An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee.
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| Title: | An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee. |
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| Authors: | Huang, Tiansheng1 cs_tianshenghuang@mail.scut.edu.cn, Lin, Weiwei1 linww@scut.edu.cn, Wu, Wentai2, He, Ligang2 Ligang.He@warwick.ac.uk, Li, Keqin3 lik@newpaltz.edu, Zomaya, Albert Y.4 albert.zomaya@sydney.edu.au |
| Source: | IEEE Transactions on Parallel & Distributed Systems. Jul2021, Vol. 32 Issue 7, p1552-1564. 13p. |
| Subjects: | Fairness, Training of volunteers, Parallel programming, Suretyship & guaranty, Personally identifiable information |
| Abstract: | The issue of potential privacy leakage during centralized AI’s model training has drawn intensive concern from the public. A Parallel and Distributed Computing (or PDC) scheme, termed Federated Learning (FL), has emerged as a new paradigm to cope with the privacy issue by allowing clients to perform model training locally, without the necessity to upload their personal sensitive data. In FL, the number of clients could be sufficiently large, but the bandwidth available for model distribution and re-upload is quite limited, making it sensible to only involve part of the volunteers to participate in the training process. The client selection policy is critical to an FL process in terms of training efficiency, the final model’s quality as well as fairness. In this article, we will model the fairness guaranteed client selection as a Lyapunov optimization problem and then a C2MAB-based method is proposed for estimation of the model exchange time between each client and the server, based on which we design a fairness guaranteed algorithm termed RBCS-F for problem-solving. The regret of RBCS-F is strictly bounded by a finite constant, justifying its theoretical feasibility. Barring the theoretical results, more empirical data can be derived from our real training experiments on public datasets. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Parallel & Distributed Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 148970895 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Huang%2C+Tiansheng%22">Huang, Tiansheng</searchLink><relatesTo>1</relatesTo><i> cs_tianshenghuang@mail.scut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Lin%2C+Weiwei%22">Lin, Weiwei</searchLink><relatesTo>1</relatesTo><i> linww@scut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Wentai%22">Wu, Wentai</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22He%2C+Ligang%22">He, Ligang</searchLink><relatesTo>2</relatesTo><i> Ligang.He@warwick.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Keqin%22">Li, Keqin</searchLink><relatesTo>3</relatesTo><i> lik@newpaltz.edu</i><br /><searchLink fieldCode="AR" term="%22Zomaya%2C+Albert+Y%2E%22">Zomaya, Albert Y.</searchLink><relatesTo>4</relatesTo><i> albert.zomaya@sydney.edu.au</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Parallel+%26+Distributed+Systems%22">IEEE Transactions on Parallel & Distributed Systems</searchLink>. Jul2021, Vol. 32 Issue 7, p1552-1564. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Fairness%22">Fairness</searchLink><br /><searchLink fieldCode="DE" term="%22Training+of+volunteers%22">Training of volunteers</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+programming%22">Parallel programming</searchLink><br /><searchLink fieldCode="DE" term="%22Suretyship+%26+guaranty%22">Suretyship & guaranty</searchLink><br /><searchLink fieldCode="DE" term="%22Personally+identifiable+information%22">Personally identifiable information</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The issue of potential privacy leakage during centralized AI’s model training has drawn intensive concern from the public. A Parallel and Distributed Computing (or PDC) scheme, termed Federated Learning (FL), has emerged as a new paradigm to cope with the privacy issue by allowing clients to perform model training locally, without the necessity to upload their personal sensitive data. In FL, the number of clients could be sufficiently large, but the bandwidth available for model distribution and re-upload is quite limited, making it sensible to only involve part of the volunteers to participate in the training process. The client selection policy is critical to an FL process in terms of training efficiency, the final model’s quality as well as fairness. In this article, we will model the fairness guaranteed client selection as a Lyapunov optimization problem and then a C2MAB-based method is proposed for estimation of the model exchange time between each client and the server, based on which we design a fairness guaranteed algorithm termed RBCS-F for problem-solving. The regret of RBCS-F is strictly bounded by a finite constant, justifying its theoretical feasibility. Barring the theoretical results, more empirical data can be derived from our real training experiments on public datasets. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Parallel & Distributed Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TPDS.2020.3040887 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1552 Subjects: – SubjectFull: Fairness Type: general – SubjectFull: Training of volunteers Type: general – SubjectFull: Parallel programming Type: general – SubjectFull: Suretyship & guaranty Type: general – SubjectFull: Personally identifiable information Type: general Titles: – TitleFull: An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Huang, Tiansheng – PersonEntity: Name: NameFull: Lin, Weiwei – PersonEntity: Name: NameFull: Wu, Wentai – PersonEntity: Name: NameFull: He, Ligang – PersonEntity: Name: NameFull: Li, Keqin – PersonEntity: Name: NameFull: Zomaya, Albert Y. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 10459219 Numbering: – Type: volume Value: 32 – Type: issue Value: 7 Titles: – TitleFull: IEEE Transactions on Parallel & Distributed Systems Type: main |
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