FedShuffle: Recipes for Better Use of Local Work in Federated Learning

27 April 2022

Papers citing "FedShuffle: Recipes for Better Use of Local Work in Federated Learning"

6 / 6 papers shown

Title
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs Yan Sun Li Shen Dacheng Tao FedML 25 0 0 27 Sep 2024
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity A. Maranjyan M. Safaryan Peter Richtárik 34 13 0 28 Oct 2022
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression Abdurakhmon Sadiev Grigory Malinovsky Eduard A. Gorbunov Igor Sokolov Ahmed Khaled Konstantin Burlachenko Peter Richtárik FedML 16 21 0 14 Jun 2022
Straggler-Resilient Personalized Federated Learning Isidoros Tziotis Zebang Shen Ramtin Pedarsani Hamed Hassani Aryan Mokhtari FedML 31 9 0 05 Jun 2022
Papaya: Practical, Private, and Scalable Federated Learning Dzmitry Huba John Nguyen Kshitiz Malik Ruiyu Zhu Michael G. Rabbat ... H. Srinivas Kaikai Wang Anthony Shoumikhin Jesik Min Mani Malek FedML 110 137 0 08 Nov 2021
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout Samuel Horváth Stefanos Laskaridis Mario Almeida Ilias Leondiadis Stylianos I. Venieris Nicholas D. Lane 189 268 0 26 Feb 2021