Research on Federated Learning‐Based Privacy Protection Strategies Under Edge Computing Scenarios.
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| Title: | Research on Federated Learning‐Based Privacy Protection Strategies Under Edge Computing Scenarios. |
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| Authors: | Liu, Hongxia1 (AUTHOR), Hu, Xiangben2 (AUTHOR) huxiangben@gmail.com, huang, haiping (AUTHOR) hhp@njupt.edu.cn |
| Source: | International Journal of Distributed Sensor Networks. 5/3/2026, Vol. 2026, p1-17. 17p. |
| Subjects: | Federated learning, Edge computing, Machine learning, Privacy, Network performance |
| Abstract: | With the advent of billions of end devices connected to the edge of the internet, a large amount of valuable edge data is generated in production and life. When raw data is trained centrally, the risk of privacy leakage exists. Federated learning protects local data privacy by uniting multiple computing nodes for efficient machine learning without sharing data. However, federated learning faces the problem of data sparsity in practical applications, which makes global models difficult to train. In addition, explosive data growth reduces the availability of training models and tends to bring network transmission pressure. For solving these issues, this paper proposed a privacy‐preserving strategy based on joint learning in edge computing scenarios. First, the concept of parallel over‐parameterization is utilized to dynamically sparsely train joint learning models to reduce the resources occupied by communication and computation. Second, the privacy preservation of model parameters is investigated by adding perturbations to model parameters to ensure the security of model uploading to edge servers. Eventually, this paper conducted comparison experiments with HFL, DP‐FedAvg, and SAFL. The experimental results demonstrate that the method proposed in this paper has certain advantages in privacy protection. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Distributed Sensor Networks is the property of Wiley-Blackwell 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193465550 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Research on Federated Learning‐Based Privacy Protection Strategies Under Edge Computing Scenarios. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Hongxia%22">Liu, Hongxia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Xiangben%22">Hu, Xiangben</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> huxiangben@gmail.com</i><br /><searchLink fieldCode="AR" term="%22huang%2C+haiping%22">huang, haiping</searchLink> (AUTHOR)<i> hhp@njupt.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Distributed+Sensor+Networks%22">International Journal of Distributed Sensor Networks</searchLink>. 5/3/2026, Vol. 2026, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Federated+learning%22">Federated learning</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Privacy%22">Privacy</searchLink><br /><searchLink fieldCode="DE" term="%22Network+performance%22">Network performance</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the advent of billions of end devices connected to the edge of the internet, a large amount of valuable edge data is generated in production and life. When raw data is trained centrally, the risk of privacy leakage exists. Federated learning protects local data privacy by uniting multiple computing nodes for efficient machine learning without sharing data. However, federated learning faces the problem of data sparsity in practical applications, which makes global models difficult to train. In addition, explosive data growth reduces the availability of training models and tends to bring network transmission pressure. For solving these issues, this paper proposed a privacy‐preserving strategy based on joint learning in edge computing scenarios. First, the concept of parallel over‐parameterization is utilized to dynamically sparsely train joint learning models to reduce the resources occupied by communication and computation. Second, the privacy preservation of model parameters is investigated by adding perturbations to model parameters to ensure the security of model uploading to edge servers. Eventually, this paper conducted comparison experiments with HFL, DP‐FedAvg, and SAFL. The experimental results demonstrate that the method proposed in this paper has certain advantages in privacy protection. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Distributed Sensor Networks is the property of Wiley-Blackwell 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193465550 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/dsn/1237027 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Federated learning Type: general – SubjectFull: Edge computing Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Privacy Type: general – SubjectFull: Network performance Type: general Titles: – TitleFull: Research on Federated Learning‐Based Privacy Protection Strategies Under Edge Computing Scenarios. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Hongxia – PersonEntity: Name: NameFull: Hu, Xiangben – PersonEntity: Name: NameFull: huang, haiping IsPartOfRelationships: – BibEntity: Dates: – D: 03 M: 05 Text: 5/3/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 15501329 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: International Journal of Distributed Sensor Networks Type: main |
| ResultId | 1 |
