基于信誉评估机制和区块链的移动网络联邦学习方案

杨明; 胡学先; 张启慧; 魏江宏; 刘文芬

doi:10.11959/j.issn.2096-109x.2021083

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基于信誉评估机制和区块链的移动网络联邦学习方案

专栏Ⅱ：区块链技术 | 更新时间：2024-06-03

- 基于信誉评估机制和区块链的移动网络联邦学习方案
- Federated learning scheme for mobile network based on reputation evaluation mechanism and blockchain
- 网络与信息安全学报 2021年7卷第6期页码：99-112
- 作者机构：
  
  1. 信息工程大学，河南郑州 450001
  2. 桂林电子科技大学，广西桂林541004
- 作者简介：
  
  [ "杨明（1994− ），男，内蒙古包头人，信息工程大学硕士生，主要研究方向为大数据安全、区块链技术" ]
  [ "胡学先（1982− ），男，湖北红安人，博士，信息工程大学副教授，主要研究方向为安全协议、大数据安全" ]
  [ "张启慧（1983− ），女，安徽阜阳人，博士，信息工程大学讲师，主要研究方向为安全协议、大数据安全" ]
  [ "魏江宏（1987− ），男，甘肃定西人，博士，信息工程大学讲师，主要研究方向为属性基密码、大数据安全" ]
  [ "刘文芬（1965− ），女，湖北安陆人，桂林电子科技大学教授、博士生导师，主要研究方向为网络通信、信息安全" ]
- 基金信息：
  
  国家自然科学基金;TheNational Natural Science Foundation of China(62172433);国家自然科学基金;TheNational Natural Science Foundation of China(62172434);国家自然科学基金;TheNational Natural Science Foundation of China(61862011);国家自然科学基金;TheNational Natural Science Foundation of China(61872449);广西密码学与信息安全重点实验室研究课题;Guangxi Key Laboratory of Cryptography and Information Security(GCIS201704)
- DOI：10.11959/j.issn.2096-109x.2021083
  中图分类号： TP399
- 网络首发：2021-12，
  
  纸质出版：2021-12-15
- 稿件说明：
移动端阅览
杨明, 胡学先, 张启慧, 等. 基于信誉评估机制和区块链的移动网络联邦学习方案[J]. 网络与信息安全学报, 2021,7(6):99-112.

Ming YANG, Xuexian HU, Qihui ZHANG, et al. Federated learning scheme for mobile network based on reputation evaluation mechanism and blockchain[J]. Chinese Journal of Network and Information Security, 2021, 7(6): 99-112.
杨明, 胡学先, 张启慧, 等. 基于信誉评估机制和区块链的移动网络联邦学习方案[J]. 网络与信息安全学报, 2021,7(6):99-112. DOI： 10.11959/j.issn.2096-109x.2021083.

Ming YANG, Xuexian HU, Qihui ZHANG, et al. Federated learning scheme for mobile network based on reputation evaluation mechanism and blockchain[J]. Chinese Journal of Network and Information Security, 2021, 7(6): 99-112. DOI： 10.11959/j.issn.2096-109x.2021083.

摘要

联邦学习是一种新兴的分布式机器学习技术，通过将训练任务下放到用户端，仅将训练得到的模型参数发送给服务端，整个过程并不需要参与方直接共享数据，从而很大限度上规避了隐私问题。然而，这种学习模式中移动用户间没有预先建立信任关系，用户之间进行合作训练时会存在安全隐患。针对上述问题，提出一种基于信誉评估机制和区块链的移动网络联邦学习方案，该方案允许服务端利用主观逻辑模型对参与训练的移动用户进行信誉评估，并且基于区块链智能合约技术为其提供可信的信誉意见共享环境和动态访问策略接口。理论和实验分析结果表明，此方案可以使服务端选择可靠的用户进行训练，同时能够实现更公平和有效的信誉计算，提高联邦学习的准确性。

Abstract

Federated learning is a new distributed machine learning technology

where training tasks are deployed on user side and training model parameters are sent to the server side.In the whole process

participants do not need to share their own data directly

which greatly avoids privacy issues.However

the trust relationship between mobile users in the learning model has not been established in advance

there is hidden safety when users perform cooperative train with each other.In view of the above problems

a federated learning scheme for mobile network based on reputation evaluation mechanism and blockchain was proposed.The scheme allowed the server side to use subjective logic models to evaluate the reputation of participating mobile users and provided them with credible reputation opinions sharing environment and dynamic access strategy interface based on the technique of smart contract of blockchain.Theoretical and experimental analys is results show that the scheme can enable the server side to select reliable users for training.And it can achieve more fair and effective reputation calculations

which improves the accuracy of federated learning.

关键词

Keywords

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