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.…
| Document Type: | Conference Proceeding |
|---|---|
| Conference Type: | Konferenzartikel |
| Zitierlink: | https://opus.hs-offenburg.de/5366 | Bibliografische Angaben |
| Title (English): | Deep Learning in Resource and Data Constrained Edge Computing Systems |
| Conference: | 5. Fachkonferenz ML4CPS : Maschinelles Lernen in der Produktion, 12. bis 13. März 2020, Berlin |
| Author: | Pranav Sharma, Marcus Rüb, Daniel Gaida, Heiko Lutz, Axel SikoraStaff MemberORCiDGND |
| Date of Publication (online): | 2020/12/24 |
| Publisher: | Springer |
| First Page: | 43 |
| Last Page: | 51 |
| Parent Title (English): | Machine Learning for Cyber Physical Systems: Selected papers from the International Conference ML4CPS 2020 |
| Editor: | Jürgen Beyerer, Alexander Maier, Oliver Niggemann |
| Volume: | TIA 13 |
| ISBN: | 978-3-662-62745-7 (Print) |
| ISBN: | 978-3-662-62746-4 (Online) |
| ISSN: | 2522-8579 (Print) |
| ISSN: | 2522-8587 (Online) |
| DOI: | https://doi.org/10.1007/978-3-662-62746-4_5 |
| Language: | English | Inhaltliche Informationen |
| Institutes: | Forschung / ivESK - Institut für verlässliche Embedded Systems und Kommunikationselektronik |
| Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) | |
| Institutes: | Bibliografie |
| DDC classes: | 600 Technik, Medizin, angewandte Wissenschaften |
| Tag: | Federated Learning; Predictive Maintenance; Unsupervised Learning; Variational Autoencoders | Formale Angaben |
| Open Access: | Open Access |
| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |