Magnetic Resonance Imaging (MRI) is a crucial medical imaging technique for diagnosing various health conditions. However, the reconstruction of high quality MRI images is computationally intensive and often requires extensive data sharing, posing challenges in terms of privacy and security. Federated learning has emerged as a promising approach to address these concerns by enabling collaborative model training across multiple decentralized institutions without sharing sensitive patient data. In this paper, we propose a new ap proach for reconstructing MRI using federated learning, aiming to achieve both high reconstruction accuracy and data privacy preservation. First we will cre ate a locally dual conditional generative adversarial network to generate a high quality MRI synthesized using latent variables and noise via conditional model that is then aggregated to a server as global generator to generalize images across multi institutions without sharing the data to ensure privacy-preserving. Finally experiments on different datasets to compare our model performance with other available methods.