面向能量受限工业物联网设备的联邦学习资源管理

范绍帅; 吴剑波; 田辉

doi:10.11959/j.issn.1000-436x.2022126

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面向能量受限工业物联网设备的联邦学习资源管理

学术论文 | 更新时间：2024-06-05

- 面向能量受限工业物联网设备的联邦学习资源管理
- Federated learning resource management for energy-constrained industrial IoT devices
- 通信学报 2022年43卷第8期页码：65-77
- 作者机构：
- 作者简介：
  
  [ "范绍帅（1987- ），男，山东烟台人，博士，北京邮电大学讲师，主要研究方向为5G及后5G组网及关键技术、智能信息处理及网络自组织技术、高精度定位技术" ]
  [ "吴剑波（1999- ），女，辽宁大连人，北京邮电大学硕士生，主要研究方向为联邦学习" ]
  [ "田辉（1963- ），女，河南郑州人，博士，北京邮电大学教授、博士生导师，主要研究方向为先进移动通信系统及无线网络技术" ]
- 基金信息：
  
  国家重点研发计划基金资助项目;The National Key Research and Development Program of China(2020YFB1807800)
- DOI：10.11959/j.issn.1000-436x.2022126
  中图分类号： TP302
- 网络出版日期：2022-08，
  
  纸质出版日期：2022-08-25
- 稿件说明：
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范绍帅, 吴剑波, 田辉. 面向能量受限工业物联网设备的联邦学习资源管理[J]. 通信学报, 2022,43(8):65-77.

Shaoshuai FAN, Jianbo WU, Hui TIAN. Federated learning resource management for energy-constrained industrial IoT devices[J]. Journal on communications, 2022, 43(8): 65-77.
范绍帅, 吴剑波, 田辉. 面向能量受限工业物联网设备的联邦学习资源管理[J]. 通信学报, 2022,43(8):65-77. DOI： 10.11959/j.issn.1000-436x.2022126.

Shaoshuai FAN, Jianbo WU, Hui TIAN. Federated learning resource management for energy-constrained industrial IoT devices[J]. Journal on communications, 2022, 43(8): 65-77. DOI： 10.11959/j.issn.1000-436x.2022126.

摘要

针对工业物联网联邦学习网络中由设备电池能量有限导致的设备失效、训练中断等问题，并考虑到无线资源受限的影响，提出一种动态的多维资源联合管理算法。首先，以最大化固定训练时间学习精度为目标，将优化问题解耦为相互依赖的电池能量分配子问题、设备资源分配子问题和通信资源分配子问题。其次，基于粒子群优化算法求解能耗预算下设备传输和计算资源分配策略。再次，提出资源块迭代匹配算法求解出最佳通信资源分配策略。最后，提出在线能量分配算法动态调整设备能量分配策略。仿真结果表明，与基准算法相比，所提算法能够提高模型学习精度，在能源不足场景下性能优势更明显。

Abstract

Given the impact of limited wireless resources

a dynamic multi-dimensional resource joint management algorithm was proposed

which intended to tackle the problem of device failure and training interruption caused by the limited battery energy in federated learning network in industrial Internet of things (IIoT).Firstly

the optimization problem was decoupled into battery energy allocation

equipment resource allocation and communication resource allocation sub-problems which were interdependent with the goal of maximizing the fixed-time learning accuracy.Then

the equipment transmission and computing resource allocation problem were solved based on particle swarm optimization algorithm under the given energy budget.Thereafter

the resource block iterative matching algorithm was proposed to optimize the optimal communication resource allocation strategy.Finally

the online energy allocation algorithm was proposed to adjust the energy budget allocation.Simulation results validate the proposed algorithm can improve the model learning accuracy compared with other benchmarks

and can perform better in energy shortage scenarios.

关键词

Keywords

references

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