We explored unconditional and conditional Generative Adversarial Networks (GANs) in centralized and decentralized settings. The centralized setting imitates studies on large but highly unbalanced skin lesion dataset, while the decentralized one simulates a more realistic hospital scenario with three institutions. We evaluated models’ performance in terms of fidelity, diversity, speed of training, and predictive ability of classifiers trained on the generated synthetic data. In addition, we provided explainability focused on both global and local features. Calculated distance between real images and their projections in the latent space proved the authenticity of generated samples, which is one of the main concerns in this type of applications. The code for studies is publicly available (https://github.com/aidotse/stylegan2-ada-pytorch).