Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization

10 June 2020

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

8 / 8 papers shown

Title
SignSGD with Federated Voting Chanho Park H. Vincent Poor Namyoon Lee FedML 40 1 0 25 Mar 2024
Taming Resource Heterogeneity In Distributed ML Training With Dynamic Batching S. Tyagi Prateek Sharma 24 22 0 20 May 2023
STSyn: Speeding Up Local SGD with Straggler-Tolerant Synchronization Feng Zhu Jingjing Zhang Xin Wang 33 3 0 06 Oct 2022
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent Pedro Soto Ilia Ilmer Haibin Guan Jun Li 35 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 37 1 0 25 Aug 2021
Robust and Communication-Efficient Collaborative Learning Amirhossein Reisizadeh Hossein Taheri Aryan Mokhtari Hamed Hassani Ramtin Pedarsani 25 89 0 24 Jul 2019
Optimal Distributed Online Prediction using Mini-Batches O. Dekel Ran Gilad-Bachrach Ohad Shamir Lin Xiao 179 683 0 07 Dec 2010