Approaches to Uncertainty Quantification in Federated Deep Learning

Florian Linsner, Linara Adilova, Sina Däubener, Michael Kamp, Asja Fischer: Approaches to Uncertainty Quantification in Federated Deep Learning. Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2021, vol. 2, Springer, 2021.

Abstract

BibTeX (Download)

@workshop{linsner2021uncertainty,
title = {Approaches to Uncertainty Quantification in Federated Deep Learning},
author = {Florian Linsner and Linara Adilova and Sina Däubener and Michael Kamp and Asja Fischer},
url = {https://michaelkamp.org/wp-content/uploads/2022/04/federatedUncertainty.pdf},
year  = {2021},
date = {2021-09-17},
urldate = {2021-09-17},
booktitle = {Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2021},
issuetitle = {Workshop on Parallel, Distributed, and Federated Learning},
volume = {2},
pages = {128-145},
publisher = {Springer},
keywords = {federated learning, uncertainty},
pubstate = {published},
tppubtype = {workshop}
}

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