Quickstart JAX#

This tutorial will show you how to use Flower to build a federated version of an existing JAX workload. We are using JAX to train a linear regression model on a scikit-learn dataset. We will structure the example similar to our PyTorch - From Centralized To Federated walkthrough. First, we build a centralized training approach based on the Linear Regression with JAX tutorial`. Then, we build upon the centralized training code to run the training in a federated fashion.

Before we start building our JAX example, we need install the packages jax, jaxlib, scikit-learn, and flwr:

$ pip install jax jaxlib scikit-learn flwr

Linear Regression with JAX#

We begin with a brief description of the centralized training code based on a Linear Regression model. If you want a more in-depth explanation of what’s going on then have a look at the official JAX documentation.

Let’s create a new file called jax_training.py with all the components required for a traditional (centralized) linear regression training. First, the JAX packages jax and jaxlib need to be imported. In addition, we need to import sklearn since we use make_regression for the dataset and train_test_split to split the dataset into a training and test set. You can see that we do not yet import the flwr package for federated learning. This will be done later.

from typing import Dict, List, Tuple, Callable
import jax
import jax.numpy as jnp
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split

key = jax.random.PRNGKey(0)

The load_data() function loads the mentioned training and test sets.

def load_data() -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray], List[np.ndarray]]:
    # create our dataset and start with similar datasets for different clients
    X, y = make_regression(n_features=3, random_state=0)
    X, X_test, y, y_test = train_test_split(X, y)
    return X, y, X_test, y_test

The model architecture (a very simple Linear Regression model) is defined in load_model().

def load_model(model_shape) -> Dict:
    # model weights
    params = {
        'b' : jax.random.uniform(key),
        'w' : jax.random.uniform(key, model_shape)
    return params

We now need to define the training (function train()), which loops over the training set and measures the loss (function loss_fn()) for each batch of training examples. The loss function is separate since JAX takes derivatives with a grad() function (defined in the main() function and called in train()).

def loss_fn(params, X, y) -> Callable:
    err = jnp.dot(X, params['w']) + params['b'] - y
    return jnp.mean(jnp.square(err))  # mse

def train(params, grad_fn, X, y) -> Tuple[np.array, float, int]:
    num_examples = X.shape[0]
    for epochs in range(10):
        grads = grad_fn(params, X, y)
        params = jax.tree_multimap(lambda p, g: p - 0.05 * g, params, grads)
        loss = loss_fn(params,X, y)
        # if epochs % 10 == 9:
        #     print(f'For Epoch {epochs} loss {loss}')
    return params, loss, num_examples

The evaluation of the model is defined in the function evaluation(). The function takes all test examples and measures the loss of the linear regression model.

def evaluation(params, grad_fn, X_test, y_test) -> Tuple[float, int]:
    num_examples = X_test.shape[0]
    err_test = loss_fn(params, X_test, y_test)
    loss_test = jnp.mean(jnp.square(err_test))
    # print(f'Test loss {loss_test}')
    return loss_test, num_examples

Having defined the data loading, model architecture, training, and evaluation we can put everything together and train our model using JAX. As already mentioned, the jax.grad() function is defined in main() and passed to train().

def main():
    X, y, X_test, y_test = load_data()
    model_shape = X.shape[1:]
    grad_fn = jax.grad(loss_fn)
    print("Model Shape", model_shape)
    params = load_model(model_shape)
    params, loss, num_examples = train(params, grad_fn, X, y)
    evaluation(params, grad_fn, X_test, y_test)

if __name__ == "__main__":

You can now run your (centralized) JAX linear regression workload:

python3 jax_training.py

So far this should all look fairly familiar if you’ve used JAX before. Let’s take the next step and use what we’ve built to create a simple federated learning system consisting of one server and two clients.

JAX meets Flower#

The concept of federating an existing workload is always the same and easy to understand. We have to start a server and then use the code in jax_training.py for the clients that are connected to the server. The server sends model parameters to the clients. The clients run the training and update the parameters. The updated parameters are sent back to the server, which averages all received parameter updates. This describes one round of the federated learning process, and we repeat this for multiple rounds.

