FUSION: Uncertainty‐Guided Federated Semi‐Supervised Learning for Medical Image Segmentation

ABSTRACT Federated learning (FL) for medical image segmentation poses critical challenges, including non‐IID data distributions, limited access to labelled annotations, and stringent privacy constraints across institutions. To address these, we propose FUSION (Federated Unified Semi‐Supervised Optimisation Network), a novel dual‐path training framework that integrates both Federated Labelled Data Learning (FLDL) and Federated Unlabelled Data Training (FUDT). Central to FUSION is a two‐stage pseudo‐label refinement strategy designed to ensure robustness under real‐world federated constraints. First, synthetic label denoising is performed using Monte Carlo dropout‐based uncertainty estimation, enabling clients to identify and exclude low‐confidence predictions. Second, prototype‐based correction is applied to further refine pseudo‐labels by aligning them with class‐specific feature centroids, mitigating errors caused by domain shifts and inter‐client variability. These refined labels are used for localised training on unlabelled clients, while a dynamic aggregation scheme modulated by a reliability‐based hyperparameter μ adjusts the influence of labelled versus unlabelled clients during global model updates. This tightly coupled interaction between pseudo‐label quality and federated optimisation ensures stability, accelerates convergence, and enhances generalisation across heterogeneous clients. FUSION is evaluated on three diverse datasets: TCGA‐LGG (brain MRI), Kvasir‐SEG (colonoscopy), and UDIAT (ultrasound) and consistently outperforms state‐of‐the‐art FL models in Dice, IoU, HD95, and ASD metrics. Results confirm the critical role of synthetic label refinement in enhancing segmentation accuracy, boundary precision, and model scalability. FUSION provides a technically grounded, privacy‐preserving, and label‐efficient solution for real‐world multi‐institutional medical image segmentation tasks.