自动化学报2024，Vol.50Issue(6)：1059-1085,27.DOI:10.16383/j.aas.c230336
基于区块链的联邦学习:模型、方法与应用
Blockchain-enabled Federated Learning:Models,Methods and Applications
摘要
Abstract
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 chal-lenges,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 over-come 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 pro-gresses.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 decent-ralized,secured and trusted data ecosystem,and also promoting the development of digital economy industries.关键词
区块链/联邦学习/智能合约/机器学习/隐私保护Key words
Blockchain/federated learning/smart contract/machine learning/privacy protection引用本文复制引用
李程,袁勇,郑志勇,杨东,王飞跃..基于区块链的联邦学习:模型、方法与应用[J].自动化学报,2024,50(6):1059-1085,27.基金项目
国家自然科学基金(72171230),澳门科学技术发展基金(0050/2020/A1),北京市未来区块链与隐私计算高精尖创新中心项目资助 Supported by National Natural Science Foundation of China(72171230),Science and Technology Development Fund of Ma-cau(0050/2020/A1),and Beijing Future Blockchain and Privacy Computing Advanced Innovation Center （72171230）
