FedFa: A Fully Asynchronous Training Paradigm for Federated Learning

FedFa: A Fully Asynchronous Training Paradigm for Federated Learning

17 April 2024

Khalid Ayedh Alharthi

Papers citing "FedFa: A Fully Asynchronous Training Paradigm for Federated Learning"

10 / 10 papers shown

Title
Corrected with the Latest Version: Make Robust Asynchronous Federated Learning Possible Chaoyi Lu Yiding Sun Pengbo Li Zhichuan Yang FedML 98 1 0 05 Apr 2025
Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics Prajjwal Bhargava Aleksandr Drozd Anna Rogers 134 105 0 04 Oct 2021
Asynchronous Federated Learning on Heterogeneous Devices: A Survey Chenhao Xu Youyang Qu Yong Xiang Longxiang Gao FedML 156 254 0 09 Sep 2021
LoRA: Low-Rank Adaptation of Large Language Models J. E. Hu Yelong Shen Phillip Wallis Zeyuan Allen-Zhu Yuanzhi Li Shean Wang Lu Wang Weizhu Chen OffRL AI4TS AI4CE ALM AIMat 466 10,367 0 17 Jun 2021
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization Jianyu Wang Qinghua Liu Hao Liang Gauri Joshi H. Vincent Poor MoMe FedML 63 1,337 0 15 Jul 2020
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 256 6,261 0 10 Dec 2019
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification T. Hsu Qi Matthew Brown FedML 143 1,150 0 13 Sep 2019
LEAF: A Benchmark for Federated Settings S. Caldas Sai Meher Karthik Duddu Peter Wu Tian Li Jakub Konecný H. B. McMahan Virginia Smith Ameet Talwalkar FedML 147 1,421 0 03 Dec 2018
Local SGD Converges Fast and Communicates Little Sebastian U. Stich FedML 176 1,063 0 24 May 2018
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 406 17,486 0 17 Feb 2016