Federated Learning starts to give a new perspective regarding the applicability of machine learning in real-life scenarios. Its main goal is to train the model while keeping the participants' data in their devices, thus guaranteeing the privacy of their data. One of the main architectures is the Horizontal Federated Learning, which is the most common one implemented. However, the challenges of the model (security attacks, data leakage), led to using some privacy-enhancing elements. Even those have their own trade-offs that make Federated Learning a challenge to implement in real-life scenarios (communication cost, training time). One question may rise up based on the aforementioned challenges: What if there is a hybrid way of implementing the Federated Learning so that we can overcome the challenges of the present implementations and expand its potential? This paper aims to answer this by diving into five hybrid models that use a variety of components for preserving privacy (Differential Privacy combined with Secure Multiparty Computation, blockchain) and will be compared with each other. Based on that, a reader can get an overview of how the hybrid approach affects the evolution of Horizontal Federated Learning.