Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients

26 January 2022

Papers citing "Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients"

8 / 8 papers shown

Title
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data Junki Mori Kosuke Kihara Taiki Miyagawa Akinori F. Ebihara Isamu Teranishi Hisashi Kashima 71 1 0 18 Dec 2024
Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets Xingrun Yan Shiyuan Zuo Rongfei Fan Han Hu Li Shen Puning Zhao Yong Luo FedML 47 0 0 19 Aug 2024
One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity Naibo Wang Yuchen Deng Wenjie Feng Shichen Fan Jianwei Yin See-Kiong Ng FedML 24 5 0 18 Apr 2024
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum Riccardo Zaccone Carlo Masone Marco Ciccone FedML 13 2 0 30 Nov 2023
A Privacy Preserving System for Movie Recommendations Using Federated Learning David Neumann Andreas Lutz Karsten Müller Wojciech Samek 11 9 0 07 Mar 2023
MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare Yiqiang Chen Wang Lu Xin Qin Jindong Wang Xing Xie FedML 28 57 0 17 Jun 2022
Federated Learning on Non-IID Data Silos: An Experimental Study Q. Li Yiqun Diao Quan Chen Bingsheng He FedML OOD 85 936 0 03 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 141 1,663 0 14 Apr 2018