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2020 | OriginalPaper | Buchkapitel

Industrial Federated Learning – Requirements and System Design

verfasst von : Thomas Hiessl, Daniel Schall, Jana Kemnitz, Stefan Schulte

Erschienen in: Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness. The PAAMS Collection

Verlag: Springer International Publishing

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Abstract

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.
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Metadaten
Titel
Industrial Federated Learning – Requirements and System Design
verfasst von
Thomas Hiessl
Daniel Schall
Jana Kemnitz
Stefan Schulte
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-51999-5_4

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