Title: Generative Adversarial Federated Model. Abstract: As an emerging technique, vertical federated learning collaborates with different data sources to jointly train a machine learning model without data exchange. However, federated learning is computationally expensive and inefficient in modeling due to complex encryption algorithms or secure computation protocols. Split learning offers a solution to the high computational cost and low modeling efficiency of traditional federated learning using encryption algorithms or secure computation protocols. However, vanilla split learning still suffers privacy leakage, especially the label leakage from the active party. Here, we propose the Generative Adversarial Federated Model (GAFM) built upon the vanilla split learning framework and the Generative Adversarial Network (GAN) for improved label privacy protection against commonly used attacks. In our empirical studies on two publicly available datasets, GAFM showed significant performance improvement for prediction and label privacy protection compared to existing models, including Marvell and SplitNN, which is an application of split learning to neural networks. We provide intuition on why GAFM can improve over SplitNN and Marvell and demonstrate that GAFM offers label protection through gradient perturbation compared to SplitNN. 