{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/generative-autoregressive-transformers-for","title":"Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction","arxiv_id":"2502.04521","date":"2025-02-06","proceeding":null,"authors":["Valiyeh A. Nezhad","Gokberk Elmas","Bilal Kabas","Fuat Arslan","Tolga Çukur"],"abstract":"Although learning-based models hold great promise for MRI reconstruction, single-site models built on limited local datasets often suffer from poor generalization. This challenge has spurred interest in collaborative model training on multi-site datasets via federated learning (FL) -- a privacy-preserving framework that aggregates model updates instead of sharing imaging data. Conventional FL aggregates locally trained model weights into a global model, inherently constraining all sites to use a homogeneous model architecture. This rigidity forces sites to compromise on architectures tailored to their compute resources and application-specific needs, making conventional FL unsuitable for model-heterogeneous settings where each site may prefer a distinct architecture. To overcome this limitation, we introduce FedGAT, a novel model-agnostic FL technique based on generative autoregressive transformers. FedGAT decentralizes the training of a global generative prior that learns the distribution of multi-site MR images. For high-fidelity synthesis, we propose a novel site-prompted GAT prior that controllably synthesizes realistic MR images from desired sites via autoregressive prediction across spatial scales. Each site then trains its own reconstruction model -- using an architecture of its choice -- on a hybrid dataset augmenting its local MRI dataset with GAT-generated synthetic MR images emulating datasets from other sites. This hybrid training strategy enables site-specific reconstruction models to generalize more effectively across diverse data distributions while preserving data privacy. Comprehensive experiments on multi-institutional datasets demonstrate that FedGAT enables flexible, model-heterogeneous collaborations and achieves superior within-site and cross-site reconstruction performance compared to state-of-the-art FL baselines.","url_abs":"https://arxiv.org/abs/2502.04521v2","url_pdf":"https://arxiv.org/pdf/2502.04521v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"generative-autoregressive-transformers-for","repo_url":"https://github.com/icon-lab/FedGAT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"federated-learning","task_name":"Federated Learning"},{"task_slug":"mri-reconstruction","task_name":"MRI Reconstruction"},{"task_slug":"privacy-preserving","task_name":"Privacy Preserving"}],"methods":[{"method_slug":"gat","method_name":"GAT"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}