Natural Compression for Distributed Deep Learning

27 May 2019

Papers citing "Natural Compression for Distributed Deep Learning"

25 / 25 papers shown

Title
Accelerated Distributed Optimization with Compression and Error Feedback Yuan Gao Anton Rodomanov Jeremy Rack Sebastian U. Stich 43 0 0 11 Mar 2025
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression Laurent Condat A. Maranjyan Peter Richtárik 39 3 0 07 Mar 2024
Correlated Quantization for Faster Nonconvex Distributed Optimization Andrei Panferov Yury Demidovich Ahmad Rammal Peter Richtárik MQ 25 4 0 10 Jan 2024
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates Ahmad Rammal Kaja Gruntkowska Nikita Fedin Eduard A. Gorbunov Peter Richtárik 35 5 0 15 Oct 2023
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks Natalie Lang Nir Shlezinger Rafael G. L. DÓliveira S. E. Rouayheb FedML 65 4 0 01 Aug 2023
A Survey From Distributed Machine Learning to Distributed Deep Learning Mohammad Dehghani Zahra Yazdanparast 15 0 0 11 Jul 2023
ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression Avetik G. Karagulyan Peter Richtárik FedML 21 6 0 08 Mar 2023
Does Federated Learning Really Need Backpropagation? H. Feng Tianyu Pang Chao Du Wei-Neng Chen Shuicheng Yan Min-Bin Lin FedML 26 10 0 28 Jan 2023
Joint Privacy Enhancement and Quantization in Federated Learning Natalie Lang Elad Sofer Tomer Shaked Nir Shlezinger FedML 27 46 0 23 Aug 2022
Federated Random Reshuffling with Compression and Variance Reduction Grigory Malinovsky Peter Richtárik FedML 16 10 0 08 May 2022
TOFU: Towards Obfuscated Federated Updates by Encoding Weight Updates into Gradients from Proxy Data Isha Garg M. Nagaraj Kaushik Roy FedML 13 1 0 21 Jan 2022
Large-Scale Deep Learning Optimizations: A Comprehensive Survey Xiaoxin He Fuzhao Xue Xiaozhe Ren Yang You 22 14 0 01 Nov 2021
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback Ilyas Fatkhullin Igor Sokolov Eduard A. Gorbunov Zhize Li Peter Richtárik 44 44 0 07 Oct 2021
Communication-Efficient Federated Learning with Binary Neural Networks YuZhi Yang Zhaoyang Zhang Qianqian Yang FedML 16 31 0 05 Oct 2021
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 173 411 0 14 Jul 2021
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges Mingzhe Chen Deniz Gündüz Kaibin Huang Walid Saad M. Bennis Aneta Vulgarakis Feljan H. Vincent Poor 21 401 0 05 Apr 2021
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices Max Ryabinin Eduard A. Gorbunov Vsevolod Plokhotnyuk Gennady Pekhimenko 27 31 0 04 Mar 2021
On the Utility of Gradient Compression in Distributed Training Systems Saurabh Agarwal Hongyi Wang Shivaram Venkataraman Dimitris Papailiopoulos 23 46 0 28 Feb 2021
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout Samuel Horváth Stefanos Laskaridis Mario Almeida Ilias Leondiadis Stylianos I. Venieris Nicholas D. Lane 176 267 0 26 Feb 2021
MARINA: Faster Non-Convex Distributed Learning with Compression Eduard A. Gorbunov Konstantin Burlachenko Zhize Li Peter Richtárik 28 108 0 15 Feb 2021
Distributed Second Order Methods with Fast Rates and Compressed Communication Rustem Islamov Xun Qian Peter Richtárik 19 51 0 14 Feb 2021
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization Zhize Li D. Kovalev Xun Qian Peter Richtárik FedML AI4CE 13 133 0 26 Feb 2020
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor M. Safaryan Egor Shulgin Peter Richtárik 24 60 0 20 Feb 2020
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning Aritra Dutta El Houcine Bergou A. Abdelmoniem Chen-Yu Ho Atal Narayan Sahu Marco Canini Panos Kalnis 22 76 0 19 Nov 2019
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning Anis Elgabli Jihong Park Amrit Singh Bedi Chaouki Ben Issaid M. Bennis Vaneet Aggarwal 11 67 0 23 Oct 2019