topic>Privacy</topic><topic>Security</topic><toplevel>online_resources</toplevel><creatorcontrib>Hallaji, Ehsan</creatorcontrib><creatorcontrib>Razavi-Far, Roozbeh</creatorcontrib><creatorcontrib>Saif, Mehrdad</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hallaji, Ehsan</au><au>Razavi-Far, Roozbeh</au><au>Saif, Mehrdad</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms</atitle><jtitle>arXiv.org</jtitle><date>2022-07-05</date><risdate>2022</risdate><eissn>2331-8422</eissn><abstract>The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.2207.02337</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2022-07
issn 2331-8422
language eng
recordid cdi_proquest_journals_2685820905
source Publicly Available Content Database
subjects Data exchange
Machine learning
Privacy
Security
title Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T03%3A56%3A19IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Federated%20and%20Transfer%20Learning:%20A%20Survey%20on%20Adversaries%20and%20Defense%20Mechanisms&rft.jtitle=arXiv.org&rft.au=Hallaji,%20Ehsan&rft.date=2022-07-05&rft.eissn=2331-8422&rft_id=info:doi/10.48550/arxiv.2207.02337&rft_dat=%3Cproquest%3E2685820905%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-a525-abba2024626ee6307538ee7ff082d99dff87a16959f721df28ad613a093e28a53%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2685820905&rft_id=info:pmid/&rfr_iscdi=true