Distributed learning with compressed gradients

18 June 2018

Papers citing "Distributed learning with compressed gradients"

22 / 22 papers shown

Title
DARE the Extreme: Revisiting Delta-Parameter Pruning For Fine-Tuned Models Wenlong Deng Yize Zhao V. Vakilian Minghui Chen Xiaoxiao Li Christos Thrampoulidis 45 3 0 12 Oct 2024
Rendering Wireless Environments Useful for Gradient Estimators: A Zero-Order Stochastic Federated Learning Method Elissa Mhanna Mohamad Assaad 49 1 0 30 Jan 2024
Federated Learning is Better with Non-Homomorphic Encryption Konstantin Burlachenko Abdulmajeed Alrowithi Fahad Ali Albalawi Peter Richtárik FedML 47 6 0 04 Dec 2023
Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization Zhen Qin Zhishuai Liu Pan Xu 26 1 0 24 Oct 2023
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates Ahmad Rammal Kaja Gruntkowska Nikita Fedin Eduard A. Gorbunov Peter Richtárik 45 5 0 15 Oct 2023
Adaptive Compression for Communication-Efficient Distributed Training Maksim Makarenko Elnur Gasanov Rustem Islamov Abdurakhmon Sadiev Peter Richtárik 44 13 0 31 Oct 2022
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression Abdurakhmon Sadiev Grigory Malinovsky Eduard A. Gorbunov Igor Sokolov Ahmed Khaled Konstantin Burlachenko Peter Richtárik FedML 16 21 0 14 Jun 2022
Linear Stochastic Bandits over a Bit-Constrained Channel A. Mitra Hamed Hassani George J. Pappas 42 8 0 02 Mar 2022
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications Bokun Wang Tianbao Yang 48 31 0 24 Feb 2022
FL_PyTorch: optimization research simulator for federated learning Konstantin Burlachenko Samuel Horváth Peter Richtárik FedML 48 18 0 07 Feb 2022
BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression Haoyu Zhao Boyue Li Zhize Li Peter Richtárik Yuejie Chi 32 49 0 31 Jan 2022
Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching Tahseen Rabbani Brandon Yushan Feng Marco Bornstein Kyle Rui Sang Yifan Yang Arjun Rajkumar A. Varshney Furong Huang FedML 59 2 0 17 Sep 2021
FedNL: Making Newton-Type Methods Applicable to Federated Learning M. Safaryan Rustem Islamov Xun Qian Peter Richtárik FedML 33 78 0 05 Jun 2021
IntSGD: Adaptive Floatless Compression of Stochastic Gradients Konstantin Mishchenko Bokun Wang D. Kovalev Peter Richtárik 75 15 0 16 Feb 2021
Distributed Second Order Methods with Fast Rates and Compressed Communication Rustem Islamov Xun Qian Peter Richtárik 34 51 0 14 Feb 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
On Communication Compression for Distributed Optimization on Heterogeneous Data Sebastian U. Stich 53 23 0 04 Sep 2020
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization Zhize Li D. Kovalev Xun Qian Peter Richtárik FedML AI4CE 29 135 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 32 61 0 20 Feb 2020
Better Theory for SGD in the Nonconvex World Ahmed Khaled Peter Richtárik 13 179 0 09 Feb 2020
Natural Compression for Distributed Deep Learning Samuel Horváth Chen-Yu Ho L. Horvath Atal Narayan Sahu Marco Canini Peter Richtárik 21 151 0 27 May 2019
On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication Sindri Magnússon H. S. Ghadikolaei Na Li 27 81 0 26 Feb 2019