Paper Info
Title
A Distributed Video Analytics Architecture Based on Edge-Computing and Federated Learning
Abstract
The prevalence of high definition video cameras in our environments provides countless types of essential services. The current model of sending video streams to the cloud for processing is facing many challenges such as latency and privacy. Delegating workloads of the cloud to the nearer Edge computing nodes is becoming the trend, which offers very low latency video analytics services. However, it opens up new challenges such as the issue of coordination between edge nodes and the fragmentation of their object detection models. In this paper, we introduce a distributed video analytics architecture based on edge-computing and the newly emerging federated learning. Its design allows for real-time and distributed object detection, in addition to a privacy-preserving scheme for detection model updating. A use-case scenario of a road surveillance system is given to support the proposed architecture. Finally, Challenges and future work are pointed out.
Year
DOI
Venue
2019
10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00047
2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
Keywords
Field
DocType
Video Analytics,Edge computing,Federated Learning
Edge computing,Object detection,High-definition video,Distributed object,Computer science,Visual analytics,Latency (engineering),Analytics,Distributed computing,Cloud computing
Conference
ISBN
Citations 
PageRank 
978-1-7281-3025-5
0
0.34
References 
Authors
6
5
Name
Order
Citations
PageRank
Abdelkarim Ben Sada100.34
Mohammed Amine Bouras2172.72
Jianhua Ma31401148.82
Runhe Huang440756.46
Huansheng Ning584783.48