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作 者:林庆新 余锋 胡志强 曾凌静[4] LIN Qing-xin;YU Feng;HU Zhi-qiang;ZENG Ling-jing(Department of Computer Engineering,Fuzhou University Zhicheng College,Fuzhou 350002,China;College of Computer and Cyber Security,Fujian Normal University,Fuzhou 350017,China;Institute 706,Second Academy of China Aerospace Science and Industry Corporation,Beijing 100854,China;School of Information and Intelligent Transportation,Fujian Chuanzheng Communications College,Fuzhou 350004,China)
机构地区:[1]福州大学至诚学院计算机工程系,福建福州350002 [2]福建师范大学计算机与网络安全学院,福建福州350017 [3]中国航天科工集团第二研究院七〇六所,北京100854 [4]福建船政交通职业学院信息与智慧交通学院,福建福州350004
出 处:《计算机工程与设计》2023年第12期3571-3577,共7页Computer Engineering and Design
基 金:福建省教育厅中青年教师教育科研基金项目(JAT220836)。
摘 要:在传统的联邦学习系统中,中央服务器在接收到参与方上传的本地模型后,没有对本地模型的合法性进行验证,可能导致后续模型不可靠即模型存储可靠性问题。针对该问题提出一种基于区块链隐私增强的联邦学习可验证安全存储方案(blockchain-based federated learning for model verified security storage, BFL4MVSS)。利用区块链技术,提出验证者投票机制,保证存储模型的可靠性;引入一种奖励机制,允许系统在一定数量的恶意节点存在下正常运行。采用PyTorch框架在MNIST数据集上进行仿真验证,结果表明,其能够在15%的恶意节点的参与下确保模型具有良好的表现。In traditional federated learning systems,the central server does not verify the legitimacy of the local model after receiving it from the participants,which may lead to unreliable subsequent models,i.e.model storage reliability issues.A blockchain based federated learning for model verified security storage(BFL4MVSS)scheme based on blockchain privacy enhancement was proposed to address this issue.By utilizing blockchain technology,a validator voting mechanism was proposed to ensure the reliability of the storage model.A reward mechanism was introduced to allow the system to operate normally in the presence of a certain number of malicious nodes.The PyTorch framework was used for simulation verification on the MNIST dataset.The results show that the model can ensure good performance with the participation of 15%of malicious nodes.
关 键 词:BFL4MVSS 联邦学习 隐私增强 区块链 投票机制 奖励机制 仿真验证
分 类 号:TP301.6[自动化与计算机技术—计算机系统结构]
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