Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications. (arXiv:2304.11101v1 [cs.LG])

Data-driven machine learning is playing a crucial role in the advancements of
Industry 4.0, specifically in enhancing predictive maintenance and quality
inspection. Federated learning (FL) enables multiple participants to develop a
machine learning model without compromising the privacy and confidentiality of
their data. In this paper, we evaluate the performance of different FL
aggregation methods and compare them to central and local training approaches.
Our study is based on four datasets with varying data distributions. The
results indicate that the …

aggregation applications arxiv data data-driven datasets federated learning industrial industry industry 4.0 machine machine learning machine learning model maintenance multiple paper performance playing predictive predictive maintenance privacy quality role study training