crossrefMachines2025-09-01Cited by 1
Towards Privacy-Preserving Machine Learning for Energy Prediction in Industrial Robotics: Modeling, Evaluation and Integration
Adam Skuta, Philipp Steurer, Sebastian Hegenbart, Ralph Hoch, Thomas Loruenser
This paper explores the feasibility and implications of developing a privacy-preserving, data-driven cloud service for predicting the energy consumption of industrial robots. Using machine learning, we evaluated three neural network architectures—dense, LSTM, and convolutional–LS…