基于区块链的联邦学习技术综述  

Review of blockchain-based federated learning

作　　者：李凌霄 袁莎 金银玉 Li Lingxiao;Yuan Sha;Jin Yinyu(Institute of Online Education,Beijing Foreign Studies University,Beijing 100089,China;Artificial Intelligence&Human Languages Laboratory,Beijing Foreign Studies University,Beijing 100089,China;Beijing Academy of Artificial Intelligence,Beijing 100084,China)

机构地区：[1]北京外国语大学网络教育学院,北京100089 [2]北京外国语大学人工智能与人类语言重点实验室,北京100089 [3]北京智源人工智能研究院,北京100084

出　　处：《计算机应用研究》2021年第11期3222-3230,共9页Application Research of Computers

基　　金：国家重点研发计划资助项目(2020AAA0105200);国家自然科学基金资助项目(61806111)。

摘　　要：联邦学习与区块链在应用领域、架构特点、隐私保护机制等方面具有很强的共性、互补性和契合度,近年来,一些研究与应用将两种技术结合起来,在数据隐私保护强度、数据共享激励机制、计算性能等方面取得了不少进展。为了帮助研究者掌握联邦学习结合区块链的最新研究成果与发展方向,对基于区块链的联邦学习进行了综述。首先,介绍了联邦学习技术的相关研究和存在的不足;其次,详细讨论了当前基于区块链的联邦学习的相关研究,重点从架构特点、资源分配、安全机制、激励机制等方面进行了分析;最后,总结了基于区块链的联邦学习应用在人工智能领域的未来发展趋势和需要关注的问题。Federated learning and blockchain have a strong commonality,complementarity and compatibility in application fields,architectural features and privacy protection mechanisms.In recent years,some research and applications combined the two technologies and made a lot of progress in terms of data privacy protection,data sharing and computational performance.To help researchers grasp the latest research achievements and development directions,this paper presented a review of blockchain-based federated learning.Firstly,this paper presented the relevant research and the shortcomings of federated learning.Then,it discussed the current research related to blockchain-based federated learning,mainly focused on the architectural features,resources allocation,security mechanism and incentive mechanism.Finally,this paper concluded the future trends and issues of blockchain-based federated learning.

关 键 词：联邦学习 区块链 人工智能 数据安全 数据隐私 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]