An exploration of Privacy Preserving Federated Learning (with code)

Machine Learning, while being a very powerful technology, appears to know some drawbacks. For example the huge need for data and the possible attacks over a neural networks, discussed later, are hopefully to be answered with some Privacy Preserving Federated Learning. First of all, let's quickly discuss what Federated Learning is. Today, machine learning is widely used by anyone who wants to understand their dataset deeper and model some classification/regression tasks. However, to achieve the best model possible, one needs a lot of data, which may be difficult to gather in one place.