TensorFlow Federated
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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.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2021-04-22 UTC.
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