Federated semi-supervised learning (FSSL) involves training a model in a federated
environment using a few labeled samples and many unlabeled samples. Compared with …

One of the major drawbacks of federated learning (FL) is data imbalance and uneven
reliability of clients, which adversely impacts model performance and generalization ability …

Due to the high cost of labeling and the high requirements of annotation professionalism,
there is a lack of labeling of large quantities of data. As a solution to the problem of partially …

Federated Semi-Supervised Learning (FSSL) leverages both labeled and unlabeled data on
clients to collaboratively train a model. In FSSL, the heterogeneous data can introduce …

Federated semi-supervised learning (FSSL) is primarily challenged by two factors: the
scarcity of labeled data across clients and the non-independent and identically distribution …

Semi-supervised federated learning (SSFL) has emerged as a promising paradigm to
reduce the need for fully labeled data in training federated learning (FL) models. This paper …