Accurate　MRI　reconstruction　from　undersampled　k-space　data　is　essential　in　medical　imaging.　Still,　it　is　often　dependent　on　conditional　models　closely　tied　to　specific　imaging　operators,　which　limits　their　adaptability　to　different　imaging　protocols　and　equipment.　This　dependence　leads　to　suboptimal　performance　under　varying　conditions.　Centralized　approaches　also　pose　data　privacy　concerns,　as　they　require　data　sharing　across　institutions.　To　address　these　challenges,　we　introduce　FedGraphMRI-Net,　a　federated　learning　framework　specifically　designed　for　MRI　reconstruction　in　non-Independent　and　Identically　Distributed　(non-IID)　settings.　Our　approach　leverages　graph-based　neural　networks　to　capture　both　local　and　global　anatomical　correlations,　ensuring　patient　privacy　and　adaptability　to　diverse,　site-specific　data　distributions.　FedGraphMRI-Net　employs　a　graph　clustering　strategy　via　the　Louvain　algorithm　to　partition　global　MRI　data　into　sub-graphs,　each　representing　localized　anatomical　features　and　spatial　relationships.　Experimental　results　demonstrate　that　FedGraphMRI-Net　achieves　superior　MRI　reconstruction　performance,　obtaining　PSNR　scores　of　43.8　±　1.1,　44.1　±　1.0,　and　45.0　±　1.1　dB,　and　SSIM　values　of　98.5　±　0.2　%,　98.3　±　0.2　%,　and　98.8　±　0.1　%　for　T1,　T2,　and　PD-weighted　scans　on　the　IXI　dataset.　On　the　fastMRI　dataset　with　4x　acceleration,　the　model　achieved　PSNR　scores　of　42.0　±　1.5,　40.8　±　1.4,　and　43.2　±　1.6　dB,　along　with　SSIM　values　of　98.5　±　0.4　%,　97.9　±　0.3　%,　and　98.8　±　0.1　%　for　T1,　T2,　and　FLAIR　scans.　FedGraphMRI-Net　outperforms　state-of-the-art　models　in　cross-domain　generalization　and　high　acceleration　scenarios,　offering　a　robust,　scalable,　and　privacy-preserving　MRI　reconstruction　solution　for　varied　clinical　environments.　©　2024　Elsevier　Ltd