区块链赋能联邦学习:方法、挑战与展望  

Blockchain Enabled Federated Learning:Approaches,Challenges,and Prospects

作　　者：孙恩昌 董潇炫 张卉 李梦思 张冬英 SUN Enchang;DONG Xiaoxuan;ZHANG Hui;LI Mengsi;ZHANG Dongying(Faulty of Informational Technology,Beijing University of Technology,Beijing 100124,China;Beijing Laboratory of Advanced Information Networks,Beijing 100124,China;Network and Information Technology Center,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学信息学部,北京100124 [2]先进信息网络北京实验室,北京100124 [3]北京工业大学网络与信息技术中心,北京100124

出　　处：《北京工业大学学报》2025年第3期337-349,共13页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(61671029);北京市自然科学基金资助项目(L211002);北京市教育委员会科技计划资助项目(KM202110005021)。

摘　　要：针对区块链技术与联邦学习(federated learning,FL)结合后在安全、隐私等方面存在的问题,对区块链赋能FL中的相关方法进行综述与分析。首先,分别阐述了FL和区块链,并在此基础上总结了区块链赋能FL的前沿通用架构;其次,研究了目前安全、隐私、激励以及效率方法的进展,分析了各方法的优缺点;最后,指出了区块链赋能FL目前存在的问题,提出了解决方案,并进行了展望。In response to security and privacy in the integration of blockchain technology with federated learning(FL),comprehensive review and analysis of the relevant methods for empowering FL with blockchain are provided.First,FL and blockchain were elucidated separately,and on the basis of this,the state-of-the-art general architectures for blockchain-enabled FL were summarized.Second,the progress in security,privacy,incentives,and efficiency methods was investigated,and the advantages and disadvantages of each method were analyzed.Finally,the current issues in blockchain enabled FL were identified,and potential solutions were proposed,along with future prospects.

关 键 词：联邦学习(federated learning FL) 区块链 数据安全 数据隐私 激励机制 效率 

分 类 号：TN915[电子电信—通信与信息系统]