Federated learning (FL) enables collaborative model training without centralising raw data, but its performance is susceptible to label noise from clients. A common mitigation strategy involves using a clean, labelled public dataset at the server to assess client reliability. However, this approach is impractical due to the unrealistic assumption of availability of a clean, labelled public dataset. To address this issue, we propose FedDS, a novel approach that brings the Dawid-Skene model from statistical analysis to FL, which enables the estimation of the reliability of each client in FL without requiring any labelled data at the server. This approach effectively mitigates the adverse impact of heterogeneous label noise under a weaker and more practical assumption, offering a robust aggregation strategy for real-world FL scenarios with label noise. The code is available at https://github.com/Gia99999/FedDS .

Variable selection is important for classification of data with many irrelevant predicting variables, but it has not yet been well studied in positive-unlabeled (PU) learning, where classifiers have to be trained without labelled-negative instances. In this paper, we propose a group kernel-free PU classifier with asymmetric loss (GKF-PUAL) to achieve quadratic PU classification with group-lasso regularisation embedded for variable selection. We also propose a five-block algorithm to solve the optimization problem of GKF-PUAL. Our experimental results reveal the superiority of GKF-PUAL in both PU classification and variable selection, improving the baseline PUAL by more than 10% in F1-score across four benchmark datasets and removing over 70% of irrelevant variables on six benchmark datasets. The code for GKF-PUAL is at https://github.com/tkks22123/GKF-PUAL . • We propose a group kernel-free PU classifier (GKF-PUAL) with variable selection. • We propose a five-block algorithm for optimization of GKF-PUAL. • Experimental results verify the superiority of GKF-PUAL.