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2310.18815
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Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data
28 October 2023
Pramit Saha
Divyanshu Mishra
J. A. Noble
FedML
Re-assign community
ArXiv
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Papers citing
"Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data"
3 / 3 papers shown
Title
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification
Jiancheng Yang
Rui Shi
D. Wei
Zequan Liu
Lin Zhao
B. Ke
Hanspeter Pfister
Bingbing Ni
VLM
161
645
0
27 Oct 2021
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
Bowen Zhang
Yidong Wang
Wenxin Hou
Hao Wu
Jindong Wang
Manabu Okumura
T. Shinozaki
AAML
218
861
0
15 Oct 2021
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision
Chaoyang He
Zhengyu Yang
Erum Mushtaq
Sunwoo Lee
Mahdi Soltanolkotabi
A. Avestimehr
FedML
90
36
0
06 Oct 2021
1