The TensorFlow Federated (TFF) platform consists of two layers:
- Federated Learning (FL), high-level interfaces to plug existing Keras or non-Keras machine learning models into the TFF framework. You can perform basic tasks, such as federated training or evaluation, without having to study the details of federated learning algorithms.
- Federated Core (FC), lower-level interfaces to concisely express custom federated algorithms by combining TensorFlow with distributed communication operators within a strongly-typed functional programming environment.
Start with the TFF tutorials that walk you through the main TFF concepts and APIs using practical examples. Make sure to follow the installation instructions to configure your environment for use with TFF.
The more detailed guides (see the left sidebar of this page) then provide reference information on important topics.