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TensorFlow for Mobile and IoT and TensorFlow.js
Each device runs the local training and sends a model update back to the global
services. Note that each device has an FL runtime providing federated learning
services to an app process that stores data in a local example store. The FL runtime
fetches the training examples from the example store:
Figure 11: An example of federated learning architecture
TensorFlow FL APIs
The TensorFlow Federated (TTF) platform has two layers:
• Federated learning (FL), a high-level interface that works well with
tf.keras and non tf.keras models. In the majority of situations you will
use this API for distributed training that is privacy preserving.
• Federated core (FC), a low-level interface that is highly customizable and
allows you to interact with low level communications and with federated
algorithms. You will need this API only if you intend to implement new and
sophisticated distributed learning algorithms. This topic is rather advanced,
and we are not going to cover it in this book. If you wish to learn more,
you can find more information online (https://www.tensorflow.org/
federated/federated_core).
The FL API has three key parts:
1. Models: Used to wrap existing models for enabling federating learning.
This can be achieved via the tff.learning.from_keras_model(), or via
subclassing of tff.learning.Model(). For instance, you can have the
following code fragment:
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