Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation

3 October 2022

Papers citing "Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation"

20 / 20 papers shown

Title
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning Anastasia Koloskova Sebastian U. Stich Martin Jaggi FedML 40 80 0 16 Jun 2022
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays Konstantin Mishchenko Francis R. Bach Mathieu Even Blake E. Woodworth 54 59 0 15 Jun 2022
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 267 418 0 14 Jul 2021
Federated Learning with Buffered Asynchronous Aggregation John Nguyen Kshitiz Malik Hongyuan Zhan Ashkan Yousefpour Michael G. Rabbat Mani Malek Dzmitry Huba FedML 76 303 0 11 Jun 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz Ziyu Liu Thomas Steinke FedML 71 239 0 12 Feb 2021
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization Jianyu Wang Qinghua Liu Hao Liang Gauri Joshi H. Vincent Poor MoMe FedML 49 1,329 0 15 Jul 2020
Advances in Asynchronous Parallel and Distributed Optimization By Mahmoud Assran Arda Aytekin Hamid Reza Feyzmahdavian M. Johansson Michael G. Rabbat 51 76 0 24 Jun 2020
Personalized Federated Learning with Moreau Envelopes Canh T. Dinh N. H. Tran Tuan Dung Nguyen FedML 81 992 0 16 Jun 2020
Asynchronous Federated Learning with Differential Privacy for Edge Intelligence Yanan Li Shusen Yang Xuebin Ren Cong Zhao FedML 44 33 0 17 Dec 2019
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 183 6,229 0 10 Dec 2019
Tighter Theory for Local SGD on Identical and Heterogeneous Data Ahmed Khaled Konstantin Mishchenko Peter Richtárik 68 433 0 10 Sep 2019
Decentralized Deep Learning with Arbitrary Communication Compression Anastasia Koloskova Tao R. Lin Sebastian U. Stich Martin Jaggi FedML 39 235 0 22 Jul 2019
Asynchronous Federated Optimization Cong Xie Oluwasanmi Koyejo Indranil Gupta FedML 65 566 0 10 Mar 2019
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning Hao Yu Sen Yang Shenghuo Zhu MoMe FedML 73 604 0 17 Jul 2018
Local SGD Converges Fast and Communicates Little Sebastian U. Stich FedML 164 1,061 0 24 May 2018
Practical Secure Aggregation for Federated Learning on User-Held Data Keith Bonawitz Vladimir Ivanov Ben Kreuter Antonio Marcedone H. B. McMahan Sarvar Patel Daniel Ramage Aaron Segal Karn Seth FedML 65 501 0 14 Nov 2016
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 286 4,636 0 18 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 375 17,399 0 17 Feb 2016
An Asynchronous Mini-Batch Algorithm for Regularized Stochastic Optimization Hamid Reza Feyzmahdavian Arda Aytekin M. Johansson 48 117 0 18 May 2015
HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent Feng Niu Benjamin Recht Christopher Ré Stephen J. Wright 165 2,273 0 28 Jun 2011