Mobile phones and wearable devices are used and carried everywhere by people, producing every day a huge amount of distributed and sensitive data. Standard machine learning techniques need to collect this kind of data all on the same machine to compute the training and obtain a model useful to make predictions. To better preserve the privacy of data and, at the same time, guarantee a comparable performance, Federated and Split Learning have been designed. This work tries to improve the efficiency and privacy of Split and Federated learning combining them in two different types of architectures using the PySyft library implemented inside PyTorch.