Cited by 286Privacy-Preserving Technologies in DataBlockchain Technology Applications and SecurityCryptography and Data Security

Blockchain-enabled Federated Learning: A Survey

Open access
Qu, Youyang, Uddin, Md Palash, Gan, Chenquan, Xiang, Yong, Gao, Longxiang, Yearwood, John·2023·ACM Computing Surveys

Abstract

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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Crossref · 10.1145/3524104

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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
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

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