FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning

no code implementations • 12 Sep 2021 • Liwei Che, Zewei Long, Jiaqi Wang, Yaqing Wang, Houping Xiao, Fenglong Ma

In particular, we propose to use three networks and a dynamic quality control mechanism to generate high-quality pseudo labels for unlabeled data, which are added to the training set.

Federated Learning

FedSiam: Towards Adaptive Federated Semi-Supervised Learning

no code implementations • 6 Dec 2020 • Zewei Long, Liwei Che, Yaqing Wang, Muchao Ye, Junyu Luo, Jinze Wu, Houping Xiao, Fenglong Ma

In this paper, we focus on designing a general framework FedSiam to tackle different scenarios of federated semi-supervised learning, including four settings in the labels-at-client scenario and two setting in the labels-at-server scenario.

Federated Learning