Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization

10 June 2020

Papers citing "Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization"

11 / 11 papers shown

Title
OmniLearn: A Framework for Distributed Deep Learning over Heterogeneous Clusters S. Tyagi Prateek Sharma 73 0 0 21 Mar 2025
SignSGD with Federated Voting Chanho Park H. Vincent Poor Namyoon Lee FedML 42 1 0 25 Mar 2024
Taming Resource Heterogeneity In Distributed ML Training With Dynamic Batching S. Tyagi Prateek Sharma 29 22 0 20 May 2023
STSyn: Speeding Up Local SGD with Straggler-Tolerant Synchronization Feng Zhu Jingjing Zhang Xin Eric Wang 33 3 0 06 Oct 2022
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent Pedro Soto Ilia Ilmer Haibin Guan Jun Li 38 3 0 31 Jan 2022
Trade-offs of Local SGD at Scale: An Empirical Study Jose Javier Gonzalez Ortiz Jonathan Frankle Michael G. Rabbat Ari S. Morcos Nicolas Ballas FedML 43 19 0 15 Oct 2021
Decentralized optimization with non-identical sampling in presence of stragglers Tharindu B. Adikari S. Draper 45 1 0 25 Aug 2021
Anytime Minibatch with Delayed Gradients H. Al-Lawati S. Draper 33 0 0 15 Dec 2020
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum Jianyu Wang Vinayak Tantia Nicolas Ballas Michael G. Rabbat 25 200 0 01 Oct 2019
Robust and Communication-Efficient Collaborative Learning Amirhossein Reisizadeh Hossein Taheri Aryan Mokhtari Hamed Hassani Ramtin Pedarsani 38 89 0 24 Jul 2019
Optimal Distributed Online Prediction using Mini-Batches O. Dekel Ran Gilad-Bachrach Ohad Shamir Lin Xiao 186 683 0 07 Dec 2010