Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling

15 April 2025

Papers citing "Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling"

21 / 21 papers shown

Title
Towards Fast Rates for Federated and Multi-Task Reinforcement Learning Feng Zhu Robert W. Heath Jr. Aritra Mitra 62 1 0 09 Sep 2024
Compressed Federated Reinforcement Learning with a Generative Model Ali Beikmohammadi Sarit Khirirat Sindri Magnússon FedML 97 2 0 26 Mar 2024
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling Arman Adibi Nicolò Dal Fabbro Luca Schenato Sanjeev R. Kulkarni H. Vincent Poor George J. Pappas Hamed Hassani A. Mitra 130 8 0 19 Feb 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 63 27 0 09 Oct 2023
Stability of Q-Learning Through Design and Optimism Sean P. Meyn 64 10 0 05 Jul 2023
Federated Reinforcement Learning with Environment Heterogeneity Hao Jin Yang Peng Wenhao Yang Shusen Wang Zhihua Zhang 84 74 0 06 Apr 2022
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee Flint Xiaofeng Fan Yining Ma Zhongxiang Dai Wei Jing Cheston Tan K. H. Low FedML AI4CE 59 79 0 26 Oct 2021
Distributed TD(0) with Almost No Communication R. Liu Alexander Olshevsky FedML 48 15 0 16 Apr 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients A. Mitra Rayana H. Jaafar George J. Pappas Hamed Hassani FedML 98 159 0 14 Feb 2021
Local SGD: Unified Theory and New Efficient Methods Eduard A. Gorbunov Filip Hanzely Peter Richtárik FedML 79 111 0 03 Nov 2020
Finite-Time Analysis of Asynchronous Stochastic Approximation and $Q$ -Learning Guannan Qu Adam Wierman 55 110 0 01 Feb 2020
Tighter Theory for Local SGD on Identical and Heterogeneous Data Ahmed Khaled Konstantin Mishchenko Peter Richtárik 71 433 0 10 Sep 2019
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement Learning Zaiwei Chen Sheng Zhang Thinh T. Doan John-Paul Clarke S. T. Maguluri 59 59 0 27 May 2019
$Stochastic approximation with cone-contractive operators: Sharp $\ell_\infty$-bounds for $Q$-learning$ Stochastic approximation with cone-contractive operators: Sharp $\ell_\infty$ -bounds for $Q$ -learning Martin J. Wainwright 43 105 0 15 May 2019
Towards Federated Learning at Scale: System Design Keith Bonawitz Hubert Eichner W. Grieskamp Dzmitry Huba A. Ingerman ... H. B. McMahan Timon Van Overveldt David Petrou Daniel Ramage Jason Roselander FedML 121 2,664 0 04 Feb 2019
Finite-Time Error Bounds For Linear Stochastic Approximation and TD Learning R. Srikant Lei Ying 63 252 0 03 Feb 2019
On Markov Chain Gradient Descent Tao Sun Yuejiao Sun W. Yin BDL 41 102 0 12 Sep 2018
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation Jalaj Bhandari Daniel Russo Raghav Singal 104 339 0 06 Jun 2018
Federated Optimization: Distributed Machine Learning for On-Device Intelligence Jakub Konecný H. B. McMahan Daniel Ramage Peter Richtárik FedML 135 1,897 0 08 Oct 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 401 17,468 0 17 Feb 2016
Ergodic Mirror Descent John C. Duchi Alekh Agarwal M. Johansson Michael I. Jordan 182 125 0 24 May 2011