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Gradient Descent with Compressed Iterates

10 September 2019

Papers citing "Gradient Descent with Compressed Iterates"

29 / 29 papers shown

Title
First Analysis of Local GD on Heterogeneous Data Ahmed Khaled Konstantin Mishchenko Peter Richtárik FedML 74 172 0 10 Sep 2019
Federated Learning: Challenges, Methods, and Future Directions Tian Li Anit Kumar Sahu Ameet Talwalkar Virginia Smith FedML 129 4,540 0 21 Aug 2019
Robust and Communication-Efficient Collaborative Learning Amirhossein Reisizadeh Hossein Taheri Aryan Mokhtari Hamed Hassani Ramtin Pedarsani 87 92 0 24 Jul 2019
Decentralized Deep Learning with Arbitrary Communication Compression Anastasia Koloskova Tao R. Lin Sebastian U. Stich Martin Jaggi FedML 48 235 0 22 Jul 2019
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations Debraj Basu Deepesh Data C. Karakuş Suhas Diggavi MQ 60 406 0 06 Jun 2019
Federated Machine Learning: Concept and Applications Qiang Yang Yang Liu Tianjian Chen Yongxin Tong FedML 78 2,322 0 13 Feb 2019
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication Anastasia Koloskova Sebastian U. Stich Martin Jaggi FedML 83 511 0 01 Feb 2019
Distributed Learning with Compressed Gradient Differences Konstantin Mishchenko Eduard A. Gorbunov Martin Takáč Peter Richtárik 98 201 0 26 Jan 2019
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements S. Caldas Jakub Konecný H. B. McMahan Ameet Talwalkar 77 450 0 18 Dec 2018
Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD Jianyu Wang Gauri Joshi FedML 81 232 0 19 Oct 2018
Sparsified SGD with Memory Sebastian U. Stich Jean-Baptiste Cordonnier Martin Jaggi 82 754 0 20 Sep 2018
Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms Jianyu Wang Gauri Joshi 170 349 0 22 Aug 2018
Don't Use Large Mini-Batches, Use Local SGD Tao R. Lin Sebastian U. Stich Kumar Kshitij Patel Martin Jaggi 119 432 0 22 Aug 2018
Federated Learning with Non-IID Data Yue Zhao Meng Li Liangzhen Lai Naveen Suda Damon Civin Vikas Chandra FedML 165 2,580 0 02 Jun 2018
Local SGD Converges Fast and Communicates Little Sebastian U. Stich FedML 186 1,069 0 24 May 2018
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge Takayuki Nishio Ryo Yonetani FedML 122 1,407 0 23 Apr 2018
Adaptive Federated Learning in Resource Constrained Edge Computing Systems Shiqiang Wang Tiffany Tuor Theodoros Salonidis K. Leung C. Makaya T. He Kevin S. Chan 254 1,716 0 14 Apr 2018
Communication Compression for Decentralized Training Hanlin Tang Shaoduo Gan Ce Zhang Tong Zhang Ji Liu 68 273 0 17 Mar 2018
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis Tal Ben-Nun Torsten Hoefler GNN 62 707 0 26 Feb 2018
signSGD: Compressed Optimisation for Non-Convex Problems Jeremy Bernstein Yu Wang Kamyar Azizzadenesheli Anima Anandkumar FedML ODL 101 1,050 0 13 Feb 2018
Differentially Private Federated Learning: A Client Level Perspective Robin C. Geyer T. Klein Moin Nabi FedML 136 1,300 0 20 Dec 2017
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Chengyue Wu Song Han Huizi Mao Yu Wang W. Dally 148 1,409 0 05 Dec 2017
Gradient Sparsification for Communication-Efficient Distributed Optimization Jianqiao Wangni Jialei Wang Ji Liu Tong Zhang 90 528 0 26 Oct 2017
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent Xiangru Lian Ce Zhang Huan Zhang Cho-Jui Hsieh Wei Zhang Ji Liu 50 1,233 0 25 May 2017
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning W. Wen Cong Xu Feng Yan Chunpeng Wu Yandan Wang Yiran Chen Hai Helen Li 160 990 0 22 May 2017
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 309 4,655 0 18 Oct 2016
Federated Optimization: Distributed Machine Learning for On-Device Intelligence Jakub Konecný H. B. McMahan Daniel Ramage Peter Richtárik FedML 143 1,902 0 08 Oct 2016
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding Dan Alistarh Demjan Grubic Jerry Li Ryota Tomioka Milan Vojnović MQ 80 423 0 07 Oct 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 406 17,559 0 17 Feb 2016