Federated learning for network anomaly detection and visual quality control in shared production

Publication date
2026-05-07
Document type
Konferenzbeitrag
Author
Hittmeyer, Stefanie
Specht, Felix
Bergweiler, Simon
Rezapour, Mahdi
Organisational unit
Fraunhofer IOSB-INA
German Research Center for Artificial Intelligence (DFKI)
DOI
URI
Conference
Publisher
Universitätsbibliothek der HSU/UniBw H
Book title
Machine learning for cyber physical systems : proceedings of the conference ML4CPS 2026
First page
84
Last page
90
Is part of
Peer-reviewed
✅
Part of the university bibliography
Nein
File(s)
Language
English
Keyword
federated learning
shared production
OT networks
visual inspection
non-IID data
edge-cloud continuum
Abstract
Federated Learning (FL) addresses data sovereignty and heterogeneity in industrial cyber-physical systems by training models across sites without centralizing raw data. We report on two real-world use cases: anomaly detection in operational technology (OT) network communication, and visual inspection for quality control in a shared production scenario across several smart factories at different locations. Both settings feature non-IID data distributions, domain shift, strict privacy/IP constraints, and edge-cloud resource limitations. We benchmark local, centralized, and federated training with domain-specific architectures. Results indicate that FL improves precision-recall behavior for rare events in OT networks at identical ROC-AUC compared to baselines and approaches model quality of the globally learned model in visual inspection with appropriate round-epoch scheduling, while local models overfit site-specific data. We discuss design guidelines, benefits for shared production, and how FL could contribute to scalability and data and energy efficiency in a dynamic edge–cloud continuum.
Description
This contribution is part of the conference proceedings, which are licensed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/)
Version
Published version
Access right on openHSU
Open access