FedNL: Making Newton-Type Methods Applicable to Federated Learning

5 June 2021

Papers citing "FedNL: Making Newton-Type Methods Applicable to Federated Learning"

45 / 45 papers shown

Title
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration Xue Feng M. Paul Laiu Thomas Strohmer FedML 75 0 0 14 Mar 2025
Accelerated Distributed Optimization with Compression and Error Feedback Yuan Gao Anton Rodomanov Jeremy Rack Sebastian U. Stich 61 0 0 11 Mar 2025
MARINA-P: Superior Performance in Non-smooth Federated Optimization with Adaptive Stepsizes Igor Sokolov Peter Richtárik 106 1 0 22 Dec 2024
GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning Shayan Mohajer Hamidi Ali Bereyhi S. Asaad H. Vincent Poor 86 1 0 05 Dec 2024
Review of Mathematical Optimization in Federated Learning Shusen Yang Fangyuan Zhao Zihao Zhou Liang Shi Xuebin Ren Zongben Xu FedML AI4CE 93 1 0 02 Dec 2024
Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization Shivam Pal Aishwarya Gupta Saqib Sarwar Piyush Rai FedML 90 0 0 27 Nov 2024
Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis Zhijie Chen Qiaobo Li A. Banerjee FedML 42 0 0 11 Nov 2024
Error Feedback under $(L_0,L_1)$ -Smoothness: Normalization and Momentum Sarit Khirirat Abdurakhmon Sadiev Artem Riabinin Eduard A. Gorbunov Peter Richtárik 32 1 0 22 Oct 2024
FLeNS: Federated Learning with Enhanced Nesterov-Newton Sketch Sunny Gupta Mohit Jindal Pankhi Kashyap Pranav Jeevan Amit Sethi FedML 55 0 0 23 Sep 2024
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy Wei Huo Changxin Liu Kemi Ding Karl H. Johansson Ling Shi FedML 65 0 0 08 Aug 2024
Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm Ahmed Elbakary Chaouki Ben Issaid Mohammad Shehab Karim G. Seddik Tamer A. ElBatt Mehdi Bennis 72 2 0 10 Jun 2024
FAGH: Accelerating Federated Learning with Approximated Global Hessian Mrinmay Sen A. K. Qin Krishna Mohan FedML 45 0 0 16 Mar 2024
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning Jian Li Yong Liu Wei Wang Haoran Wu Weiping Wang FedML 61 2 0 05 Jan 2024
Distributed Adaptive Greedy Quasi-Newton Methods with Explicit Non-asymptotic Convergence Bounds Yubo Du Keyou You 57 4 0 30 Nov 2023
EControl: Fast Distributed Optimization with Compression and Error Control Yuan Gao Rustem Islamov Sebastian U. Stich 50 8 0 06 Nov 2023
AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms Rustem Islamov M. Safaryan Dan Alistarh FedML 42 13 0 31 Oct 2023
Matrix Compression via Randomized Low Rank and Low Precision Factorization R. Saha Varun Srivastava Mert Pilanci 36 20 0 17 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 60 5 0 15 Oct 2023
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 39 27 0 09 Oct 2023
FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation A. Maritan S. Dey Luca Schenato FedML 35 6 0 29 Sep 2023
Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup Yan Sun Li Shen Hao Sun Liang Ding Dacheng Tao FedML 29 17 0 30 Jul 2023
Towards a Better Theoretical Understanding of Independent Subnetwork Training Egor Shulgin Peter Richtárik AI4CE 57 6 0 28 Jun 2023
Federated Composite Saddle Point Optimization Site Bai Brian Bullins FedML 43 0 0 25 May 2023
Momentum Provably Improves Error Feedback! Ilyas Fatkhullin Alexander Tyurin Peter Richtárik 58 20 0 24 May 2023
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization Nicolò Dal Fabbro M. Rossi Luca Schenato S. Dey 31 0 0 18 May 2023
Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus A. Maritan Ganesh Sharma Luca Schenato S. Dey 38 2 0 13 May 2023
Advancements in Federated Learning: Models, Methods, and Privacy Hui Chen Huandong Wang Qingyue Long Depeng Jin Yong Li FedML 67 14 0 22 Feb 2023
FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences A. Agafonov Brahim Erraji Martin Takáč FedML 49 4 0 18 Oct 2022
PersA-FL: Personalized Asynchronous Federated Learning Taha Toghani Soomin Lee César A. Uribe FedML 78 6 0 03 Oct 2022
EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression Kaja Gruntkowska Alexander Tyurin Peter Richtárik 73 22 0 30 Sep 2022
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox Abdurakhmon Sadiev D. Kovalev Peter Richtárik 48 20 0 08 Jul 2022
Shifted Compression Framework: Generalizations and Improvements Egor Shulgin Peter Richtárik 20 6 0 21 Jun 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 39 6 0 20 Jun 2022
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning Anis Elgabli Chaouki Ben Issaid Amrit Singh Bedi K. Rajawat M. Bennis Vaneet Aggarwal FedML 21 30 0 17 Jun 2022
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation Rustem Islamov Xun Qian Slavomír Hanzely M. Safaryan Peter Richtárik 49 16 0 07 Jun 2022
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top Eduard A. Gorbunov Samuel Horváth Peter Richtárik Gauthier Gidel AAML 24 0 0 01 Jun 2022
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence Sen Na Michal Derezinski Michael W. Mahoney 44 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 67 14 0 11 Feb 2022
FL_PyTorch: optimization research simulator for federated learning Konstantin Burlachenko Samuel Horváth Peter Richtárik FedML 57 18 0 07 Feb 2022
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation Peter Richtárik Igor Sokolov Ilyas Fatkhullin Elnur Gasanov Zhize Li Eduard A. Gorbunov 31 31 0 02 Feb 2022
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning Xun Qian Rustem Islamov M. Safaryan Peter Richtárik FedML 53 23 0 02 Nov 2021
On Second-order Optimization Methods for Federated Learning Sebastian Bischoff Stephan Günnemann Martin Jaggi Sebastian U. Stich FedML 28 10 0 06 Sep 2021
Communication-Efficient Distributed Optimization with Quantized Preconditioners Foivos Alimisis Peter Davies Dan Alistarh 23 16 0 14 Feb 2021
Linearly Converging Error Compensated SGD Eduard A. Gorbunov D. Kovalev Dmitry Makarenko Peter Richtárik 170 78 0 23 Oct 2020
Distributed Computation for Marginal Likelihood based Model Choice Alexander K. Buchholz Daniel Ahfock S. Richardson FedML 30 5 0 10 Oct 2019