A Unified Framework for Federated Semi-Supervised Learning in Heterogeneous IoT Healthcare Systems

We consider a federated semi-supervised learning (FSSL) scenario in heterogeneous Internet of Things (IoT) healthcare systems where only a tiny fraction of IoT healthcare devices possess labels. The challenge is that most IoT devices need label information and exhibit heterogeneous data distributions. This article proposes a unified FSSL framework (FSSL-HD) tailored for IoT healthcare systems with heterogeneous distributions. The FSSL-HD framework introduces a novel objective function for unlabeled healthcare devices to enhance generalization performance and reduce intermodel discrepancies. A straightforward loss-based aggregation mechanism is designed to reinforce distillation from labeled healthcare devices to supervise unlabeled samples. Multiple iterations for labeled devices and dynamically adjusted confidence thresholds are proposed to improve the model performance. Our theoretical analysis of the generalization error in FSSL suggests directions for enhancing performance by improving the self-training capabilities of unlabeled devices and reinforcing the distillation and transfer of supervision signals from labeled devices. Our empirical experiments on the federated benchmark data sets and the medical image data sets show that FSSL-HD surpasses the performance of state-of-the-art methods. The code is available at https://github.com/baoshengli96/FSSL-HD.