Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression

17 May 2023

Papers citing "Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression"

10 / 10 papers shown

Title
Dyn-D $^2$ P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Z. Zhu Y. Huang Xin Wang Shouling Ji Jinming Xu 14 0 0 10 May 2025
Communication-efficient Vertical Federated Learning via Compressed Error Feedback Pedro Valdeira João Xavier Cláudia Soares Yuejie Chi FedML 30 4 0 20 Jun 2024
The Privacy Power of Correlated Noise in Decentralized Learning Youssef Allouah Anastasia Koloskova Aymane El Firdoussi Martin Jaggi R. Guerraoui 22 4 0 02 May 2024
Sparse Training for Federated Learning with Regularized Error Correction Ran Greidi Kobi Cohen FedML 19 1 0 21 Dec 2023
Communication-Efficient Federated Optimization over Semi-Decentralized Networks He Wang Yuejie Chi FedML 16 2 0 30 Nov 2023
Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression Sijin Chen Zhize Li Yuejie Chi FedML 25 0 0 29 Oct 2023
Smoothed Gradient Clipping and Error Feedback for Distributed Optimization under Heavy-Tailed Noise Shuhua Yu D. Jakovetić S. Kar 20 1 0 25 Oct 2023
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees Anastasia Koloskova Hadrien Hendrikx Sebastian U. Stich 99 48 0 02 May 2023
On the Complexity of Decentralized Smooth Nonconvex Finite-Sum Optimization Luo Luo Yunyan Bai Lesi Chen Yuxing Liu Haishan Ye 19 8 0 25 Oct 2022
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback Ilyas Fatkhullin Igor Sokolov Eduard A. Gorbunov Zhize Li Peter Richtárik 42 44 0 07 Oct 2021