Cited by 177Privacy-Preserving Technologies in DataBlockchain Technology Applications and SecurityInternet of Things and AI

Integration of blockchain and federated learning for Internet of Things: Recent advances and future challenges

Ali, Mansoor, Karimipour, Hadis, Tariq, Muhammad·2021·Computers & Security

Integrity signals

Registry

Crossref · 10.1016/j.cose.2021.102355

Cites

109 of 109 references verified0 citation errors · 0 not checkable

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  • In an FL-chain system, participating devices can be identified and verified using their public key, ensuring only trustworthy devices participate.

    “In an FL-chain (FL with blockchain technology) system, the participating devices can be identified and verified using their public key [40]. This ensures that only trustworthy devices are allowed to participate in the learning process.”

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Authors
Ali, M., Karimipour, H., & Tariq, M.
Affiliations
  • National University of Computer and Emerging Sciences (PK)
  • University of Guelph (CA)
Corresponding author
Ali, Mansoor
Venue
Computers & Security
Publisher
Elsevier BV
Published
2021-09
Volume
108
Pages
102355
ISSN
0167-4048 (ISSN-L)
OpenAlex
W3168496996
Cited by
177
References
109
Citations by year
2026: 11 · 2025: 34 · 2024: 53 · 2023: 43 · 2022: 30 · 2021: 6
FWCI
11.67
Type
journal article
Language
en
Open access
closed
Locations
Computers & Security (publishedVersion)
Funders
State Administration of Foreign Experts Affairs
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Keywords
blockchain, federated learning, Internet of Things, privacy preservation, decentralized machine learning, privacy risks, traceability, data privacy

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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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