面向视频监控基于联邦学习的智能边缘计算技术

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

Abstract

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.

Key words: federated learning, deep learning, edge computing, lightweight neural network, object detection

CLC Number:

TP391

Yu ZHAO,Jie YANG,Miao LIU,Jinlong SUN,Guan GUI. Federated learning based intelligent edge computing technique for video surveillance[J]. Journal on Communications, 2020, 41(10): 109-115.

Figures/Tables 11

References 17

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比较项	YOLOv3	改进YOLOv3
网络大小/MB	171	37
BFLOP	65.73	15.65
mAP	81.5%	76.82%
推理速度/ms	56	19

比较项	企业服务器	NVIDIA TX2
CUDA核心/个	3 584	256
运行内存/GB	128	8
功耗/W	600	7.5
硬件成本/元	30 000	3 150

数据集	MobileNet-SSD	改进YOLOv3
数据集A	75.38%	81.31%
数据集B	77.44%	85.19%
数据集C	79.19%	87.35%

Federated learning based intelligent edge computing technique for video surveillance

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