面向联合学习的D2D计算任务卸载

蔡晓然; 莫小鹏; 许杰

doi:10.11959/j.issn.2096-3750.2019.00135

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面向联合学习的D2D计算任务卸载

理论与技术 | 更新时间：2024-06-05

- 面向联合学习的D2D计算任务卸载
- D2D computation task offloading for efficient federated learning
- 物联网学报 2019年3卷第4期页码：82-90
- 作者机构：
- 作者简介：
  
  [ "蔡晓然（1996- ），女，广东阳江人，广东工业大学硕士生，主要研究方向为无线通信、机器学习和移动边缘计算" ]
  [ "莫小鹏（1996- ），男，广东湛江人，广东工业大学硕士生，主要研究方向为无线通信、机器学习、无人机通信和移动边缘计算" ]
  [ "许杰（1985- ），男，四川内江人，广东工业大学教授、博士生导师，主要研究方向为无线通信、机器学习、无线能量传输、无人机通信以及移动边缘计算" ]
- 基金信息：
  
  国家重点研发计划资助项目;The National Key R＆D Program Subsidized Projects(2018YFB1800800);广东省重点领域研发计划资助项目;Research and Development Program Subsidized Projects in Key Areas of Guangdong Province(2018B030338001)
- DOI：10.11959/j.issn.2096-3750.2019.00135
  中图分类号： TN929
- 纸质出版日期：2019-12-30，
  
  网络出版日期：2019-09，
- 稿件说明：
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蔡晓然, 莫小鹏, 许杰. 面向联合学习的D2D计算任务卸载[J]. 物联网学报, 2019,3(4):82-90.

XIAORAN CAI, XIAOPENG MO, JIE XU. D2D computation task offloading for efficient federated learning. [J]. Chinese journal on internet of things, 2019, 3(4): 82-90.
蔡晓然, 莫小鹏, 许杰. 面向联合学习的D2D计算任务卸载[J]. 物联网学报, 2019,3(4):82-90. DOI： 10.11959/j.issn.2096-3750.2019.00135.

XIAORAN CAI, XIAOPENG MO, JIE XU. D2D computation task offloading for efficient federated learning. [J]. Chinese journal on internet of things, 2019, 3(4): 82-90. DOI： 10.11959/j.issn.2096-3750.2019.00135.

摘要

联合学习是一种分布式机器学习，边缘节点的计算和通信资源受限等因素是限制其性能优化的瓶颈。当边缘节点的计算和通信能力异构时，需要对通信和计算进行联合优化。提出了一种面向联合学习的D2D计算任务卸载方案，不同边缘节点通过D2D通信交换数据样本，平衡节点的处理能力和任务负载，以最小化联合学习模型训练过程的总时延。仿真结果表明，所提出的D2D计算任务卸载方案能显著提高联合学习的模型训练速度和效率。

Abstract

Federated learning is a kind of distributed machine learning technique.The factor of communication and computation resource constraints at the edge node is becoming the performance bottleneck.In particular

when different edge node has distinct computation and communication capabilities

the model training performance may degrade severely

thus necessitating the joint communication and computation optimization.To tackle this challenge

a computational task offloading scheme enabled by device-to-device (D2D) communications was proposed

in which different edge node exchanged data samples via D2D communication links to balance the processing capability and task load

in order to minimize the total time delay for machine learning model training.Simulation results show that compared to the benchmark scheme without such D2D task offloading the training speed and efficiency of federated learning has be improved significantly.

关键词

联合学习移动边缘计算任务卸载D2D通信

Keywords

federated learningmobile edge computingtask offloadingdevice-to-device communication

references

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