Federated Learning Scheme for Environmental Monitoring Based on Edge Computing

JIANG Wei-jin; HAN Yu-qing; WU Yu-ting; ZHOU Wei; CHEN Yi-lin; WANG Hai-juan

doi:10.12263/DZXB.20230504

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Federated Learning Scheme for Environmental Monitoring Based on Edge Computing

PAPERS | 更新时间：2025-12-08

- Federated Learning Scheme for Environmental Monitoring Based on Edge Computing
- ACTA ELECTRONICA SINICA Vol. 51, Issue 11, Pages: 3061-3069(2023)
- 作者机构：
  
  1.湖南工商大学计算机学院,湖南长沙 410205
  2.武汉理工大学计算机与人工智能学院,湖北武汉 430070
  3.湘江实验室,湖南长沙 410205
  4.湖南工商大学前沿交叉学院,湖南长沙 410205
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation Basic Science Center Project(61772196;72088101);Hunan Provincial Natural Science Foundation(2020JJ4249)
- DOI：10.12263/DZXB.20230504
  CLC： TP301
- Received：05 June 2023，
  
  Revised：2023-08-16，
  
  Published：25 November 2023
- 稿件说明：
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蒋伟进,韩裕清,吴玉庭等.基于边缘计算的环境监测自适应联邦学习算法[J].电子学报,2023,51(11):3061-3069.

JIANG Wei-jin,HAN Yu-qing,WU Yu-ting,et al.Federated Learning Scheme for Environmental Monitoring Based on Edge Computing[J].ACTA ELECTRONICA SINICA,2023,51(11):3061-3069.
蒋伟进,韩裕清,吴玉庭等.基于边缘计算的环境监测自适应联邦学习算法[J].电子学报,2023,51(11):3061-3069. DOI： 10.12263/DZXB.20230504.

JIANG Wei-jin,HAN Yu-qing,WU Yu-ting,et al.Federated Learning Scheme for Environmental Monitoring Based on Edge Computing[J].ACTA ELECTRONICA SINICA,2023,51(11):3061-3069. DOI： 10.12263/DZXB.20230504.

摘要

针对环境监测领域边缘设备资源不平衡、通信延迟以及模型质量不高的问题，本文提出一种基于边缘计算的环境监测自适应联邦学习算法.该算法旨在利用边缘设备进行数据处理，并根据各个设备的资源限制调整全局模型的聚合频率，以更好地适应不同的监测环境.通过考虑边缘设备之间的资源差异，算法采用了动态优化迭代频率的策略，以提升模型的训练效果.与传统的固定迭代频率相比，该算法的调整策略更加灵活，能够更好地适应不同的数据分布和参与方特征.通过大量实验评估，并利用与同类算法CNN-FL（Convolutional Neural Networks-Federated Learning），FedAvg（Federated Averaging）和HFEL（Hierarchical Federated Edge Learning）的比较，本文提出的算法在算法性能和经济成本方面具有显著优势，这种算法为环境监测提供了一种高效、安全和可扩展的数据分析和模型建立方法，有助于推动环境监测能力的提升.

Abstract

Aiming at the problems of unbalanced edge device resources

communication delay and low model quality in the field of environmental monitoring

this paper proposes an adaptive federated learning algorithm for environmental monitoring based on edge computing. This algorithm aims to use edge devices for data processing

and according to each the resource limitation of the device adjusts the aggregation frequency of the global model to better adapt to different monitoring environments. By considering the resource differences between edge devices

the algorithm adopts a strategy of dynamically optimizing the iteration frequency to improve the training effect of the model. Compared with the traditional fixed iteration frequency

the adjustment strategy of this algorithm is more flexible and can better adapt to different data distribution and participant characteristics. Through a large number of experimental evaluations

and using the same algorithm convolutional neural networks-federated learning (CNN-FL)

federated averaging (FedAvg) and hierarchical federated edge learning (HFEL)

the algorithm proposed in this paper has significant advantages in algorithm performance and economic cost. This algorithm provides an efficient

safe and reliable method for environmental monitoring. Expanded approach to data analysis and modeling to help drive improvements in environmental monitoring capabilities.

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references

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