Blockchain-enabled Federated Learning: Models, Methods and Applications

被引：0
|
作者：
Li C. [1 ,2 ]
Yuan Y. [1 ]
Zheng Z.-Y. [1 ]
Yang D. [2 ]
Wang F.-Y. [3 ,4 ]
机构：
[1] School of Mathematics, Renmin University of China, Beijing
[2] School of Interdisciplinary Studies, Renmin University of China, Beijing
[3] State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing
[4] Institute of Systems Engineering, Macau University of Science and Technology
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2024年 / 50卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Blockchain; federated learning; machine learning; privacy protection; smart contract;
D O I：
10.16383/j.aas.c230336
中图分类号：
学科分类号：
摘要：
In recent years, human society has been witnessed to evolve fast to the era of big data, rendering the data security and privacy protection a key issue for the development of digital economies. Federated learning, as a novel pattern for distributed machine learning, is aimed to train a centralized model from decentralized datasets while protecting user privacy, and is now intensively studied in literature. However, a variety of technical challenges, e.g., centralized architecture, incentive mechanism design, and system-wide security issues, are still awaiting further research efforts. In this respect, blockchain proves to be an elegant solution for federated learning to overcome these issues, and thus has been applied in federated learning in many scenarios with success. In this paper, we proposed the conceptual model for blockchain-enabled federated learning (BeFL) based on a comprehensive review of related literatures, and discussed the key techniques, research issues, as well as the state-of-the-art research progresses. We also investigated potential application scenarios, several key issues to be addressed and the future trends. Our work is aimed at offering useful reference and guidance for establishing a new infrastructure for decentralized, secured and trusted data ecosystem, and also promoting the development of digital economy industries. © 2024 Science Press. All rights reserved.
引用
收藏
页码：1059 / 1085
页数：26
相关论文
共 161 条
  • [21] Ali M, Karimipour H, Tariq M., Integration of blockchain and federated learning for internet of things: Recent advances and future challenges, Computers & Security, 108, (2021)
  • [22] Issa W, Moustafa N, Turnbull B, Sohrabi N, Tari Z., Blockchain-based federated learning for securing internet of things: A comprehensive survey, ACM Computing Surveys, 55, 9, (2023)
  • [23] Zhu J C, Cao J N, Saxena D, Jiang S, Ferradi H., Blockchain-empowered federated learning: Challenges, solutions, and future directions, ACM Computing Surveys, 55, 11, (2023)
  • [24] Javed A R, Hassan M A, Shahzad F, Ahmed W, Singh S, Baker T, Et al., Integration of blockchain technology and federated learning in vehicular (IoT) networks: A comprehensive survey, Sensors, 22, 12, (2022)
  • [25] Qu Y Y, Uddin M P, Gan C Q, Xiang Y, Gao L X, Yearwood J., Blockchain-enabled federated learning: A survey, ACM Computing Surveys, 55, 4, (2023)
  • [26] Li Ling-Xiao, Yuan Sha, Jin Yin-Yu, Review of blockchain-based federated learning, Application Research of Computers, 38, 11, pp. 3222-3230, (2021)
  • [27] Sun Rui, Li Chao, Wang Wei, Tong En-Dong, Wang Jian, Liu Ji-Qiang, Research progress of blockchain-based federated learning, Journal of Computer Applications, 42, 11, pp. 3413-3420, (2022)
  • [28] Saraswat D, Verma A, Bhattacharya P, Tanwar S, Sharma G, Bokoro P N, Et al., Blockchain-based federated learning in UAVs beyond 5G networks: A solution taxonomy and future directions, IEEE Access, 10, pp. 33154-33182, (2022)
  • [29] Hu Y F, Zhou Y H, Xiao J, Wu C., GFL: A decentralized federated learning framework based on blockchain, (2020)
  • [30] Ma C, Li J, Shi L, Ding M, Wang T T, Han Z, Et al., When federated learning meets blockchain: A new distributed learning paradigm, IEEE Computational Intelligence Magazine, 17, 3, (2022)
12345678910