ZeroFL: Efficient On-Device Training for Federated Learning with Local
Sparsity

ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity

4 August 2022

Javier Fernandez-Marques

Titouan Parcollet

Nicholas D. Lane

Papers citing "ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity"

8 / 8 papers shown

Title
When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devices Peng Zhang Yingjie Liu Yingbo Zhou Xiao Du Xian Wei Ting Wang Mingsong Chen FedML 17 1 0 08 May 2024
Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization Bailey J. Eccles Leon Wong Blesson Varghese 33 2 0 22 Apr 2024
FedImpro: Measuring and Improving Client Update in Federated Learning Zhenheng Tang Yonggang Zhang S. Shi Xinmei Tian Tongliang Liu Bo Han Xiaowen Chu FedML 19 13 0 10 Feb 2024
Federated learning compression designed for lightweight communications Lucas Grativol Ribeiro Mathieu Léonardon Guillaume Muller Virginie Fresse Matthieu Arzel FedML 25 3 0 23 Oct 2023
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices Kilian Pfeiffer R. Khalili J. Henkel FedML 37 5 0 26 May 2023
Protea: Client Profiling within Federated Systems using Flower Wanru Zhao Xinchi Qiu Javier Fernandez-Marques Pedro Porto Buarque de Gusmão Nicholas D. Lane 27 6 0 03 Jul 2022
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 178 267 0 26 Feb 2021
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 141 684 0 31 Jan 2021