Complement Sparsification: Low-Overhead Model Pruning for Federated
Learning

Complement Sparsification: Low-Overhead Model Pruning for Federated Learning

10 March 2023

Cristian Borcea

Papers citing "Complement Sparsification: Low-Overhead Model Pruning for Federated Learning"

6 / 6 papers shown

Title
FedSpaLLM: Federated Pruning of Large Language Models Guangji Bai Yijiang Li Zilinghan Li Liang Zhao Kibaek Kim FedML 65 4 0 20 Feb 2025
Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices Dingzhu Wen Ki-Jun Jeon Kaibin Huang FedML 70 90 0 30 Sep 2021
Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity Saeed Vahidian Mahdi Morafah Bill Lin 59 58 0 02 May 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
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 246 4,489 0 23 Jan 2020
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 1,687 0 14 Apr 2018