Anytime Stochastic Gradient Descent: A Time to Hear from all the Workers

6 October 2018

Papers citing "Anytime Stochastic Gradient Descent: A Time to Hear from all the Workers"

7 / 7 papers shown

Title
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent Pedro Soto Ilia Ilmer Haibin Guan Jun Li 38 3 0 31 Jan 2022
Gradient Coding with Dynamic Clustering for Straggler-Tolerant Distributed Learning Baturalp Buyukates Emre Ozfatura S. Ulukus Deniz Gunduz 35 15 0 01 Mar 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients A. Mitra Rayana H. Jaafar George J. Pappas Hamed Hassani FedML 55 157 0 14 Feb 2021
Diversity/Parallelism Trade-off in Distributed Systems with Redundancy Pei Peng E. Soljanin P. Whiting 30 14 0 05 Oct 2020
Coded Distributed Computing with Partial Recovery Emre Ozfatura S. Ulukus Deniz Gunduz 38 28 0 04 Jul 2020
Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability J. Keuper Franz-Josef Pfreundt GNN 55 97 0 22 Sep 2016
Optimal Distributed Online Prediction using Mini-Batches O. Dekel Ran Gilad-Bachrach Ohad Shamir Lin Xiao 186 683 0 07 Dec 2010