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"

9 / 9 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 40 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 37 14 0 05 Oct 2020
Coded Distributed Computing with Partial Recovery Emre Ozfatura S. Ulukus Deniz Gunduz 43 29 0 04 Jul 2020
Efficient Replication for Straggler Mitigation in Distributed Computing Amir Behrouzi-Far E. Soljanin 24 14 0 03 Jun 2020
Adaptive Distributed Stochastic Gradient Descent for Minimizing Delay in the Presence of Stragglers Serge Kas Hanna Rawad Bitar Parimal Parag Venkateswara Dasari S. E. Rouayheb 35 16 0 25 Feb 2020
Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability J. Keuper Franz-Josef Pfreundt GNN 58 98 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