Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoT

no code implementations • 5 Mar 2023 • Jiaqi Wang, Shenglai Zeng, Zewei Long, Yaqing Wang, Houping Xiao, Fenglong Ma

This is a new yet practical scenario in federated learning, i. e., labels-at-server semi-supervised federated learning (SemiFL).

Federated Learning Network Pruning

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

FedCon: A Contrastive Framework for Federated Semi-Supervised Learning

no code implementations • 9 Sep 2021 • Zewei Long, Jiaqi Wang, Yaqing Wang, Houping Xiao, Fenglong Ma

Most existing FedSSL methods focus on the classical scenario, i. e, the labeled and unlabeled data are stored at the client side.

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