基于区块链的联邦学习技术综述

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.

Review of blockchain-based federated learning

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.