Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications

Viktorija Pruckovskaja, Axel Weißenfeld, Clemens Heistracher, Anita Graser, Julia Kafka, Peter Leputsch, Daniel Schall, Jana Kemnitz

Publikation: Beitrag in Buch oder TagungsbandVortrag mit Beitrag in TagungsbandBegutachtung


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 performance of FL is highly dependent on the data and its distribution among clients. In some scenarios, FL can be an effective alternative to traditional central or local training methods. Additionally, we introduce a new federated learning dataset from a real-world quality inspection setting.
Titel2023 Prognostics and Health Management Conference (PHM Paris 2023)
Redakteure/-innenChetan S. Kulkarni, Indranil Roychoudhury
PublikationsstatusVeröffentlicht - 2023
Veranstaltung2023 Prognostics and Health Management Conference (PHM) - Hilton Salt Lake City, Salt Lake City, Utah, USA/Vereinigte Staaten
Dauer: 28 Okt. 20232 Nov. 2023


Konferenz2023 Prognostics and Health Management Conference (PHM)
Land/GebietUSA/Vereinigte Staaten
StadtSalt Lake City, Utah

Research Field

  • Ehemaliges Research Field - Data Science


Untersuchen Sie die Forschungsthemen von „Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications“. Zusammen bilden sie einen einzigartigen Fingerprint.

Diese Publikation zitieren