基于智能合约和联邦存储的异步联邦学习模型  

Asynchronous Federated Model of Public Health Emergency Monitoring Based on Smart Contract and Federated Storage

作　　者：刘星辰 杜军平[1,2] 梁美玉 李昂[1,2] LIU Xingchen;DU Junping;LIANG Meiyu;LI Ang(School of Computer Science(National Pilot School of Software Engineering),Beijing University of Posts and Telecommunications,Beijing 100876,China;Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia(Beijing University of Posts and Telecommunications),Beijing 100876,China)

机构地区：[1]北京邮电大学计算机学院(国家示范性软件学院),北京100876 [2]智能通信软件与多媒体北京市重点实验室(北京邮电大学),北京100876

出　　处：《数据采集与处理》2024年第6期1532-1542,共11页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(U22B2038,62192784,62172056)。

摘　　要：公共安全突发事件中对数据安全的重视程度越来越高,联邦学习由于不再需要上传数据到中心服务器进行计算,减少了隐私泄露的可能而受到广泛关注。然而当前基于智能合约的联邦学习由于运算较大,存在着效率低等缺陷,因此本文提出了一种面向公共卫生突发事件检测的智能合约与联邦存储的异步联邦学习方法。该方法允许联邦节点在任何时间加入和退出联邦学习;依托智能合约与分布存储,进一步增加了公共卫生安全领域的数据安全与训练效率;同时采用自适应的差分隐私对其上传到分布式存储节点的梯度进行动态保护,进一步降低了隐私泄露的风险。在公共数据集和公共卫生安全数据集上大量的实验表明,本文提出的方法在精度上优于已知的对比方法,且在达到相同精度的情况下所需时间更少。With the increasing emphasis on data security in public safety emergencies,federated learning has gained attention for its ability to perform computations without uploading data to a central server,thereby reducing the risk of privacy breaches.However,current federated learning approaches based on smart contracts face challenges such as inefficiency due to their computational demands.To address it,this paper proposes an asynchronous federated learning method for detecting public health emergencies,integrating smart contracts and federated storage.This approach allows federated nodes to join and leave the federated learning process at any time.By leveraging smart contracts and distributed storage,it enhances data security and training efficiency in the public health domain.Furthermore,adaptive differential privacy is employed to dynamically protect the gradients uploaded to distributed storage nodes,further reducing the risk of privacy leakage.Extensive experiments conducted on public datasets and public health security datasets demonstrate that the proposed method outperforms existing approaches in terms of accuracy and requires less time to achieve the same level of precision.

关 键 词：智能合约 联邦学习 公共卫生突发事件 联邦存储 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]