FedDD: Toward Communication-efficient Federated Learning with
Differential Parameter Dropout

FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout

31 August 2023

Xu Chen

Papers citing "FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout"

6 / 6 papers shown

Title
Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey Feng Liang Zhen Zhang Haifeng Lu Victor C. M. Leung Yanyi Guo Xiping Hu GNN 29 6 0 09 Apr 2024
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data Tailin Zhou Zehong Lin Jinchao Zhang Danny H. K. Tsang MoMe FedML 21 12 0 13 May 2023
Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices Dingzhu Wen Ki-Jun Jeon Kaibin Huang FedML 66 88 0 30 Sep 2021
Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning Hyun-Suk Lee Jang-Won Lee 74 52 0 02 Mar 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 176 267 0 26 Feb 2021
Adaptive Federated Learning in Resource Constrained Edge Computing Systems Shiqiang Wang Tiffany Tuor Theodoros Salonidis K. Leung C. Makaya T. He Kevin S. Chan 144 1,680 0 14 Apr 2018