The ability of machine learning and deep learning models to learn from data has led to their widespread adoption in a number of real-world settings today. To cite some, object recognition, autonomous driving systems, semantic segmentation and natural language generation are just a few examples of tasks that can be tackled by machine and deep learning. The typical "centralized" strategy is based on learning a model using collected sample data with the goal of generalizing to unseen data. Although this method produces excellent results, it has a fundamental flaw: in many real-world situations, gathering the necessary data is not trivial since more and more data is becoming protected by privacy regulations, making it inaccessible.