Hierarchical Semi-Supervised Federated Learning Method for Dermatosis Diagnosis

AI-aided dermatological diagnosis models require large amounts of high-quality data for effective training. While mobile devices make data collection easier, the lack of professional annotations and privacy concerns hinder their use in model training. Semi-supervised Federated Learning allows for collaborative training on both labeled and unlabeled data while protecting privacy. However, existing pseudo-labeling methods struggle with non-IID dermatosis data from different clients. To address this, we propose a multi-stage hierarchical semi-supervised federated learning framework (HSSFL), enabling labeled clients to assist unlabeled ones in generating pseudo-labels and maximizing data use. HSSFL consists of three stages: federated client clustering, iterative self-training, and opportunistic fine-tuning. Using correlation awareness for federated client clustering, followed by an ally-prioritized approach for self-training, enhances the reliability of pseudo-labels. Opportunistic fine-tuning further minimizes label errors. Experiments on both general and real-world dermatological datasets demonstrate that HSSFL outperforms state-of-the-art semi-supervised federated learning methods.