The DOI matches its Crossref record; 94 of the 110 references it cites were found in a registry or an index.
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]
Citations
Cites
ImageNet classification with deep convolutional neural networks
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Distilling the Knowledge in a Neural Network
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Cited by
Sorted by citations
Items 101–101 of 101
Secure SLA-Driven Federated Healthcare Pipeline with Fog-Based Optimization and Adaptive Learning
Kumar, S., Lal, N., & Kumar, K. (2026) · International Conference on Innovative Sustainable Computational Technologies (CISCT) · doi:10.1109/cisct69589.2026.11726978
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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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.
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