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A Review of Privacy-Preserving Federated Learning for the Internet-of-Things
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“Federated learning is a trending approach to resolve privacy issues in learning problems [275,276,277,278].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [276]
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Wang, B., & Hua, X. (2026) · Cluster Computing · doi:10.1007/s10586-026-06141-6
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Metadata
- Authors
- Briggs, C., Fan, Z., & Andras, P.
- Affiliations
- Keele University (GB)
- Corresponding author
- Briggs, Christopher
- ORCID
- Fan, Zhong
- Venue
- Studies in Computational Intelligence
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 21-50
- ISSN
- 1860-949X (ISSN-L)
- 1860-9503
- OpenAlex
- W3084130864
- Cited by
- 101
- References
- 110
- Citations by year
- 2026: 9 · 2025: 24 · 2024: 29 · 2023: 14 · 2022: 12 · 2021: 12
- FWCI
- 28.01
- Type
- book chapter
- Language
- en
- Open access
- green
- eprints.keele.ac.uk
- Locations
- Studies in computational intelligence (publishedVersion)
- Keele Research Repository (Keele University) (submittedVersion, cc-by)
- Edinburgh Napier Research Repository (Edinburgh Napier University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- privacy-preserving federated learning, Internet of Things, data privacy, communication cost reduction, data heterogeneity, user-generated data, distributed learning, deep learning, personal data protection
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