基于多级代理许可区块链的联邦边缘学习模型

Federated edge learning model based on multi-level proxy permissioned blockchain

针对零信任边缘计算环境下联邦学习面临的隐私安全及学习效率低等问题,提出了一种边缘计算中基于多级代理许可区块链的联邦学习模型,设计多级代理许可区块链构建联邦边缘学习可信底层环境,实现分层模型聚合方案缓解模型训练压力,利用秘密共享和差分隐私设计混合策略增强模型隐私。针对边缘客户端可信度为零或极差的问题,设计了基于信誉验证的联邦任务节点选择算法,将正向训练样本及本地模型作为信誉奖励,完善安全验证方案,进一步保证模型抵御恶意敌手攻击的有效性。实验结果表明,在40%恶意敌手的攻击下,相较于现有的先进方案,所提方案准确率提升了10%,以较高的模型准确率实现了较高的隐私安全。

Aiming at the problems of privacy security and low learning efficiency faced by federated learning in zero trust edge computing environment,a federated learning model based on multi-level proxy permission blockchain for edge computing was proposed.The multi-level proxy permission blockchain was designed to establish a trusted underlying environment for federated edge learning,and the hierarchical model aggregation scheme was implemented to alleviate the pressure of model training.A hybrid strategy was devised to enhance model privacy using secret sharing and differential privacy.A federated task node selection algorithm based on reputation verification was devised to address the problem of zero or extremely poor credibility of edge clients.Positive training samples and the local model were utilized as reputation rewards to refine the security verification scheme,and further ensure the effectiveness of the model against malicious adversaries.Experimental results show that under the attack of 40%malicious adversaries,compared with the existing advanced schemes,the accuracy of the proposed scheme is improved by 10%,and high privacy security is achieved with high model accuracy.