Centralized machine learning involves having the model and the dataset on the same device. Companies such as Google upload data to the cloud to train their machine learning models.
Federated Learning flips this paradigm. Instead of transfering data to the cloud, we send cloud-based models to our devices. These models are then trained locally on our devices.
Once we have trained these models locally, the updated models are sent to the server instead of data. The server checks these models and then updates the global model on the cloud. This process is referred to as
TensorFlow Federated is an open-source framework by Google that is used to implement Federated Learning.
In this article, we will learn how TensorFlow Federated can be utilized by researchers and machine learning experts to implement federated learning on datasets.
To understand the contents of this article, you need to be familiar with:
- The Python programming language.
- The TensorFlow machine learning framework.
- Federated Learning.
- NIST dataset
- Introducing TensorFlow Federated (TFF)
- The code behind TensorFlow Federated (TFF)
- Wrapping up
- Additional resources
Introduction to TensorFlow Federated (TFF)
TFF is an open-source framework for Federated learning performed on decentralized data. It is spearheaded by Google and has gained popularity in the recent years.
TFF has three main features:
- TFF is architecture-agnostic.
This means that it can compile all code into an abstract representation. As a result, it can be deployed in a diverse environment.
- TFF saves effort.
It is designed to mitigate the pain points that we developers face when developing federated learning systems.
Some of these challenges include interleaving the different types of logic, the global vs local perspective on communication, and tension between the order of construction vs execution.
- TFF has many extensions.
Some of the available extensions include differential privacy, compression, and quantization.
TensorFlow federated layers
TFF offers two main layers:
Federated Learning (FL) API The FL API is a high-level API that implements federated training and evaluation. It can be applied to existing TensorFlow models or data.
Federated Core (FC) API FC is a low level framework below the Federated Learning API. This API provides generic expressions to run and simulate custom types of computations, as well as control your own orchestrations. It also has a local runtime that supports simulations.
In this tutorial, we will focus on the FL API and the code behind it.
Application of Federated learning
There are different ways you can get involved depending on your interest:
A machine learning developer can apply Federated Learning APIs to existing TensorFlow models.
A federated learning researcher can help to design new federated learning algorithms using the FC API.
A systems researcher can assist in optimizing generated computation structures.
A system developer can help in integrating TFF with different development environments.
The code behind TensorFlow Federated (TFF)
First, let’s briefly take a look at how the Keras model looks like:
def create_compiled_keras_model(): model = tf.keras.models.Sequential([ tf.keras.layers.Dense( 10, activation=tf.nn.softmax, kernel_initializer = 'zeros', input_shape = (784, ) ) ]) model.compile( loss=tf.keras.losses.SparseCategoricalCrossentropy(), optimizer=tf.keras.optimizers.SGD(learning_rate=0.02), metrics=[tf.keras.metrics.SparseCategoricalAccuracy()] ) return model
Keras model uses a
Sequential() API as it allows us to create models layer-by-layer. This is ideal for solving simple neural network problems.
However, its not ideal for complex networks that share layers or have many inputs/outputs such as residual and siamese networks.
In that case,
functional APIs are used. The functional API has more flexibility since one can easily define models where layers connect to more than just the previous and next layers.
Refer to the following video to understand these differences in depth:
We will import it into our main function
model_fn using the
def model_fn(): keras_model = create_compiled_keras_model() return tff.learning.from_compiled_keras_model(keras_model, sample_batch)
The above code shows where you will add the Keras model.
state = train.initialize() for _ in range (5): state, metrics = train.next(state, train_data) print (metrics.loss)
In the above code, the
initialize() method retrieves the initial server state. It then calls
train.next which will run our federated training. This includes sending the initial server state to each of the clients.
Each client will run its own local rounds of training and then send an update to the server. The server stores the new aggregated global model produced from the decentralized data.
eval = tff.learning.build_federated_evaluation(model_fn) metrics = eval(state.model, test_data)
Finally, we can perform federated evaluation to understand the state of our trained model. The
build_federated_evaluation() method helps to perform this federated evaluation.
