{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T12:00:07Z","timestamp":1763553607459,"version":"3.45.0"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Magnetic Resonance Imaging (MRI) reconstruction is computationally heavy from under sampled data, and centralized data sharing within deep learning models was met with privacy concerns. We therefore propose FL-Swarm MRCM, a novel federated learning framework that integrates FedDyn dynamic regularization, a swarm-optimized generative adversarial network (SwarmGAN), and a structure-aware cross-entropy loss to enhance cross-site MRI reconstruction without sharing raw data. The framework avoids client drift, locally adapts hyper-parameters using Particle Swarm Optimization, and preserves anatomic fidelity. Evaluations on fastMRI, BraTS-2020, and OASIS datasets under non-IID partitions show that FL-Swarm MRCM improves reconstruction quality, achieving PSNR = 29.78 dB and SSIM = 0.984, outscoring FL-MR and FL-MRCM baselines. The federated framework for adversarial training proposed here enables reproducible, privacy-preserving, and strongly multi-institutional MRI reconstruction with better cross-site generalization for clinical use.<\/jats:p>","DOI":"10.3390\/bdcc9110295","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T11:17:27Z","timestamp":1763551047000},"page":"295","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FL-Swarm MRCM: A Novel Federated Learning Framework for Cross-Site Medical Image Reconstruction"],"prefix":"10.3390","volume":"9","author":[{"given":"Ailya","family":"Izhar","sequence":"first","affiliation":[{"name":"Department of Software Engineering, University of Engineering and Technology, Taxila 47050, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syed Muhammad","family":"Anwar","sequence":"additional","affiliation":[{"name":"Shiekh Zayed Institute for Pediatric Surgical Innovation, Children\u2019s National Hospital, Washington, DC 20010, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"da Silva, F.R., Camacho, R., and Tavares, J.M.R.S. 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