基于区块链的联邦学习研究进展  

Research progress of blockchain-based federated learning

作　　者：孙睿 李超[1,2] 王伟 童恩栋[1,2] 王健[1,2] 刘吉强 SUN Rui;LI Chao;WANG Wei;TONG Endong;WANG Jian;LIU Jiqiang(Beijing Key Laboratory of Security and Privacy in Intelligent Transportation(Beijing Jiaotong University),Beijing 100044,China;School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]智能交通数据安全与隐私保护技术北京市重点实验室(北京交通大学),北京100044 [2]北京交通大学计算机与信息技术学院,北京100044

出　　处：《计算机应用》2022年第11期3413-3420,共8页journal of Computer Applications

基　　金：国家重点研发计划项目(2020YFB2103802);中央高校基本科研业务费专项资金资助项目(2019RC038)。

摘　　要：联邦学习(FL)是一种能够实现用户数据不出本地的新型隐私保护学习范式。随着相关研究工作的不断深入,FL的单点故障及可信性缺乏等不足之处逐渐受到重视。近年来,起源于比特币的区块链技术取得迅速发展,它开创性地构建了去中心化的信任,为FL的发展提供了一种新的可能。对现有基于区块链的FL框架进行对比分析,深入讨论区块链与FL相结合所解决的FL重要问题,并阐述了基于区块链的FL技术在物联网(IoT)、工业物联网(IIoT)、车联网(IoV)、医疗服务等多个领域的应用前景。Federated Learning(FL) is a novel privacy-preserving learning paradigm that can keep users’ data locally.With the progress of the research on FL, the shortcomings of FL, such as single point of failure and lack of credibility, are gradually gaining attention. In recent years, the blockchain technology originated from Bitcoin has achieved rapid development, which pioneers the construction of decentralized trust and provides a new possibility for the development of FL. The existing research works on blockchain-based FL were reviewed, the frameworks for blockchain-based FL were compared and analyzed. Then, key points of FL solved by the combination of blockchain and FL were discussed. Finally, the application prospects of blockchain-based FL were presented in various fields, such as Internet of Things(IoT), Industrial Internet of Things(IIoT), Internet of Vehicles(IoV) and medical services.

关 键 词：联邦学习 区块链 结构框架 融合应用 隐私保护 

分 类 号：TP309.2[自动化与计算机技术—计算机系统结构]