Here’s how the whole code looks like for TFF:
train_data, test_data = tff.simulation.datasets.emnist.load_data() def model_fn(): keras_model = create_keras_model() return tff.learning.from_keras_model(keras_model, sample_batch) train = tff.learning.build_federated_averaging_process(model_fn) state = train.initialize() for _ in range (5): state, metrics = train.next(state, train_data) print (metrics.loss) eval = tff.learning.build_federated_evaluation(model_fn) metrics = eval(state.model, test_data)
In summary, the general components for the FL API include:
- Federated computation builders TFF provides two builder functions:
tff.learning.build_federated_averaging_processgenerates the federated computations for federated training.
tff.learning.build_federated_evaluationgenerates the federated computations for federated evaluation.
Let’s use the MNIST training example to introduce the Federated Learning (FL) API layer of TFF.
Step 1: Installing TensorFlow Federated
Please make sure to install TensorFlow Federated before importing it into your notebook. Failure to do this might result in an error.
We install TensorFlow Federated using the following command:
pip install tensorflow-federated --upgrade
Step 2: Importing dependencies into our notebook
import tensorflow as tf import tensorflow_federated as tff
We’ve imported both
tensorflow federated into our project.
Step 3: Simulation dataset
emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data() def client_data(n): return emnist_train.create_tf_dataset_for_client(source.client_ids[n]).map( lambda e: (tf.reshape(e['pixels'], [-1]), e['label']) ).repeat(10).batch(20)
The simulation dataset used is the federated version of the MNIST dataset called NIST and is provided by the Leaf project. Leaf provides a benchmarking framework for federated learning.
Why a federated version of the dataset?
It’s because the dataset in FL is obtained from multiple users. This poses a unique set of challenges that normal versions of the dataset don’t exhibit.
We import the federated data into the project using the
Step 4: Training using Federated data
train_data = [client_data(n) for n in range(3)] trainer = tff.learning.build_federated_averaging_process( model_fn, client_optimizer_fn=lambda: tf.keras.optimizers.SGD(0.1)) state = trainer.initialize() for _ in range(50): state, metrics = trainer.next(state, train_data) print(metrics['train']['loss'])
In the training bit, you’ll notice that only a subset of client devices are selected to receive the training model. This is because not all devices are eligible. At any given time, only a few devices may have relevant data to solve your problem.
12.931682 13.094639 12.765134 11.813275 11.521152 10.681865 10.033001 ...... ...... ...... 0.038877 0.03537092 0.032601092 0.030380366 0.028545696 0.02700758
In TFF, after the model has been trained on the selected devices, results are obtained and the loss calculated.
In the experiment above, the training loss is decreasing after each round of federated training, indicating that the model is converging.
We’ve set our training to go for 50 rounds. The training loss at the end of the training is
0.02700758 down from
12.931682 recorded at the start of the training.
In realistic situations, users can join and exit the experiment freely. This means that one would randomly select a sample of users for each round. However, to make things simple, and allow the system to converge quickly, we’ll reuse the same users.
Summary of the implementation
Feel free to modify parameters such as batch sizes, number of users, epochs, and learning rates to simulate training on random users.
This was a simple introduction to TensorFlow Federated and the FC API. We used the MNIST training example to introduce the Federated Learning (FL) API layer of TFF.
The code I’ve shown above is open-source and available on Github. You can access it using this link.
Remember, with Federated Learning, we can learn from everyone, without learning about anyone.
- Federated Learning for Mobile Keyboard Prediction
- TensorFlow Federated
- TensorFlow Federated: Machine Learning on Decentralized Data
- An online comic book from GoogleAI to learn Federated learning
- Federated Learning for Image Classification
Peer Review Contributions by: Collins Ayuya
About the authorWillies Ogola
Willies Ogola is pursuing his Master’s in Computer Science in Hubei University of Technology, China. His research direction is on Artificial Intelligence and Embedded Systems. He likes researching during his free time and is passionate about technology.