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.
@article{arxiv.2005.06850,
title = {Industrial Federated Learning -- Requirements and System Design},
author = {Thomas Hiessl and Daniel Schall and Jana Kemnitz and Stefan Schulte},
journal= {arXiv preprint arXiv:2005.06850},
year = {2020}
}
Comments
12 pages, accepted for https://www.paams.net/workshops/agedai