融合云代理分流重加密的去中心化联邦学习模型  

Decentralized Federated Learning with Cloud Proxy Group Re-Encryption

在线阅读下载全文

作  者:杜宛蓉 文斌[1,2] 周尚 刘文龙 马梦帅 DUWanrong;WEN Bin;ZHOU Shang;LIUWenlong;MAMengshuai(School of Information Science and Technology,Hainan Normal University,Haikou,Hainan 571158,China;Key Laboratory of Data Science and Smart Education of Ministry of Education,Hainan Normal University,Haikou,Hainan 571158,China)

机构地区:[1]海南师范大学,信息科学技术学院,海南海口571158 [2]数据科学与智慧教育教育部重点实验室(海南师范大学),海南海口571158

出  处:《数据与计算发展前沿(中英文)》2025年第2期86-95,共10页Frontiers of Data & Computing

基  金:国家自然科学基金“区块链数据服务支持高可用数据共享与交易实现机理研究(62362029)”;国家自然科学基金“面向数据跨境流动的市场化多方数据安全共享研究(62462029)”;海南省自然科学基金“GNN支持网络安全态势感知数据分析与异常行为检测研究(623RC485)”。

摘  要:【目的】旨在解决联邦学习模型参数被篡改、服务器高并发性带来的通信延迟数据训练的效率低下等问题。【方法】本文提出了一种融合云代理分流重加密的去中心化联邦学习分层数据共享框架。该框架利用区块链技术对服务器进行去中心化处理,在边缘层上构建了区块链网络,使终端设备能够就近与边缘设备通信传输,从而降低时间开销。又在区块链基础上融合了云代理池分流重加密共享算法,选择最优代理节点的同时得到安全保证,实现了数据安全共享与可信访问控制。【结果】通过实验验证了本文提出的共享框架相比于传统的联邦学习数据共享框架,显著减少模型的通信时间,降低模型的通信延迟。【结论】实验结果表明该框架在TPS性能、抗合谋能力、可监管性和扩展性方面表现优异,使数据共享更加高效、安全。[Objective]This paper aims to address issues such as parameter tampering in federated learning models and the inefficiencies associated with communication delays during data training,which are exacerbated by high server concurrency.[Methods]A decentralized hierarchical data sharing framework for federated learning is proposed,integrating cloud agents and employing re-encryption techniques.This framework is designed to leverage block-chain technology to decentralize server operations and to establish a block-chain network at the edge layer,thereby enabling terminal devices to communicate and transmit data with nearby edge devices,reducing time overhead.Based on this blockchain foundation,a cloud agent pool along with a re-encryption sharing algorithm is incorporated to ensure security while selecting optimal agent nodes,facilitating secure data sharing and trusted access control.[Results]Experimental results demonstrate that the proposed sharing framework significantly reduces communication time and model delay compared to traditional federated learning data-sharing frameworks.[Conclusions]The experimental findings indicate that the framework exhibits outstanding performance in terms of transactions per second(TPS),resistance to collusion,controllability,and scalability,ultimately enhancing both the efficiency and security of data sharing.

关 键 词:联邦学习 区块链 云代理分流重加密 

分 类 号:TP393.0[自动化与计算机技术—计算机应用技术] TP309[自动化与计算机技术—计算机科学与技术]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象