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Deep Learning in Resource and Data Constrained Edge Computing Systems

  • To demonstrate how deep learning can be applied to industrial applications with limited training data, deep learning methodologies are used in three different applications. In this paper, we perform unsupervised deep learning utilizing variational autoencoders and demonstrate that federated learning is a communication efficient concept for machine learning that protects data privacy. As anTo demonstrate how deep learning can be applied to industrial applications with limited training data, deep learning methodologies are used in three different applications. In this paper, we perform unsupervised deep learning utilizing variational autoencoders and demonstrate that federated learning is a communication efficient concept for machine learning that protects data privacy. As an example, variational autoencoders are utilized to cluster and visualize data from a microelectromechanical systems foundry. Federated learning is used in a predictive maintenance scenario using the C-MAPSS dataset.show moreshow less

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Metadaten
Author:Pranav Sharma, Marcus Rüb, Daniel Gaida, Heiko Lutz, Axel SikoraORCiDGND
Editor:Jürgen Beyerer, Alexander Maier, Oliver Niggemann
Publisher:Springer
Date of Publication (online):2020/12/24
ISBN:978-3-662-62745-7 (Print)
ISBN:978-3-662-62746-4 (Online)
Language:English
Tag:Federated Learning; Predictive Maintenance; Unsupervised Learning; Variational Autoencoders
DDC classes:600 Technik, Medizin, angewandte Wissenschaften
Parent Title (English):Machine Learning for Cyber Physical Systems: Selected papers from the International Conference ML4CPS 2020
Volume:TIA 13
ISSN:2522-8579 (Print)
ISSN:2522-8587 (Online)
First Page:43
Last Page:51
Document Type:Conference Proceeding
Open Access:Frei zugänglich
Institutes:Bibliografie
Release Date:2022/01/14
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
Note:
5. Fachkonferenz ML4CPS – Maschinelles Lernen in der Produktion vom 12. bis 13. März 2020 Berlin
DOI:https://doi.org/10.1007/978-3-662-62746-4_5