LSDDL: Layer-Wise Sparsification for Distributed Deep Learning-Reference-Cited by-同舟云学术

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LSDDL: Layer-Wise Sparsification for Distributed Deep Learning

Published:2021-11 Issue: Volume:26 Page:100272
ISSN:2214-5796
Container-title:Big Data Research
language:en
Short-container-title:Big Data Research

Author:

Hong Yuxi,Han Peng^ORCID

Publisher

Elsevier BV

Subject

Information Systems and Management,Computer Science Applications,Information Systems,Management Information Systems

Reference78 articles.

1. Integrating compression and execution in column-oriented database systems;Abadi,2006

2. TensorFlow: a system for large-scale machine learning;Abadi,2016

3. QSGD: communication-efficient SGD via gradient quantization and encoding;Alistarh,2017

4. The convergence of sparsified gradient methods;Alistarh,2018

5. SIGNSGD: compressed optimisation for non-convex problems;Bernstein,2018

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Near-Linear Scaling Data Parallel Training with Overlapping-Aware Gradient Compression;2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS);2023-12-17

2. Dynamic layer-wise sparsification for distributed deep learning;Future Generation Computer Systems;2023-10

3. A Layer Selection Optimizer for Communication-Efficient Decentralized Federated Deep Learning;IEEE Access;2023

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