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Federated Learning with Extremely Noisy Clients via Negative
  Distillation
v1v2 (latest)

Federated Learning with Extremely Noisy Clients via Negative Distillation

20 December 2023
Yang Lu
Lin Chen
Yonggang Zhang
Yiliang Zhang
Bo Han
Yiu-ming Cheung
Hanzi Wang
    FedML
ArXiv (abs)PDFHTMLGithub (7★)

Papers citing "Federated Learning with Extremely Noisy Clients via Negative Distillation"

4 / 4 papers shown
Title
FNBench: Benchmarking Robust Federated Learning against Noisy Labels
FNBench: Benchmarking Robust Federated Learning against Noisy Labels
Xuefeng Jiang
Jia Li
Nannan Wu
Z. F. Wu
Xujing Li
Sheng Sun
Gang Xu
Yansen Wang
Qi Li
Min Liu
FedML
87
3
0
10 May 2025
Learning Locally, Revising Globally: Global Reviser for Federated
  Learning with Noisy Labels
Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
Yuxin Tian
Mouxing Yang
Yuhao Zhou
Jian Wang
Qing Ye
Tongliang Liu
Gang Niu
Jiancheng Lv
FedML
148
0
0
30 Nov 2024
FedNoisy: Federated Noisy Label Learning Benchmark
FedNoisy: Federated Noisy Label Learning Benchmark
Siqi Liang
Jintao Huang
Junyuan Hong
Dun Zeng
Jiayu Zhou
Zenglin Xu
FedML
151
7
0
20 Jun 2023
Emerging Trends in Federated Learning: From Model Fusion to Federated X
  Learning
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
Shaoxiong Ji
Yue Tan
Teemu Saravirta
Zhiqin Yang
Yixin Liu
Lauri Vasankari
Shirui Pan
Guodong Long
A. Walid
FedML
159
79
0
25 Feb 2021
1