Preserving Data Privacy via Federated Learning: Challenges and Solutions
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Li, Zengpeng | - |
dc.contributor.author | Sharma, Vishal | - |
dc.contributor.author | Mohanty, Saraju P. | - |
dc.date.accessioned | 2021-09-10T06:50:21Z | - |
dc.date.available | 2021-09-10T06:50:21Z | - |
dc.date.issued | 2020-05-01 | - |
dc.identifier.issn | 2162-2248 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/19526 | - |
dc.description.abstract | Data have always been a major priority for businesses of all sizes. Businesses tend to enhance their ability in contextualizing data and draw new insights from it as the data itself proliferates with the advancement of technologies. Federated learning acts as a special form of privacy-preserving machine learning technique and can contextualize the data. It is a decentralized training approach for privately collecting and training the data provided by mobile devices, which are located at different geographical locations. Furthermore, users can benefit from obtaining a well-trained machine learning model without sending their privacy-sensitive personal data to the cloud. This article focuses on the most significant challenges associated with the preservation of data privacy via federated learning. Valuable attack mechanisms are discussed, and associated solutions are highlighted to the corresponding attack. Several research aspects along with promising future directions and applications via federated learning are additionally discussed. | - |
dc.format.extent | 9 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Preserving Data Privacy via Federated Learning: Challenges and Solutions | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/MCE.2019.2959108 | - |
dc.identifier.scopusid | 2-s2.0-85083255183 | - |
dc.identifier.wosid | 000525361500001 | - |
dc.identifier.bibliographicCitation | IEEE Consumer Electronics Magazine, v.9, no.3, pp 8 - 16 | - |
dc.citation.title | IEEE Consumer Electronics Magazine | - |
dc.citation.volume | 9 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 8 | - |
dc.citation.endPage | 16 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Hardware & Architecture | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordAuthor | Data privacy | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Servers | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Consumer electronics | - |
dc.subject.keywordAuthor | Data models | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(31538) 22, Soonchunhyang-ro, Asan-si, Chungcheongnam-do, Republic of Korea+82-41-530-1114
COPYRIGHT 2021 by SOONCHUNHYANG UNIVERSITY ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.