Federated Machine Learning (FedML) is a distributed machine learning approach which enables training on decentralised data. A server coordinates a network of nodes, each of which has local, private training data. The nodes contribute to the construction of a global model by training on local data , and the server combines non-sensitive node model contributions into the global model. Federated learning addresses fundamental problems of centralized AI such as privacy, ownership, and locality of data. It extends, even disrupts, the centralized AI paradigm in which better algorithms always comes at the cost of collecting more and more sensitive data.