基于区块链的联邦学习研究综述  

Review of Research on Blockchain-Based Federated Learning

作　　者：兰浩良 王群[1] 徐杰 薛益时 张勃 LAN Haoliang;WANG Qun;XU Jie;XUE Yishi;ZHANG Bo(Department of Computer Information and Network Security,Jiangsu Police Institute,Nanjing 210031,China;Intelligence Command Center of Taixing Municipal Public Security Bureau,Taixing 225400,China)

机构地区：[1]江苏警官学院计算机信息与网络安全系,南京210031 [2]泰兴市公安局情报指挥中心,泰兴225400

出　　处：《信息网络安全》2024年第11期1643-1654,共12页Netinfo Security

基　　金：国家自然科学基金(62202209);江苏省社科应用研究精品工程课题(23SYC-127);江苏高校哲学社会科学研究一般项目(2022JYB0471);江苏警官学院科研启动金(2911121110);江苏警官学院自然科学研究项目(2022SJYZZ05)。

摘　　要：基于区块链的联邦学习作为一种新兴的去中心化的分布式机器学习新范式,其在克服传统联邦学习所面临的数据孤岛、隐私泄露以及安全威胁等不足的同时,也面临着区块链技术在成本、效率以及有效性等方面带来的新挑战。为此,文章首先结合基本原理、技术分类、优势以及待解决问题对联邦学习和区块链进行阐述。在此基础上,文章围绕联邦学习与区块链所涉及的架构、性能、隐私性、安全性、激励机制、共识机制等对基于区块链的联邦学习研究进行了系统的总结分析。最后,文章从基于区块链的联邦学习原理、平衡性以及应用三个维度,探讨未来的研究趋势和亟待解决的主要问题。As an emerging decentralized distributed machine learning paradigm,blockchain based federated learning not only overcomes the shortcomings of traditional federated learning such as data silos,privacy breaches,and security threats,but also faces new challenges in terms of cost,efficiency,and effectiveness brought by blockchain technology.Therefore,this article first elaborated on federated learning and blockchain by combining basic principles,technical classifications,complementary advantages,and unresolved problems.On this basis,a systematic summary and analysis of current research on blockchain based federated learning was conducted around the architecture,performance,privacy,security,incentive mechanisms,consensus mechanisms,and applications involved in the combination of federated learning and blockchain.Finally,starting from the three dimensions of blockchain based federated learning itself,balance,and application,explored its future research trends and the main problems that urgently need to be solved.

关 键 词：机器学习 区块链 联邦学习 去中心化 分布式 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]