Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data

28 October 2023

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