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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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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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