  Federated Learning has shown great potentials for the distributed data
utilization and privacy protection. Most existing federated learning approaches
focus on the supervised setting, which means all the data stored in each client
has labels. However, in real-world applications, the client data are impossible
to be fully labeled. Thus, how to exploit the unlabeled data should be a new
challenge for federated learning. Although a few studies are attempting to
overcome this challenge, they may suffer from information leakage or misleading
information usage problems. To tackle these issues, in this paper, we propose a
novel federated semi-supervised learning method named FedTriNet, which consists
of two learning phases. In the first phase, we pre-train FedTriNet using
labeled data with FedAvg. In the second phase, we aim to make most of the
unlabeled data to help model learning. 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. Finally,
FedTriNet uses the new training set to retrain the model. Experimental results
on three publicly available datasets show that the proposed FedTriNet
outperforms state-of-the-art baselines under both IID and Non-IID settings.
