ICML 2018oral441 citations
$D^2$: Decentralized Training over Decentralized Data
Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, Ji Liu
Abstract
While training a machine learning model using multiple workers, each of which collects data from its own data source, it would be useful when the data collected from different workers are
BibTeX
@InProceedings{pmlr-v80-tang18a,
title = {$D^2$: Decentralized Training over Decentralized Data},
author = {Tang, Hanlin and Lian, Xiangru and Yan, Ming and Zhang, Ce and Liu, Ji},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {4848--4856},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
month = {10--15 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v80/tang18a/tang18a.pdf},
url = {https://proceedings.mlr.press/v80/tang18a.html},
abstract = {While training a machine learning model using multiple workers, each of which collects data from its own data source, it would be useful when the data collected from different workers are