一种基于带宽分配的联邦学习激励机制

郭英芸; 高博; 张志飞; 张煜; 熊轲

doi:10.11959/j.issn.2096-3750.2022.00300

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一种基于带宽分配的联邦学习激励机制

理论与技术 | 更新时间：2024-08-16

- 一种基于带宽分配的联邦学习激励机制
- An incentive mechanism with bandwidth allocation for federated learning
- 物联网学报 2022年6卷第4期页码：82-92
- 作者机构：
  
  1. 北京交通大学计算机与信息技术学院，北京 100044
  2. 北京交通大学高速铁路网络管理教育部工程研究中心，北京 100044
  3. 国网能源研究院有限公司，北京 102209
- 作者简介：
  
  [ "郭英芸（1995- ），女，北京交通大学硕士生，主要研究方向为移动与互联网络" ]
  [ "高博（1984- ），男，北京交通大学副教授，主要研究方向为无线网络、移动计算、机器学习" ]
  [ "张志飞（1971- ），男，博士，北京交通大学计算机与信息技术学院高级工程师，博士，主要研究方向为网络通信理论、网络安全等" ]
  [ "张煜（1983- ），男，博士，国网能源研究院有限公司高级工程师，主要研究方向为泛在电力物联网、无线协作网络、电能替代等" ]
  [ "熊轲（1981-），男，博士，北京交通大学计算机与信息技术学院教授、副院长，主要研究方向为无线协作网络、无线移动网络和网络信息理论等" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61872028);中央高校基本科研业务费资助项目;The Fundamental Research Funds for the Central Universities(2021JBM008);中央高校基本科研业务费资助项目;The Fundamental Research Funds for the Central Universities(2022JBXT001)
- DOI：10.11959/j.issn.2096-3750.2022.00300
  中图分类号： TP393
- 纸质出版日期：2022-12-30，
  
  网络出版日期：2022-12，
- 稿件说明：
移动端阅览
郭英芸, 高博, 张志飞, 等. 一种基于带宽分配的联邦学习激励机制[J]. 物联网学报, 2022,6(4):82-92.

YINGYUN GUO, BO GAO, ZHIFEI ZHANG, et al. An incentive mechanism with bandwidth allocation for federated learning. [J]. Chinese journal on internet of things, 2022, 6(4): 82-92.
郭英芸, 高博, 张志飞, 等. 一种基于带宽分配的联邦学习激励机制[J]. 物联网学报, 2022,6(4):82-92. DOI： 10.11959/j.issn.2096-3750.2022.00300.

YINGYUN GUO, BO GAO, ZHIFEI ZHANG, et al. An incentive mechanism with bandwidth allocation for federated learning. [J]. Chinese journal on internet of things, 2022, 6(4): 82-92. DOI： 10.11959/j.issn.2096-3750.2022.00300.

摘要

联邦学习（FL

federated learning）是一种新兴的机器学习范式，它可以充分利用移动众包资源进行去中心化数据训练。然而，在无线网络中部署 FL 面临网络带宽有限、移动用户自私等挑战。为了应对这些挑战，提出了一种基于带宽分配的激励机制（IMBA

incentive mechanism with bandwidth allocation）。IMBA考虑用户数据质量和计算能力的不同设计支付方案，以激励高数据质量用户贡献其计算资源，进而提升模型训练精度。通过最小化训练时间和支付成本权重和确定最佳支付与带宽分配方案，通过优化带宽分配降低训练时延。实验表明， IMBA能够有效提高训练精度，降低训练时间，并帮助服务器灵活权衡训练时间和支付报酬。

Abstract

Federated learning (FL) is an emerging machine learning paradigm that can make full use of crowd sourced mobile resources for training on decentralized data.However

it is challenging to deploy FL over a wireless network because of the limited bandwidth and clients’ selfishness.To address these challenges

an incentive mechanism with bandwidth allocation (IMBA) was proposed.Considering the difference between clients' data quality and computing power

IMBA designs a payment scheme to incentivize high-quality clients to contribute their computing resources

thus improving the training accuracy of the model.By minimizing the weight sum of training time and payment cost

the optimal payment and bandwidth allocation scheme was determined

and the training delay was reduced by optimizing bandwidth allocation.Experiments show that IMBA effectively improves training accuracy

reduces the training delay and helps the server flexibly balance training delay and hiring payment.

关键词

联邦学习激励机制带宽分配Stackelberg博弈训练质量

Keywords

federated learningincentive mechanismbandwidth allocationStackelberg gametraining quality

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