FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning
This addresses the problem of limited labeled data in federated learning for distributed applications, but it is incremental as it builds on existing semi-supervised and federated learning techniques.
The paper tackles the challenge of exploiting unlabeled data in federated learning by proposing FedTriNet, a method that uses three networks and dynamic quality control to generate pseudo labels, and it outperforms state-of-the-art baselines on three datasets under IID and Non-IID settings.
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.