Hierarchical Stackelberg Game Swarm Learning Incentive Method for Wireless Edge Network

KANG Hai-yan; JI Shan-shan

doi:10.12263/DZXB.20231158

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Hierarchical Stackelberg Game Swarm Learning Incentive Method for Wireless Edge Network

PAPERS | 更新时间：2025-12-24

- Hierarchical Stackelberg Game Swarm Learning Incentive Method for Wireless Edge Network
- ACTA ELECTRONICA SINICA Vol. 52, Issue 7, Pages: 2382-2392(2024)
- 作者机构：
  
  北京信息科技大学信息管理学院，北京 100192
- 作者简介：
- 基金信息：
  
  Humanities and Social Sciences of the Ministry of Education(20YJAZH046)
- DOI：10.12263/DZXB.20231158
  CLC： TP391.9;TN929.52
- Received：12 December 2023，
  
  Revised：2024-04-08，
  
  Published：25 July 2024
- 稿件说明：
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康海燕, 冀珊珊. 面向无线边缘网络的分层Stackelberg博弈群体激励方法[J]. 电子学报, 2024, 52(07): 2382-2392.

KANG Hai-yan, JI Shan-shan. Hierarchical Stackelberg Game Swarm Learning Incentive Method for Wireless Edge Network[J]. Acta Electronica Sinica, 2024, 52(07): 2382-2392.
康海燕, 冀珊珊. 面向无线边缘网络的分层Stackelberg博弈群体激励方法[J]. 电子学报, 2024, 52(07): 2382-2392. DOI：10.12263/DZXB.20231158

KANG Hai-yan, JI Shan-shan. Hierarchical Stackelberg Game Swarm Learning Incentive Method for Wireless Edge Network[J]. Acta Electronica Sinica, 2024, 52(07): 2382-2392. DOI：10.12263/DZXB.20231158

摘要

现有分布式机器学习模型的相关激励机制大多基于单层服务器架构，难以适应当前异构无线计算场景，同时存在计算资源分配不平衡、通信成本高昂等问题.针对上述问题，创新地提出一种面向无线边缘网络的分层Stackelberg博弈群体激励方法（Hierarchical Stackelberg game Swarm Learning Incentive method for wireless edge network，HSISL），创新地将Stackelberg博弈机制引入群体学习模型中，依据各参与方性能差异，云端聚合平台、边缘簇节点、边缘计算节点三方进行动态博弈，通过双定价公平激励过程，共同制定个性化分层资源分配策略，得到模型训练的最优纳什解，有效引导边缘计算模型进行正向加速.通过理论与实验分析，HSISL能够有效提升模型公平性与训练效率，其在MNIST数据集上的准确率可达96.06%.

Abstract

In today’s rapidly evolving landscape of distributed machine learning

conventional data incentive solutions often fall short due to their reliance on simplistic single-server architectures

in addition

as computing environments become increasingly complex

particularly within the context of heterogeneous wireless networks

these traditional approaches struggle to meet the dynamic computational demands such as unbalanced resource allocation and exorbitant communication costs. In response to the above dilemma

this paper innovatively proposes a hierarchical Stackelberg game swarm learning incentive method for wireless edge network (HSISL). This paper innovatively introduces the Stackelberg game mechanism into the swarm learning. Based on the performance differences of each computing terminal

the cloud aggregation platform

edge cluster nodes

and edge computing nodes conduct dynamic games and jointly formulate personalized hierarchical resource allocation strategies through the fair incentive process of dual pricing

which can effectively guide the edge computing model to accelerate forward. Through theoretical and experimental analysis

the HSISL method can obtain the optimal incentive Nash equilibrium solution for model training. Compared with other incentive methods

the HSISL method can effectively improve the fairness of the model. With training efficiency

its accuracy on the MNIST data set can reach 96.06%.

关键词

Keywords

references

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