A comprehensive survey on federated semi-supervised learning: applications, challenges, and future directions

Abstract Federated Learning (FL) is a decentralized method for collaboratively training a global machine learning model while preserving the privacy of user data. In recent years, its privacy-preserving and collaborative nature has attracted substantial interest among researchers. However, in real-world scenarios, it is often impractical to assume that all clients have fully labeled data, as acquiring large labeled datasets can be costly and requires expertise. To overcome this problem in FL, a new approach called Federated Semi-Supervised Learning (FSSL) is presented which aims to enhance the overall performance of the global model in FL by incorporating unlabeled data during model training. To provide a thorough examination of this area and to stimulate further research, we provide a systematic and comprehensive overview of recent studies using the FSSL method. We also discuss current challenges and open issues to provide researchers with directions for future research.