Refine
Year of publication
- 2021 (1)
Document Type
Conference Type
- Konferenzartikel (1)
Language
- English (1)
Has Fulltext
- no (1) (remove)
Is part of the Bibliography
- yes (1) (remove)
Keywords
Institute
Open Access
- Open Access (1)
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 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.