Basis Matters: Better Communication-Efficient Second Order Methods for
Federated Learning

Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning

2 November 2021

Peter Richtárik

Papers citing "Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning"

10 / 10 papers shown

Title
Review of Mathematical Optimization in Federated Learning Shusen Yang Fangyuan Zhao Zihao Zhou Liang Shi Xuebin Ren Zongben Xu FedML AI4CE 67 0 0 02 Dec 2024
A second-order-like optimizer with adaptive gradient scaling for deep learning Jérôme Bolte Ryan Boustany Edouard Pauwels Andrei Purica ODL 25 0 0 08 Oct 2024
FAGH: Accelerating Federated Learning with Approximated Global Hessian Mrinmay Sen A. K. Qin Krishna Mohan FedML 14 0 0 16 Mar 2024
Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates Guangchen Lan Han Wang James Anderson Christopher G. Brinton Vaneet Aggarwal FedML 19 27 0 09 Oct 2023
Towards a Better Theoretical Understanding of Independent Subnetwork Training Egor Shulgin Peter Richtárik AI4CE 16 6 0 28 Jun 2023
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox Abdurakhmon Sadiev D. Kovalev Peter Richtárik 15 20 0 08 Jul 2022
FedSSO: A Federated Server-Side Second-Order Optimization Algorithm Xinteng Ma Renyi Bao Jinpeng Jiang Yang Liu Arthur Jiang Junhua Yan Xin Liu Zhisong Pan FedML 24 6 0 20 Jun 2022
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation Rustem Islamov Xun Qian Slavomír Hanzely M. Safaryan Peter Richtárik 30 16 0 07 Jun 2022
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence Sen Na Michal Derezinski Michael W. Mahoney 22 16 0 20 Apr 2022
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing Nicolò Dal Fabbro S. Dey M. Rossi Luca Schenato FedML 26 14 0 11 Feb 2022