Communication-Efficient Federated Bayesian Learning via Client Selection-Reference-Cited by-同舟云学术

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Communication-Efficient Federated Bayesian Learning via Client Selection

Published:2022-12-04 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Globecom Workshops (GC Wkshps)
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Author:

Yang Jiarong¹,Liu Yuan¹,Kassab Rahif²

Affiliation:

1. South China University of Technology,Electronic and Information Engineering,Guangzhou,China

2. King’s College London,Department of Engineering,UK

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10008390/10008474/10008503.pdf?arnumber=10008503

Reference27 articles.

1. Bayesian learning via stochastic gradient langevin dynamics;welling;Proc 28th Int Conf Mach Learn,2011

2. Embarrassingly parallel mcmc using deep invertible transformations;mesquita;Proceedings of Conferences on Uncertainty in Artificial Intelligence,2020

3. Variational federated multi-task learning;corinzia,2019

4. Patterns of Scalable Bayesian Inference

5. Distributed stochastic gradient mcmc;ahn;Proc Int Conf Mach Learn,2014

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