TensorFlow Federated (TFF) enables developers to train machine learning models on data that remains distributed across clients, without centralizing sensitive data. It provides high-level APIs for applying federated training and evaluation to existing TensorFlow models, as well as lower-level interfaces for implementing novel federated algorithms. TFF includes a simulation runtime for experimenting with federated computations.