A survey of privacy-preserving federated learning for intrusion detection systems.
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| Title: | A survey of privacy-preserving federated learning for intrusion detection systems. |
|---|---|
| Authors: | Bunko, Thomas1, tbunko@our.ecu.edu.au, Johnstone, Michael N.1, m.johnstone@ecu.edu.au, Yang, Wencheng2, wencheng.yang@unisq.edu.au, Scott, Ben A.3,4, ben.scott@mq.edu.au |
| Source: | Artificial Intelligence Review; May2026, Vol. 59 Issue 5, p1-47, 47p |
| Database: | Applied Science & Technology Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 192482050 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10462-026-11519-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 47 StartPage: 1 Titles: – TitleFull: A survey of privacy-preserving federated learning for intrusion detection systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bunko, Thomas – PersonEntity: Name: NameFull: Johnstone, Michael N. – PersonEntity: Name: NameFull: Yang, Wencheng – PersonEntity: Name: NameFull: Scott, Ben A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02692821 Numbering: – Type: volume Value: 59 – Type: issue Value: 5 Titles: – TitleFull: Artificial Intelligence Review Type: main |
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