数据与计算发展前沿 ›› 2025, Vol. 7 ›› Issue (2): 86-95.

CSTR: 32002.14.jfdc.CN10-1649/TP.2025.02.009

doi: 10.11871/jfdc.issn.2096-742X.2025.02.009

• 技术与应用 • 上一篇    下一篇

融合云代理分流重加密的去中心化联邦学习模型

杜宛蓉1,2(),文斌1,2,*(),周尚1,2,刘文龙1,2,马梦帅1,2   

  1. 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
  • 收稿日期:2024-10-28 出版日期:2025-04-20 发布日期:2025-04-23
  • 通讯作者: 文斌
  • 作者简介:杜宛蓉，海南师范大学，硕士研究生，主要研究方向为大数据服务共享，联邦学习。
    本文主要承担工作为实验设计、实验方法改进、分析和处理数据、论文撰写。
    DU Wanrong is a master’s student at the Hainan Normal University of China. Her research interests include big Data service sharing and federal learning.
    In this paper, she is responsible for experimental design, experimental method improvement, data analysis and processing, and paper writing.
    E-mail: 1016935568@qq.com|文斌，工学博士、海南师范大学教授，云计算与大数据研究中心主任，数据科学与智慧教育教育部重点实验室责任教授，海南省人工智能学会区块链专委会主任，中国计算机学会软件工程专委会、服务计算专委会执行委员，已经出版学术专著5部、发表学术论文40余篇、授权发明专利2项，主要研究领域为网络异常行为检测、软件安全、大数据服务共享与交易。
    在本文中负责论文修改和指导相关工作。
    WEN Bin, Ph.D., is a professor at the Hainan Normal University, Director of Cloud Computing and Big Data Research Center, Responsible Professor of the Key Laboratory of Data Science and Smart Education, Ministry of Education, Director of Blockchain Special Committee of Hainan Artificial Intelligence Society, Executive Member of Software Engineering Special Committee and Service Computing Special Committee of China Computer Federation. He has published 5 academic monographs, over 40 academic papers, and 2 authorized invention patents. His research interests include network abnormal behavior detection, software security, big data service sharing and trading.
    In this paper, he is responsible for revising and guiding related work in this article.
    E-mail: binwen@hainnu.edu.cn
  • 基金资助:
    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)

Decentralized Federated Learning with Cloud Proxy Group Re-Encryption

DU Wanrong1,2(),WEN Bin1,2,*(),ZHOU Shang1,2,LIU Wenlong1,2,MA Mengshuai1,2   

  1. 1. School of Information Science and Technology, Hainan Normal University, Haikou, Hainan 571158, China
    2. Key Laboratory of Data Science and Smart Education of Ministry of Education, Hainan Normal University, Haikou, Hainan 571158, China
  • Received:2024-10-28 Online:2025-04-20 Published:2025-04-23
  • Contact: WEN Bin

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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。

关键词: 联邦学习, 区块链, 云代理分流重加密

Abstract:

[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 block-chain 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.

Key words: federated learning, blockchain, proxy re-encrypti