Deep Learning in Resource and Data Constrained Edge Computing Systems
Published in Machine Learning for Cyber Physical Systems, 2021
Recommended citation: P Sharma, M Rüb, D Gaida, H Lutz, A Sikora. (2021). "Deep Learning in Resource and Data Constrained Edge Computing Systems." Machine Learning for Cyber Physical Systems. https://library.oapen.org/bitstream/handle/20.500.12657/46102/2021_Book_MachineLearningForCyberPhysica.pdf?sequence=1#page=47
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
Recommended citation: P Sharma, M Rüb, D Gaida, H Lutz, A Sikora. (2009). "Deep Learning in Resource and Data Constrained Edge Computing Systems." Machine Learning for Cyber Physical Systems.