基于激励机制的联邦学习优化算法

田有亮; 吴柿红; 李沓; 王林冬; 周骅

doi:10.11959/j.issn.1000-436x.2023095

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基于激励机制的联邦学习优化算法

学术论文 | 更新时间：2024-06-06

- 基于激励机制的联邦学习优化算法
- Federated learning optimization algorithm based on incentive mechanism
- 通信学报 2023年44卷第5期页码：169-180
- 作者机构：
  
  1. 贵州大学公共大数据国家重点实验室，贵州贵阳 550025
  2. 贵州大学计算机科学与技术学院，贵州贵阳 550025
  3. 贵州大学密码学与数据安全研究所，贵州贵阳 550025
  4. 贵州大学大数据与信息工程学院，贵州贵阳 550025
- 作者简介：
  
  [ "田有亮（1982- ），男，贵州盘州人，博士，贵州大学教授、博士生导师，主要研究方向为博弈论、密码学与安全协议、大数据隐私保护" ]
  [ "吴柿红（1995- ），女，贵州福泉人，贵州大学硕士生，主要研究方向为联邦学习、密码学等" ]
  [ "李沓（1998- ），男，贵州盘州人，贵州大学博士生，主要研究方向为密码学与区块链技术" ]
  [ "王林冬（1997- ），男，浙江杭州人，贵州大学硕士生，主要研究方向为数字水印、信息安全等" ]
  [ "周骅（1978- ），男，江苏无锡人，博士，贵州大学副教授、硕士生导师，主要研究方向为物联网安全、硬件安全机制、电路与系统" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2021YFB3101100);国家自然科学基金资助项目(U1836205);国家自然科学基金资助项目(62272123);贵州省高层次创新型人才基金资助项目([2020]6008);贵阳市科技计划基金资助项目([2021]1-5);贵阳市科技计划基金资助项目([2022]2-4);贵州省科技计划基金资助项目([2020]5017);贵州省科技计划基金资助项目([2022]065);贵州大学人才引进基金资助项目([2015]-53)
- DOI：10.11959/j.issn.1000-436x.2023095
  中图分类号： TN92
- 网络首发：2023-05，
  
  纸质出版：2023-05-25
- 稿件说明：
移动端阅览
田有亮, 吴柿红, 李沓, 等. 基于激励机制的联邦学习优化算法[J]. 通信学报, 2023,44(5):169-180.

Youliang TIAN, Shihong WU, Ta LI, et al. Federated learning optimization algorithm based on incentive mechanism[J]. Journal on Communications, 2023, 44(5): 169-180.
田有亮, 吴柿红, 李沓, 等. 基于激励机制的联邦学习优化算法[J]. 通信学报, 2023,44(5):169-180. DOI： 10.11959/j.issn.1000-436x.2023095.

Youliang TIAN, Shihong WU, Ta LI, et al. Federated learning optimization algorithm based on incentive mechanism[J]. Journal on Communications, 2023, 44(5): 169-180. DOI： 10.11959/j.issn.1000-436x.2023095.

摘要

针对联邦学习的训练过程迭代次数多、训练时间长、效率低等问题，提出一种基于激励机制的联邦学习优化算法。首先，设计与时间和模型损失相关的信誉值，基于该信誉值，设计激励机制激励拥有高质量数据的客户端加入训练。其次，基于拍卖理论设计拍卖机制，客户端通过向雾节点拍卖本地训练任务，委托高性能雾节点训练本地数据从而提升本地训练效率，解决客户端间的性能不均衡问题。最后，设计全局梯度聚合策略，增加高精度局部梯度在全局梯度中的权重，剔除恶意客户端，从而减少模型训练次数。

Abstract

Federated learning optimization algorithm based on incentive mechanism was proposed to address the issues of multiple iterations

long training time and low efficiency in the training process of federated learning.Firstly

the reputation value related to time and model loss was designed.Based on the reputation value

an incentive mechanism was designed to encourage clients with high-quality data to join the training.Secondly

the auction mechanism was designed based on the auction theory.By auctioning local training tasks to the fog node

the client entrusted the high-performance fog node to train local data

so as to improve the efficiency of local training and solve the problem of performance imbalance between clients.Finally

the global gradient aggregation strategy was designed to increase the weight of high-precision local gradient in the global gradient and eliminate malicious clients

so as to reduce the number of model training.

关键词

Keywords

references

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