Rethinking Data Heterogeneity in Federated Learning: Introducing a New
Notion and Standard Benchmarks

Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks

30 September 2022

Saeed Vahidian

Papers citing "Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks"

7 / 7 papers shown

Title
FedCC: Robust Federated Learning against Model Poisoning Attacks Hyejun Jeong H. Son Seohu Lee Jayun Hyun T. Chung FedML 54 5 0 20 Feb 2025
PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy Achintha Wijesinghe Songyang Zhang Zhi Ding FedML 18 7 0 19 May 2023
When Do Curricula Work in Federated Learning? Saeed Vahidian Sreevatsank Kadaveru Woo-Ram Baek Weijia Wang Vyacheslav Kungurtsev C. L. P. Chen M. Shah Bill Lin FedML 30 11 0 24 Dec 2022
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
Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity Saeed Vahidian Mahdi Morafah Bill Lin 56 57 0 02 May 2021
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
Adaptive Personalized Federated Learning Yuyang Deng Mohammad Mahdi Kamani M. Mahdavi FedML 183 541 0 30 Mar 2020