Federated learning scheme for mobile network based on reputation evaluation mechanism and blockchain

Ming YANG; Xuexian HU; Qihui ZHANG; Jianghong WEI; Wenfen LIU

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

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Federated learning scheme for mobile network based on reputation evaluation mechanism and blockchain

TopicⅡ: Blockchain Technology | 更新时间：2024-06-03

- Federated learning scheme for mobile network based on reputation evaluation mechanism and blockchain
- Chinese Journal of Network and Information Security Vol. 7, Issue 6, Pages: 99-112(2021)
- 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：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2021083
  CLC： TP399
- Online First：2021-12，
  
  Published：15 December 2021
- 稿件说明：
移动端阅览
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：

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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