Tackling Noisy Clients in Federated Learning with End-to-end Label
Correction

Tackling Noisy Clients in Federated Learning with End-to-end Label Correction

8 August 2024

Xuefeng Jiang

Papers citing "Tackling Noisy Clients in Federated Learning with End-to-end Label Correction"

4 / 4 papers shown

Title
Knowledge Augmentation in Federation: Rethinking What Collaborative Learning Can Bring Back to Decentralized Data Wentai Wu Ligang He Saiqin Long Ahmed M. Abdelmoniem Yingliang Wu Rui Mao 55 0 0 05 Mar 2025
Text Guided Image Editing with Automatic Concept Locating and Forgetting Jia Li Lijie Hu Zhixian He Jingfeng Zhang Tianhang Zheng Di Wang DiffM 38 8 0 30 May 2024
Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance Youngjoon Lee Sangwoo Park Joonhyuk Kang FedML 30 7 0 29 Oct 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