Federated Learning (FL) is a machine learning approach that has gained considerable interest over the years. FL allows global models to train without compromising the data privacy of the clients' training datasets by sending the global model to each client to learn the weights and propagating only the learned weights back to a central location. However, it is not without limitations as several challenges hinder the model's performance. One of those challenges is the presence of non-IID (Independent and Identically Distributed) properties in the training data. Most real-world data is non-IID, and this imbalance in data distribution has been shown to significantly affect the model's performance. To address this issue, we propose a generative federated learning by pre-training the global model on synthetic data created by a generative model that follows the collective distribution of all clients' training datasets. Our research shows that this approach bridges the performance gap between IID and non-IID in FL, except for certain extreme non-IID cases.