Populates the main landing page with proper introductory content.
@@ -8,13 +8,33 @@ landing_page:
items:
- classname: devsite-landing-row-50
description: >
+ <!-- Please keep the content of this file in sync with README.md -->
<p>TensorFlow Federated (TFF) is an open-source framework for collaborative computations
- on distributed data that does not require collecting data at a centralized location. The
- platform consists of two layers.</p>
+ on distributed data that does not require collecting data at a centralized location.</p>
+ <p>The framework has initially been developed to facilitate open research and
+ experimentation with
+ <a href="https://ai.googleblog.com/2017/04/federated-learning-collaborative.html">
+ Federated Learning</a>, a technology that enables devices owned by end users to
+ collaboratively learn a shared prediction model while keeping potentially sensitive
+ training data on the devices, thus decoupling the ability to do machine learning from the
+ need to collect and store the data in the cloud.</p>
+ <p>With the interfaces provided by TFF, developers can test existing federated learning
+ algorithms on their models and data, or design new experimental algorithms and run them on
+ existing models and data, all within the same open source environment. The framework has
+ been designed with compositionality in mind, and can be used to combine
+ independently-developed techniques and components that offer complementary capabilities into
+ larger systems.</p>
+ <p>Beyond this, TFF also provides a set of building blocks that can be used to implement a
+ variety of custom non-learning algorithms, such as analytics over sensitive distributed
+ on-device data.</p>
+ <p>The platform consists of two layers:</p>
list:
- heading: Federated Learning (FL)
description: >
- Aaa.... something about FL
+ This layer offers a set of high-level interfaces that allow users to plug existing Keras
+ or non-Keras machine learning models into the framework, and perform basic tasks, such as
+ federated training or evaluation, without having to study the details of federated
+ learning algorithms.
path: /federated/federated_learning
icon:
icon_name: chevron_right
@@ -22,7 +42,10 @@ landing_page:
background: grey
- heading: Federated Core (FC)
description: >
- Aaa.... something about FC
+ This layer offers a set of lower-level interfaces that allow users to concisely express
+ their own custom federated algorithms by combining TensorFlow with distributed
+ communication operators within a strongly-typed functional programming environment. This
+ layer also serves as a foundation upon which we've built the Federated Learning layer.
path: /federated/federated_core
icon:
icon_name: chevron_right
