Federated Learning with Extremely Noisy Clients via Negative
Distillation

Federated Learning with Extremely Noisy Clients via Negative Distillation

20 December 2023

Bo Han

Yiu-ming Cheung

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

5 / 5 papers shown

Title
FNBench: Benchmarking Robust Federated Learning against Noisy Labels Xuefeng Jiang Jia Li Nannan Wu Z. F. Wu Xujing Li Sheng Sun Gang Xu Y. Wang Qi Li Min Liu FedML 28 2 0 10 May 2025
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 53 0 0 30 Nov 2024
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 21 76 0 25 Feb 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,109 0 06 Jun 2015
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 282 39,170 0 01 Sep 2014