@inproceedings{34874a9c3d87481382715cc4552f2709,
title = "Kimad: Adaptive Gradient Compression with Bandwidth Awareness",
abstract = "In distributed training, communication often emerges as a bottleneck. In response, we introduce Kimad, a solution that offers adaptive gradient compression. By consistently monitoring bandwidth, Kimad refines compression ratios to match specific neural network layer requirements. Our exhaustive tests and proofs confirm Kimad's outstanding performance, establishing it as a benchmark in adaptive compression for distributed deep learning.",
keywords = "distributed training, gradient compression",
author = "Jihao Xin and Ivan Ilin and Shunkang Zhang and Marco Canini and Peter Richt{\'a}rik",
note = "Publisher Copyright: {\textcopyright} 2023 Owner/Author.; 4th International Workshop on Distributed Machine Learning, DistributedML 2023 ; Conference date: 08-12-2023",
year = "2023",
month = dec,
day = "8",
doi = "10.1145/3630048.3630184",
language = "English (US)",
series = "DistributedML 2023 - Proceedings of the 4th International Workshop on Distributed Machine Learning",
publisher = "Association for Computing Machinery, Inc",
pages = "35--48",
booktitle = "DistributedML 2023 - Proceedings of the 4th International Workshop on Distributed Machine Learning",
}