Deep Gradient Compression: Reducing the Communication Bandwidth for
Distributed Training

Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

5 December 2017

Song Han

Papers citing "Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training"

16 / 616 papers shown

Title
Federated Learning with Non-IID Data Yue Zhao Meng Li Liangzhen Lai Naveen Suda Damon Civin Vikas Chandra FedML 46 2,526 0 02 Jun 2018
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training Maohua Zhu Jason Clemons Jeff Pool Minsoo Rhu S. Keckler Yuan Xie 11 13 0 01 Jun 2018
Grow and Prune Compact, Fast, and Accurate LSTMs Xiaoliang Dai Hongxu Yin N. Jha VLM SyDa 31 90 0 30 May 2018
cpSGD: Communication-efficient and differentially-private distributed SGD Naman Agarwal A. Suresh Felix X. Yu Sanjiv Kumar H. B. McMahan FedML 28 486 0 27 May 2018
Local SGD Converges Fast and Communicates Little Sebastian U. Stich FedML 79 1,047 0 24 May 2018
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication Felix Sattler Simon Wiedemann K. Müller Wojciech Samek MQ 36 211 0 22 May 2018
Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training Liang Luo Jacob Nelson Luis Ceze Amar Phanishayee Arvind Krishnamurthy 76 120 0 21 May 2018
Exploiting Unintended Feature Leakage in Collaborative Learning Luca Melis Congzheng Song Emiliano De Cristofaro Vitaly Shmatikov FedML 81 1,455 0 10 May 2018
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis Tal Ben-Nun Torsten Hoefler GNN 33 703 0 26 Feb 2018
SparCML: High-Performance Sparse Communication for Machine Learning Cédric Renggli Saleh Ashkboos Mehdi Aghagolzadeh Dan Alistarh Torsten Hoefler 29 126 0 22 Feb 2018
3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning Hyeontaek Lim D. Andersen M. Kaminsky 15 70 0 21 Feb 2018
On Scale-out Deep Learning Training for Cloud and HPC Srinivas Sridharan K. Vaidyanathan Dhiraj D. Kalamkar Dipankar Das Mikhail E. Smorkalov ... Dheevatsa Mudigere Naveen Mellempudi Sasikanth Avancha Bharat Kaul Pradeep Dubey BDL 26 30 0 24 Jan 2018
Differentially Private Federated Learning: A Client Level Perspective Robin C. Geyer T. Klein Moin Nabi FedML 39 1,280 0 20 Dec 2017
Differentially Private Distributed Learning for Language Modeling Tasks Vadim Popov Mikhail Kudinov Irina Piontkovskaya Petr Vytovtov A. Nevidomsky FedML 38 3 0 20 Dec 2017
Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method Xu Sun Xuancheng Ren Shuming Ma Bingzhen Wei Wei Li Jingjing Xu Houfeng Wang Yi Zhang 23 25 0 17 Nov 2017
Gradient Sparsification for Communication-Efficient Distributed Optimization Jianqiao Wangni Jialei Wang Ji Liu Tong Zhang 15 522 0 26 Oct 2017