This research delves into the application of unconditional and conditional Generative Adversarial Networks (GANs) in both centralized and decentralized settings. The centralized approach replicates studies on a large but imbalanced skin lesion dataset, while the decentralized approach emulates a more realistic hospital scenario with data from three institutions. We meticulously assess the models’ performance in terms of fidelity, diversity, training speed, and the predictive capabilities of classifiers trained on synthetic data. Moreover, we delve into the explainability of the models, focusing on both global and local features. Crucially, we validate the authenticity of the generated samples by calculating the distance between real images and their respective projections in the latent space, addressing a key concern in such applications.