On the Discrepancy between the Theoretical Analysis and Practical
Implementations of Compressed Communication for Distributed Deep Learning

On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning

19 November 2019

El Houcine Bergou

Atal Narayan Sahu

Papers citing "On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning"

12 / 12 papers shown

Title
Personalized federated learning based on feature fusion Wolong Xing Zhenkui Shi Hongyan Peng Xiantao Hu Xianxian Li FedML 39 0 0 24 Jun 2024
$How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance$ How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance Hongkang Li Shuai Zhang Yihua Zhang Meng Wang Sijia Liu Pin-Yu Chen 41 4 0 12 Mar 2024
RS-DGC: Exploring Neighborhood Statistics for Dynamic Gradient Compression on Remote Sensing Image Interpretation Weiying Xie Zixuan Wang Jitao Ma Daixun Li Yunsong Li 30 0 0 29 Dec 2023
Demystifying the Myths and Legends of Nonconvex Convergence of SGD Aritra Dutta El Houcine Bergou Soumia Boucherouite Nicklas Werge M. Kandemir Xin Li 26 0 0 19 Oct 2023
Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception? Xiaoxiao Sun Nidham Gazagnadou Vivek Sharma Lingjuan Lyu Hongdong Li Liang Zheng 47 8 0 22 Sep 2023
On the effectiveness of partial variance reduction in federated learning with heterogeneous data Bo-wen Li Mikkel N. Schmidt T. S. Alstrøm Sebastian U. Stich FedML 37 9 0 05 Dec 2022
Adaptive Compression for Communication-Efficient Distributed Training Maksim Makarenko Elnur Gasanov Rustem Islamov Abdurakhmon Sadiev Peter Richtárik 39 13 0 31 Oct 2022
Distributed Learning With Sparsified Gradient Differences Yicheng Chen Rick S. Blum Martin Takáč Brian M. Sadler 31 15 0 05 Feb 2022
FastSGD: A Fast Compressed SGD Framework for Distributed Machine Learning Keyu Yang Lu Chen Zhihao Zeng Yunjun Gao 20 9 0 08 Dec 2021
Rethinking gradient sparsification as total error minimization Atal Narayan Sahu Aritra Dutta A. Abdelmoniem Trambak Banerjee Marco Canini Panos Kalnis 45 56 0 02 Aug 2021
Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees S. Shi Zhenheng Tang Qiang-qiang Wang Kaiyong Zhao X. Chu 19 22 0 20 Nov 2019
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017