Incentive Mechanism Based on Evolutionary Game and Interruption Probability Optimization for Semi-Distributed Federated Edge Learning

LIU Yu-xin; WANG Yi-hang; WANG Si-yang; XIA Wen-chao; ZHAO Hai-tao; BU Xian-de

doi:10.12263/DZXB.20250658

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Incentive Mechanism Based on Evolutionary Game and Interruption Probability Optimization for Semi-Distributed Federated Edge Learning

PAPERS | 更新时间：2026-02-10

- Incentive Mechanism Based on Evolutionary Game and Interruption Probability Optimization for Semi-Distributed Federated Edge Learning
- ACTA ELECTRONICA SINICA Vol. 53, Issue 11, Pages: 3956-3969(2025)
- 作者机构：
  
  1.南京邮电大学教育部泛在网络健康服务系统工程研究中心，江苏南京 210003
  2.网络通信与安全紫金山实验室，江苏南京 211111
  3.中国电力科学研究院有限公司信息通信研究所，江苏南京 210003
- 作者简介：
- 基金信息：
  
  Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu(BK20212001);Young Elite Scientists Sponsorship Program by CAST(2022QNRC001)
- DOI：10.12263/DZXB.20250658
  CLC： TP181;
- Received：28 July 2025，
  
  Accepted：10 November 2025，
  
  Published：25 November 2025
- 稿件说明：
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刘雨欣, 王一航, 王思洋, 等. 基于演化博弈与中断概率优化的半分布式联邦边缘学习激励机制[J]. 电子学报, 2025, 53(11): 3956-3969.

LIU Yu-xin, WANG Yi-hang, WANG Si-yang, et al. Incentive Mechanism Based on Evolutionary Game and Interruption Probability Optimization for Semi-Distributed Federated Edge Learning[J]. Acta Electronica Sinica, 2025, 53(11): 3956-3969.
刘雨欣, 王一航, 王思洋, 等. 基于演化博弈与中断概率优化的半分布式联邦边缘学习激励机制[J]. 电子学报, 2025, 53(11): 3956-3969. DOI：10.12263/DZXB.20250658

LIU Yu-xin, WANG Yi-hang, WANG Si-yang, et al. Incentive Mechanism Based on Evolutionary Game and Interruption Probability Optimization for Semi-Distributed Federated Edge Learning[J]. Acta Electronica Sinica, 2025, 53(11): 3956-3969. DOI：10.12263/DZXB.20250658

摘要

随着物联网快速发展，大规模泛在分布终端产生的数据规模激增.为提升网络智慧服务能力，采用半分布式联邦边缘学习（Semi-Decentralized FEderated Edge Learning，SD-FEEL）方式，通过多个边缘服务器各自协调一个终端簇执行本地更新，边缘服务器之间周期性共享模型更新，可以在保障学习性能的同时有效缓解网络拥塞.然而，在实际部署中，缺乏有效激励会导致终端参与学习的积极性不高，且训练过程中可能发生无线通信中断，这些因素都会降低模型训练效率.因此，本文提出一种面向SD-FEEL场景的基于演化博弈与中断概率优化的激励机制.具体来说，首先，设计同时考虑数据大小与质量的终端贡献评估指标，并据此构建奖励函数激励高质量终端参与训练，提升全局模型性能并确保激励公平性.其次，引入演化博弈框架，捕捉终端的有限理性和动态决策行为，均衡边缘服务器负载，确定种群中关联各边缘服务器的最优比例，实现种群总效益最大化.在此基础上，以最小化无线通信中断概率为目标，优化终端与边缘服务器的具体关联策略.仿真结果表明，所提方法能够有效均衡边缘服务负载，与随机接入方案和声誉激励机制（Reputation-Aware Incentive Mechanism，RAIM）方案相比，通信中断概率分别降低32.04%和35.55%，模型准确性分别提升3.58%和4.34%.

Abstract

With the rapid advancement of the Internet of Things

the volume of data generated by large-scale

ubiquitous distributed terminals is surging. To enhance the intelligence of network services

we adopt the semi-decentralized federated edge learning (SD-FEEL) paradigm

in which multiple edge servers each coordinate a cluster of terminals to perform local model updates

while periodically exchanging updates among themselves. This approach preserves learning performance while effectively mitigating network congestion. However

real-world deployments encounter key challenges: inadequate incentives reduce terminals’ motivation to participate in training

and wireless communication interruptions during the process can degrade overall training efficiency. To address these issues

this paper proposes an incentive mechanism for SD-FEEL scenarios

leveraging evolutionary game theory and optimization of interruption probabilities. Specifically

first

we design a terminal contribution metric that incorporates both data quantity and quality

along with a corresponding reward function to encourage participation from high-quality terminals. This not only boosts global model performance but also ensures fairness in incentives. Second

we introduce an evolutionary game framework to model terminals’ bounded rationality and dynamic decision-making behaviors. This framework balances edge server loads

determines the optimal proportions of terminals associating with each server within the population

and maximizes the population’s overall utility. Building on this foundation

we further optimize specific terminal-to-edge server association strategies with the goal of minimizing the probability of wireless communication interruptions. Simulation results demonstrate that the proposed method can effectively balance the edge service load. Compared to the random access method and the reputation-aware incentive mechanism(RAIM) scheme

the communication interruption probability is reduced by 32.04% and 35.55% respectively

and the model accuracy is improved by 3.58% and 4.34%

respectively.

关键词

Keywords

references

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