FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels

20 May 2022

Papers citing "FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels"

6 / 6 papers shown

Title
Data Quality Control in Federated Instruction-tuning of Large Language Models Yaxin Du Rui Ye Fengting Yuchi W. Zhao Jingjing Qu Y. Wang Siheng Chen ALM FedML 45 0 0 15 Oct 2024
FedNoisy: Federated Noisy Label Learning Benchmark Siqi Liang Jintao Huang Junyuan Hong Dun Zeng Jiayu Zhou Zenglin Xu FedML 32 4 0 20 Jun 2023
Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients Victoria Huang S. Sohail Michael Mayo T. Lorido-Botran Mark Rodrigues Chris Anderson Melanie Ooi FedML 11 2 0 16 May 2023
Quantifying the Impact of Label Noise on Federated Learning Shuqi Ke Chao Huang Xin Liu FedML 8 7 0 15 Nov 2022
Adaptive Personalized Federated Learning Yuyang Deng Mohammad Mahdi Kamani M. Mahdavi FedML 183 535 0 30 Mar 2020
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 303 488 0 05 Mar 2020