Federated learning based intelligent edge computing technique for video surveillance

Yu ZHAO; Jie YANG; Miao LIU; Jinlong SUN; Guan GUI

doi:10.11959/j.issn.1000-436x.2020192

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Federated learning based intelligent edge computing technique for video surveillance

Papers | 更新时间：2024-06-05

- Federated learning based intelligent edge computing technique for video surveillance
- Journal on Communications Vol. 41, Issue 10, Pages: 109-115(2020)
- 作者机构：
  
  南京邮电大学通信与信息工程学院，江苏南京 210003
- 作者简介：
- 基金信息：
  
  The Major Project of the Ministry of Industry and Information Technology of China(TC190A3WZ-2);The National Natural Science Foundation of China(61901228)
- DOI：10.11959/j.issn.1000-436x.2020192
  CLC： TP391
- Online First：2020-10，
  
  Published：25 October 2020
- 稿件说明：
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Yu ZHAO, Jie YANG, Miao LIU, et al. Federated learning based intelligent edge computing technique for video surveillance[J]. Journal on Communications, 2020, 41(10): 109-115.
DOI：

Yu ZHAO, Jie YANG, Miao LIU, et al. Federated learning based intelligent edge computing technique for video surveillance[J]. Journal on Communications, 2020, 41(10): 109-115. DOI： 10.11959/j.issn.1000-436x.2020192.

摘要

随着全球数据量的激增，集中式云计算无法提供低时延、高效率的视频监控服务。基于此，提出分布式边缘计算模型，在边缘端直接处理视频数据，减少网络的传输压力，缓解中央云服务器的计算负担，降低视频监控系统的处理时延。结合联邦学习算法，采用轻量级神经网络，分场景训练模型，并将其部署于计算能力受限的边缘设备上。实验结果表明，对比通用神经网络模型，所提方法检测准确度提高18%，模型训练时间有效减少。

Abstract

With the explosion of global data

centralized cloud computing cannot provide low-latency

high-efficiency video surveillance services.A distributed edge computing model was proposed

which directly processed video data at the edge node to reduce the transmission pressure of the network

eased the computational burden of the central cloud server

and reduced the processing delay of the video surveillance system.Combined with the federated learning algorithm

a lightweight neural network was used

which trained in different scenarios and deployed on edge devices with limited computing power.Experimental results show that

compared with the general neural network model

the detection accuracy of the proposed method is improved by 18%

and the model training time is reduced.

关键词

Keywords

references

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