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To address these, we propose FUSION (Federated Unified Semi\u2010Supervised Optimisation Network), a novel dual\u2010path training framework that integrates both Federated Labelled Data Learning (FLDL) and Federated Unlabelled Data Training (FUDT). Central to FUSION is a two\u2010stage pseudo\u2010label refinement strategy designed to ensure robustness under real\u2010world federated constraints. First, synthetic label denoising is performed using Monte Carlo dropout\u2010based uncertainty estimation, enabling clients to identify and exclude low\u2010confidence predictions. Second, prototype\u2010based correction is applied to further refine pseudo\u2010labels by aligning them with class\u2010specific feature centroids, mitigating errors caused by domain shifts and inter\u2010client variability. These refined labels are used for localised training on unlabelled clients, while a dynamic aggregation scheme modulated by a reliability\u2010based hyperparameter \u03bc adjusts the influence of labelled versus unlabelled clients during global model updates. This tightly coupled interaction between pseudo\u2010label quality and federated optimisation ensures stability, accelerates convergence, and enhances generalisation across heterogeneous clients. FUSION is evaluated on three diverse datasets: TCGA\u2010LGG (brain MRI), Kvasir\u2010SEG (colonoscopy), and UDIAT (ultrasound) and consistently outperforms state\u2010of\u2010the\u2010art 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\u2010preserving, and label\u2010efficient solution for real\u2010world multi\u2010institutional medical image segmentation tasks.<\/jats:p>","DOI":"10.1049\/ipr2.70147","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T07:31:17Z","timestamp":1753169477000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["FUSION: Uncertainty\u2010Guided Federated Semi\u2010Supervised Learning for Medical Image Segmentation"],"prefix":"10.1049","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1140-3230","authenticated-orcid":false,"given":"Abdul","family":"Raheem","sequence":"first","affiliation":[{"name":"College of Computer Science Beijing University of Technology  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science Beijing University of Technology  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyang","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Computer Science Beijing University of Technology  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Malik Abdul","family":"Manan","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fahad","family":"Sabah","sequence":"additional","affiliation":[{"name":"College of Computer Science Beijing University of Technology  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shahzad","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746\u2010019\u20100111\u20103"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746\u2010020\u201000314\u20102"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746\u2010021\u201000412\u20109"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1049\/htl.2019.0068"},{"key":"e_1_2_10_6_1","doi-asserted-by":"crossref","unstructured":"H.Gao X.Xiao L.Qiu M. 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