面向6G网络的联邦学习资源协作激励机制设计

蒋丽; 谢胜利; 张彦

doi:10.12052/gdutxb.210114

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面向6G网络的联邦学习资源协作激励机制设计

更新时间：2026-05-27

- 面向6G网络的联邦学习资源协作激励机制设计
- Incentivizing Resource Cooperation for Federated Learning in 6G Networks
- 广东工业大学学报 2021年38卷第6期页码：47-52, 83
- 作者机构：
  
  1. 广东工业大学自动化学院,广东,广州,510006
  2. 挪威奥斯陆大学信息学院,奥斯陆,0316
- 作者简介：
  
  蒋丽(1986–)，女，特聘副教授，主要研究方向为6G网络和网络内生安全等，E-mail：jiangli@gdut.edu.cn
  张彦(1977–)，男，教授，博士，欧洲科学院院士，主要研究方向为6G网络和绿色通信等，E-mail：yanzhang@ieee.org
- 基金信息：
  
  国家重点研发计划项目(2020YFB1807801);移动通信教育部工程研究中心开放研究项目(cqupt-mct-202003)
- DOI：10.12052/gdutxb.210114
  中图分类号： TN929.5
- 网络首发：2021-11-10，
  
  纸质出版：2021
- 稿件说明：
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蒋丽, 谢胜利, 张彦. 面向6G网络的联邦学习资源协作激励机制设计[J]. 广东工业大学学报, 2021, 38(6): 47-52, 83.

Jiang Li, Xie Sheng-li, Zhang Yan. Incentivizing Resource Cooperation for Federated Learning in 6G Networks[J]. 2021, 38(6): 47-52, 83.
蒋丽, 谢胜利, 张彦. 面向6G网络的联邦学习资源协作激励机制设计[J]. 广东工业大学学报, 2021, 38(6): 47-52, 83. DOI： 10.12052/gdutxb.210114.

Jiang Li, Xie Sheng-li, Zhang Yan. Incentivizing Resource Cooperation for Federated Learning in 6G Networks[J]. 2021, 38(6): 47-52, 83. DOI： 10.12052/gdutxb.210114.

摘要

第6代(6G)移动通信在满足人类智能通信需求的同时

也给用户数据的安全与隐私保护带来了极大挑战。为此

基于联邦学习的分布式机器学习架构应运而生。然而

在联邦的模型训练过程中

移动设备会产生大量计算和通信开销。自私的移动设备不愿意参与模型训练

这将降低联邦学习性能。本文基于迭代双边拍卖设计了一种有效的联邦学习资源协作激励机制

任务计算终端作为卖方

任务请求终端作为买方

本地接入点根据买卖双方的出价做出模型训练时延和相应定价决策

在买卖双方信息非对称情况下最大化联邦学习市场总效用。仿真实验表明

所提机制具有良好的收敛性

可显著提高联邦学习的准确率

同时降低训练损失。

Abstract

Recent advances in 6th Generation (6G) mobile networks can meet the needs of deeper intelligent communication. Meanwhile

it also brings great challenges to the security and privacy preservation of user data. Federated learning is emerging as a distributed learning method to preserve privacy by enabling users to train machine learning models locally and requiring the users to upload only model parameters instead of sending original data to the server. However

the users with mobile devices may be unwilling to participate in federated learning tasks due to considerable overhead for computation and communication

which degrades performance and hinders the application of federated learning. In this study

an iterative double auction is designed based on resource cooperation scheme to incentivize the mobile devices to contribute their resources in federated learning tasks

where model trainers act as sellers and task requesters as buyers. Access point determines optimal training time cost and payment according to the prices offered by the sellers and the buyers. The goal of our incentivizing resource cooperation scheme is to maximize the total utility of federated learning market under information asymmetry. Numerical results show that the proposed scheme can converge to the optimal solution

and also can significantly improve model accuracy and degrade model loss value.

关键词

Keywords

references

SUO S Q, WANG Y M. Discussion and analysis on the native intelligence for the future 6G network [J]. Mobile Communications, 2020, 44(6): 126-30.

SUN Y H, WANG Z Y, YUAN S, et al . The sixth-generation mobile communication network with endogenous intelligence architectures, use cases and challenges [J ] . Application of Electronic Technique, 2021, 47(3): 8-13.

ZHENG S Y, CUI M, ZHANG G C. Reinforcement learning-based online trajectory optimization for secure UAV communications [J]. Journal of Guangdong University of Technology, 2021, 38(4): 60-64.

ZHANG C L, FU Y L, LI H, et al . Research on security scenarios and security models for 6G networking [J ] . Chinese Journal of Network and Information Security, 2021, 7(1): 29-45.

CHEN M, YANG Z, SAAD W, et al . A joint learning and communications framework for federated learning over wireless networks [J ] . IEEE Transactions on Wireless Communications, 2021, 20(1): 269-283.

NI W, LIU Y, TIAN H. Intelligent reflecting surfaces enhanced federated learning[C]//IEEE Globecom Workshops. Taipei: IEEE, 2021: 7-11.

LU Y, HUANG X, ZHANG K, et al . Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles [J ] . IEEE Transactions on Vehicular Technology, 2020, 69(4): 4298-4311.

LE T, TRAN N, TUN Y, et al . An incentive mechanism for federated learning in wireless cellular network: an auction approach [J ] . IEEE Transactions on Wireless Communications, 2021, 20(8): 4874-4887.

TRAN N, BAO W, ZOMAYA A, et al . Federated learning over wireless networks: optimization model design and analysis [C ] //IEEE Infocom 2019-IEEE Conference on Computer Communications. Paris: IEEE, 2019: 1387-1395.

FRIEDMAN D, RUST J. The double auction market: institutions theories and evidence [M]. Boulder: Westview Press, 1993: 429.

BOYD S, VANDENBERGHE L. Convex optimization [M]. New York: Cambridge University Press, 2004: 727.

KRIZHEVSKY A. Learning multiple layers of features from tiny images [D]. Toronto: University of Toronto, 2009.

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