Fix formatting of `README.md` manually.
@@ -6,20 +6,20 @@ 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 framework has initially been developed to facilitate open
-research and experimentation with [Federated Learning]
-(https://ai.googleblog.com/2017/04/federated-learning-collaborative.html),
-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.
-
-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.
+The framework has initially been developed to facilitate open research and
+experimentation with
+[Federated Learning] (https://ai.googleblog.com/2017/04/federated-learning-collaborative.html),
+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.
+
+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.
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
