# LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and Communication-Efficient Distributed Learning

@article{Zhang2021LAGCLA,
title={LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and Communication-Efficient Distributed Learning},
author={Jingjing Zhang and Osvaldo Simeone},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2021},
volume={32},
pages={962-974}
}
• Published 22 May 2019
• Computer Science
• IEEE Transactions on Neural Networks and Learning Systems
Gradient-based distributed learning in parameter server (PS) computing architectures is subject to random delays due to straggling worker nodes and to possible communication bottlenecks between PS and workers. Solutions have been recently proposed to separately address these impairments based on the ideas of gradient coding (GC), worker grouping, and adaptive worker selection. This article provides a unified analysis of these techniques in terms of wall-clock time, communication, and…
12 Citations

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