FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering

9 July 2024

Nian-Feng Tzeng

Papers citing "FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering"

3 / 3 papers shown

Title
SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training Md Sirajul Islam Sanjeev Panta F. Xu Xu Yuan Li Chen N. Tzeng FedML 36 0 0 22 Feb 2025
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces Saeed Vahidian Mahdi Morafah Weijia Wang Vyacheslav Kungurtsev C. L. P. Chen M. Shah Bill Lin FedML 40 45 0 21 Sep 2022
Federated Learning on Non-IID Data Silos: An Experimental Study Q. Li Yiqun Diao Quan Chen Bingsheng He FedML OOD 87 943 0 03 Feb 2021