Magnetic　Resonance　Imaging　(MRI)　is　a　crucial　tool　in　medical　diagnostics,　yet　reconstructing　high-quality　images　from　under-sampled　k-space　data　poses　significant　challenges.　This　study　introduces　Federated　Adversarial　MRI　Enhancement　(FAME),　a　novel　framework　combining　Federated　Learning　(FL)　with　Generative　Adversarial　Networks　(GANs)　to　enhance　MRI　reconstruction　while　maintaining　patient　privacy.　FAME　utilizes　a　hybrid　model　aggregation　strategy　that　dynamically　weights　updates　from　local　generators,　ensuring　a　balanced　contribution　based　on　dataset　size　and　quality.　Each　local　generator　is　trained　on-site-specific　data,　while　a　global　discriminator　evaluates　and　refines　the　aggregated　updates　to　improve　image　quality.　FAME　addresses　key　issues　in　medical　imaging,　including　data　privacy,　model　generalization,　and　robustness,　by　integrating　advanced　GAN　architectures　such　as　multi-scale　convolutions,　attention　mechanisms,　and　Graph　Neural　Networks　(GNNs).　Differential　privacy　and　secure　aggregation　protocols　are　implemented　to　protect　sensitive　data　during　training.　Extensive　experiments　using　the　fastMRI　Brain　and　Knee　datasets,　along　with　the　BraTS　2020　and　IXI　dataset,　show　that　FAME　outperforms　existing　models,　achieving　superior　PSNR　and　SSIM　values.　This　decentralized　framework　offers　scalable,　privacy-preserving　MRI　reconstruction,　making　it　a　promising　solution　for　diverse　clinical　applications.