Learning Unlabeled Clients Divergence via Anchor Model Aggregation for
Federated Semi-supervised Learning

Learning Unlabeled Clients Divergence via Anchor Model Aggregation for Federated Semi-supervised Learning

14 July 2024

Marawan Elbatel

Hao Wang

Xiaomeng Li

Papers citing "Learning Unlabeled Clients Divergence via Anchor Model Aggregation for Federated Semi-supervised Learning"

6 / 6 papers shown

Title
Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data Pramit Saha Divyanshu Mishra J. A. Noble FedML 24 8 0 28 Oct 2023
Towards Unbiased Training in Federated Open-world Semi-supervised Learning Jie M. Zhang Xiaosong Ma Song Guo Wenchao Xu FedML 22 8 0 01 May 2023
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging Rui Yan Liangqiong Qu Qingyue Wei Shih-Cheng Huang Liyue Shen D. Rubin Lei Xing Yuyin Zhou FedML 70 86 0 17 May 2022
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning Yidong Wang Hao Chen Qiang Heng Wenxin Hou Yue Fan ... Marios Savvides T. Shinozaki Bhiksha Raj Bernt Schiele Xing Xie 175 251 0 15 May 2022
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling Bowen Zhang Yidong Wang Wenxin Hou Hao Wu Jindong Wang Manabu Okumura T. Shinozaki AAML 213 848 0 15 Oct 2021
Balanced Meta-Softmax for Long-Tailed Visual Recognition Jiawei Ren Cunjun Yu Shunan Sheng Xiao Ma Haiyu Zhao Shuai Yi Hongsheng Li 157 541 0 21 Jul 2020