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Industrial federated learning – requirements and system design
Publikationstyp
Conference Paper
Date Issued
2020-01
Sprache
English
First published in
Number in series
1233
Start Page
42
End Page
53
Citation
18th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2020)
Contribution to Conference
Publisher DOI
Scopus ID
Federated Learning (FL) is a very promising approach for improving decentralized Machine Learning (ML) models by exchanging knowledge between participating clients without revealing private data. Nevertheless, FL is still not tailored to the industrial context as strong data similarity is assumed for all FL tasks. This is rarely the case in industrial machine data with variations in machine type, operational- and environmental conditions. Therefore, we introduce an Industrial Federated Learning (IFL) system supporting knowledge exchange in continuously evaluated and updated FL cohorts of learning tasks with sufficient data similarity. This enables optimal collaboration of business partners in common ML problems, prevents negative knowledge transfer, and ensures resource optimization of involved edge devices.
Subjects
Edge computing
Federated Learning
Industrial AI
MLE@TUHH
DDC Class
600: Technik