Edge Computing Based on Federated Learning for Machine Monitoring

This paper focused on providing a general solution based on edge computing and cloud computing in IoT to machine monitoring in manufacturing of small and medium-sized factory. For real-time consideration, edge computing and cloud computing models were seamlessly cooperated to perform information cap...

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Bibliographic Details
Published in:Applied sciences 2022-05, Vol.12 (10), p.5178
Main Authors: Tsai, Yao-Hong, Chang, Dong-Meau, Hsu, Tse-Chuan
Format: Article
Language:English
Subjects:
Artificial intelligence
Automation
Cloud computing
Communication
computer vision
Cooperation
Deep learning
edge computing
Efficiency
Factories
federated learning
Fog
Internet of Things
Manufacturing
Monitoring
Neural networks
Power
Real time
Sensors
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Summary:This paper focused on providing a general solution based on edge computing and cloud computing in IoT to machine monitoring in manufacturing of small and medium-sized factory. For real-time consideration, edge computing and cloud computing models were seamlessly cooperated to perform information capture, event detection, and adaptive learning. The proposed IoT system processed regional low-level features for detection and recognition in edge nodes. Cloud-computing including fog computing was responsible for mid- and high-level features by using the federated learning network. The system fully utilized all resources in the integrated deep learning network to achieve high performance operations. The edge node was implemented by a simple camera embedded on Terasic DE2-115 board to monitor machines and process data locally. Learning-based features were generated by cloud computing through the data sent from edge and the identification results could be obtained by combining mid- and high-level features with the nonlinear classifier. Therefore, each factory could monitor the real-time condition of machines without operators and keep its data privacy. Experimental results showed the efficiency of the proposed method when compared with other methods.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12105178