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T. Hiessl, D. Schall, J. Kemnitz, S. Schulte:
"Industrial Federated Learning - Requirements and System Design";
Vortrag: Workshop on Agents and Edge-AI (AgEdAI 2020) at PAAMS 2020 - Online Conference, L'Aquila, Italy; 07.10.2020 - 09.10.2020; in: "Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness. The PAAMS Collection 2020", F. De La Prieta, P. Mathieu, J. Rincon Arango, A. El Bolock, E. Del Val, J. Prunera, J. Carneiro, R. Fuentes, F. Lopes, V. Julian (Hrg.); Springer, Cham, CCIS 1233 (2020), ISBN: 978-3-030-51998-8; S. 42 - 53.

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.

Federated Learning · Industrial AI · Edge computing

http://dx.doi.org/10.1007/978-3-030-51999-5_4