Updated March 15, 2023
Introduction to TensorFlow Federated
Tensorflow federated is the framework we can use for computing data, a decentralized and machine learning domain. Moreover, this framework is completely open sourced. In short, It is also denoted as TTF, which is developed keeping in mind the exploration and research of federated learning to perform all sorts of machine learning experiments.
This article will try to understand tensorflow federated, how we can use it, its Model, characteristics, computation API, and finally conclude our view.
What is TensorFlow federated?
The framework helps you perform machine learning on completely decentralized data. We train the models that are shared globally and include the clients participating in placing their data for training locally. One of the examples which will be helpful to understand where we make the use of tensorflow federation is for the training of keyboard prediction model on mobile phones and making sure at the same time that the sensitive and secured data is not being uploaded on the data server.
The developers can use and include Federated learning algorithms in their data and models. At the same time, the novel algorithms are available and open for any experimentation for the developers. Therefore, the people performing the research on this can find ample examples and the point where they can start for various experiment topics. Federated analytics is the computation that is non-learning based and can be implemented using the interface of tensorflow federated.
How and where to use TensorFlow federated?
We can make the use of federated learning in various ways that include –
- By using FC API, design and create new federated learning algorithms.
- Assisting the development and optimization of computation structures that are generated.
- Apply the APIs of the federated learning to the models of TensorFlow that exist currently.
- Integrate the Tensorflow Federated framework with other environments of development.
You can make use of it by following the below steps –
- Installation of TFF –
This can be done by opening the terminal or command prompt and typing in the following command for execution –
pip install tensorflow-federated –upgrade
- Create a notebook and import the package and other dependencies.
The most basic dependencies are tensorflow and tensorflow_federated packages that must be imported.
- Prepare the dataset for simulation.
The data should be of NIST or MNIST format and is by default provided when you go for creating a leaf project.
- Make the use of federated data to train the Model.
After that, you can train the Model and make it aware of various functionalities that it should perform and be aware as you do with any of the TensorFlow models.
- Print the summarized information about the implementation of tensorflow federated.
Finally, you can print out the machine learning tensorflow federated model results.
TensorFlow federated Model
The two models used in TensorFlow federated FL API are tff.learning.Model and create_keras_model().
The tff.learning.Model works on a particular supplied set of variables of tensorflow. Each of the individual methods is responsible for computing the tf.function. From the perspective of python, the class should be completely stateless. A particular method can be traced only once along with its arguments to create the graph functions of tensorflow.
The tensorflow variables can be of the following – Local variables or weights. Local variables are used for accumulating the metrics of aggregation for calling the forward pass, while weights are the ones that are required for predictions.
The learning model has various attributes: input specification, federated output computation, non-trainable, trainable, and local variables. In addition, this model mModel’sinclude forward pass and report local outputs. For a complete reference to this Model, yoModel, refer to the documentation of the official website link.
The model of tf.keras.Model() is a collection of layers in a single object that helps inference and training features. The arguments that need to be passed to it include inputs, outputs, and the Model’s nModel’so methods of this Model areModel frequently used, which include a summary and get_layer methods.
TensorFlow federated characteristics
The main characteristics are listed below –
- Effort saving – Whenever any developer approaches to create a learning system of federated, the pain points where the developers mostly face the problem are targeted here, and the platform of tensorflow federated is designed keeping the mitigations of those points in mind for the convenience of developers. The challenges faced by most of the developers include local and global communication perspectives, logic interleaving of various types, and execution and construction order tension.
- Architecture agnostic – It can compile the whole code and provide the representation of the same in an abstract way, which facilitates the developer to deploy its Model acrModel diverse environment.
- Availability of many extensions – Quantization, compression, and differential privacy are some of the extensions available in Tensorflow Federated.
TensorFlow federated Computation API
There are two types of computation APIs, which are described below –
- Federated Core API, also known as FC –
The low-level interface used at the system’s core part is included in this API. Federated algorithms can be concisely expressed along with the combination of TensorFlow using this API. It also consists of a functional programming environment that is typed strongly and includes the distributed operators for communication. This API layer is the base over which we have created the building of federated learning.
- Federated Learning API, referred to as FL –
The developers can include the evaluation and federated training models to the existing models of TensorFlow by using the high-level interfaces provided in this federated learning API layer.
Developers can use Tensorflow federated to express the federated computations in a declarative manner. This leads to deploying their models to a diverse environment in runtime. In addition, Multi-machine simulation, which has great performance, is also included in it.
This is a guide to TensorFlow Federated. Here we discuss the Introduction, How and where to use TensorFlow federated, and Examples with code implementation. You may also have a look at the following articles to learn more –