# TensorFlow Federated

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The TensorFlow Federated (TFF) platform consists of two layers:

- [Federated Learning (FL)](https://www.tensorflow.org/federated/federated_learning), 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)](https://www.tensorflow.org/federated/federated_core), 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](https://www.tensorflow.org/federated/tutorials/tutorials_overview) that walk you
through the main TFF concepts and APIs using practical examples. Make sure to
follow the [installation instructions](https://www.tensorflow.org/federated/install) 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.