Blockchain-enabled Federated Learning: A Survey
Open accessAbstract
Federated learning (FL) has experienced a boom in recent years, which is jointly promoted by the prosperity of machine learning and Artificial Intelligence along with emerging privacy issues. In the FL paradigm, a central server and local end devices maintain the same model by exchanging model updates instead of raw data, with which the privacy of data stored on end devices is not directly revealed. In this way, the privacy violation caused by the growing collection of sensitive data can be mitigated. However, the performance of FL with a central server is reaching a bottleneck, while new threats are emerging simultaneously. There are various reasons, among which the most significant ones are centralized processing, data falsification, and lack of incentives. To accelerate the proliferation of FL, blockchain-enabled FL has attracted substantial attention from both academia and industry. A considerable number of novel solutions are devised to meet the emerging demands of diverse scenarios. Blockchain-enabled FL provides both theories and techniques to improve the performance of FL from various perspectives. In this survey, we will comprehensively summarize and evaluate existing variants of blockchain-enabled FL, identify the emerging challenges, and propose potentially promising research directions in this under-explored domain.
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Blockchain technology can be used to create a transparent and decentralized model selection process and machine learning process.
“Blockchain technology can also be used to create a transparent and decentralized model selection process and machine learning process [41].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [41]
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Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
Martínez Beltrán, E. T., Pérez, M. Q., Sánchez, P. M. S., Bernal, S. L., Bovet, G., Pérez, M. G., Pérez, G. M., & Celdrán, A. H. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3315746 · Cited by 625 · see citing works
“In contrast, most of the works covering DFL approaches focus on analyzing the use of Distributed Ledger Technology (DLT) technologies (e.g., Blockchain) [30].”
BackgroundBlockchain-empowered Federated Learning: Challenges, Solutions, and Future Directions
Zhu, J., Cao, J., Saxena, D., Jiang, S., & Ferradi, H. (2023) · ACM Computing Surveys · doi:10.1145/3570953 · Cited by 309 · see citing works
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A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems
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Threats and Defenses in the Federated Learning Life Cycle: A Comprehensive Survey and Challenges
Li, Y., Guo, Z., Yang, N., Chen, H., Yuan, D., & Ding, W. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3563537 · Cited by 38 · see citing works
- Authors
- Qu, Y., Uddin, M. P., Gan, C., Xiang, Y., Gao, L., & Yearwood, J.
- Affiliations
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Deakin University (AU)
- Chongqing University of Posts and Telecommunications (CN)
- Qilu University of Technology (CN)
- Venue
- ACM Computing Surveys
- Published
- 2022-11-21
- Volume
- 55
- Issue
- 4
- Pages
- 1-35
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4220883012
- Cited by
- 286
- References
- 141
- Citations by year
- 2026: 55 · 2025: 88 · 2024: 92 · 2023: 44 · 2022: 7
- FWCI
- 19.01
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Locations
- ACM Computing Surveys (publishedVersion)
- Own your potential (DEAKIN) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Australian Research Council (DP220100983)
- 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 › Artificial Intelligence › Cryptography and Data Security
- Keywords
- blockchain, federated learning, privacy preservation, data falsification, incentive mechanisms, data ownership
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.
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