MedSynGAN addresses the challenges posed by data scarcity, diversity, and privacy in medical imaging, mainly when generating synthetic chest X-ray images. The proposed system, Federated Generative Adversarial Network (MedSynGAN), relies on a decentralized approach wherein a model can be trained at multiple healthcare settings with patient data kept strictly private to those sites. One of the significant advantages of this solution lies in its ability to integrate federated learning with the advanced architectures of GAN, such as DCGAN and ProGAN, for enhanced quality and stability of images. The MedSynGAN system offers improved model robustness and several other advantages such as performing diverse dataset learning without compromising sensitive information. Performance is measured using metrics such as Fréchet Inception Distance (FID), with some of the results reported reaching scores as low as 26.71 for high-quality images with structural similarity indices reaching 0.85 and peak signal-to-noise ratios above 51.29 to ensure that fidelity is high with applicability in a clinical setting. The synthetic images generated by this system show promise for augmenting training datasets for various diagnostic tasks such as pneumonia detection or nodule classification, potentially addressing data imbalance issues in medical image analysis. This research has tremendous applications in real-world healthcare because it caters to medical research and AI training needs while maintaining the critical privacy of patients’ data.