The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Privacy-preserving blockchain-based federated learning for traffic flow prediction
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“Reference [23] added noise on the raw data by local differential privacy, but it reduced the model accuracy severely.”
From A Blockchain‐Based Federated Learning Method for Smart Healthcare (2021) · cites this paper as [23]
Citations
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“[120] introduced a blockchain-enabled FL framework for securing the control of urban traffic flow.”
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Evaluating Federated Learning for intrusion detection in Internet of Things: Review and challenges
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Metadata
- Authors
- Qi, Y., Hossain, M. S., Nie, J., & Li, X.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- King Saud University (SA)
- Nanyang Technological University (SG)
- Corresponding author
- Hossain, M. Shamim
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 117
- Pages
- 328-337
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W3112885687
- Cited by
- 339
- References
- 55
- Citations by year
- 2026: 31 · 2025: 49 · 2024: 99 · 2023: 70 · 2022: 60 · 2021: 30
- FWCI
- 15.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Future Generation Computer Systems (publishedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Funders
- King Saud University, Riyadh, Saudi Arabia
- Key Project in Higher Education of Guangdong Province, China (2020ZDZX3030)
- Young Innovation Talents Project in Higher Education of Guangdong Province, China (2018KQNCX333)
- 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 › Computer Networks and Communications › Cryptography and Data Security
- Keywords
- traffic flow prediction, federated learning, blockchain, consortium blockchain, differential privacy, data poisoning, privacy preservation, intelligent transportation systems, noise addition
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