How can we train machine learning models using distributed sensitive data? What if we could take advantage of the same properties of crowdsourcing to solve important machine learning problems? That is the idea behind Federated Learning. Federated Learning is a machine learning framework that allows data scientists to train statistical models using sensitive data from users, without uploading user data to servers. It is distributed training technique where training and testing occur locally (at the edge) and only the meta-information is sent to a central server that combines multiple model updates (from multiple clients) into a new model.