Our example consists of one server and two clients. Let’s set up server.py first. The server needs to import the Flower package flwr. Next, we use the start_server function to start a server and tell it to perform three rounds of federated learning.

import flwr as fl

if __name__ == "__main__":
    fl.server.start_server(server_address="", config=fl.server.ServerConfig(num_rounds=3))

We can already start the server:

python3 server.py

Finally, we will define our client logic in client.py and build upon the previously defined JAX training in jax_training.py. Our client needs to import flwr, but also jax and jaxlib to update the parameters on our JAX model:

from typing import Dict, List, Callable, Tuple

import flwr as fl
import numpy as np
import jax
import jax.numpy as jnp

import jax_training

Implementing a Flower client basically means implementing a subclass of either flwr.client.Client or flwr.client.NumPyClient. Our implementation will be based on flwr.client.NumPyClient and we’ll call it FlowerClient. NumPyClient is slightly easier to implement than Client if you use a framework with good NumPy interoperability (like JAX) because it avoids some of the boilerplate that would otherwise be necessary. FlowerClient needs to implement four methods, two methods for getting/setting model parameters, one method for training the model, and one method for testing the model:

  1. set_parameters (optional)
    • set the model parameters on the local model that are received from the server

    • transform parameters to NumPy ndarray’s

    • loop over the list of model parameters received as NumPy ndarray’s (think list of neural network layers)

  2. get_parameters
    • get the model parameters and return them as a list of NumPy ndarray’s (which is what flwr.client.NumPyClient expects)

  3. fit
    • update the parameters of the local model with the parameters received from the server

    • train the model on the local training set

    • get the updated local model parameters and return them to the server

  4. evaluate
    • update the parameters of the local model with the parameters received from the server

    • evaluate the updated model on the local test set

    • return the local loss to the server

The challenging part is to transform the JAX model parameters from DeviceArray to NumPy ndarray to make them compatible with NumPyClient.

The two NumPyClient methods fit and evaluate make use of the functions train() and evaluate() previously defined in jax_training.py. So what we really do here is we tell Flower through our NumPyClient subclass which of our already defined functions to call for training and evaluation. We included type annotations to give you a better understanding of the data types that get passed around.

class FlowerClient(fl.client.NumPyClient):
    """Flower client implementing using linear regression and JAX."""

    def __init__(
        params: Dict,
        grad_fn: Callable,
        train_x: List[np.ndarray],
        train_y: List[np.ndarray],
        test_x: List[np.ndarray],
        test_y: List[np.ndarray],
    ) -> None:
        self.params= params
        self.grad_fn = grad_fn
        self.train_x = train_x
        self.train_y = train_y
        self.test_x = test_x
        self.test_y = test_y

    def get_parameters(self, config) -> Dict:
        # Return model parameters as a list of NumPy ndarrays
        parameter_value = []
        for _, val in self.params.items():
        return parameter_value

    def set_parameters(self, parameters: List[np.ndarray]) -> Dict:
        # Collect model parameters and update the parameters of the local model
        params_item = list(zip(self.params.keys(),parameters))
        for item in params_item:
            key = item[0]
            value = item[1]
            self.params[key] = value
        return self.params

    def fit(
        self, parameters: List[np.ndarray], config: Dict
    ) -> Tuple[List[np.ndarray], int, Dict]:
        # Set model parameters, train model, return updated model parameters
        print("Start local training")
        self.params = self.set_parameters(parameters)
        self.params, loss, num_examples = jax_training.train(self.params, self.grad_fn, self.train_x, self.train_y)
        results = {"loss": float(loss)}
        print("Training results", results)
        return self.get_parameters(config={}), num_examples, results

    def evaluate(
        self, parameters: List[np.ndarray], config: Dict
    ) -> Tuple[float, int, Dict]:
        # Set model parameters, evaluate the model on a local test dataset, return result
        print("Start evaluation")
        self.params = self.set_parameters(parameters)
        loss, num_examples = jax_training.evaluation(self.params,self.grad_fn, self.test_x, self.test_y)
        print("Evaluation accuracy & loss", loss)
        return (
            {"loss": float(loss)},

Having defined the federation process, we can run it.

def main() -> None:
    """Load data, start MNISTClient."""

    # Load data
    train_x, train_y, test_x, test_y = jax_training.load_data()
    grad_fn = jax.grad(jax_training.loss_fn)

    # Load model (from centralized training) and initialize parameters
    model_shape = train_x.shape[1:]
    params = jax_training.load_model(model_shape)

    # Start Flower client
    client = FlowerClient(params, grad_fn, train_x, train_y, test_x, test_y)
    fl.client.start_client(server_address="", client=client.to_client())

if __name__ == "__main__":

And that’s it. You can now open two additional terminal windows and run

python3 client.py

in each window (make sure that the server is still running before you do so) and see your JAX project run federated learning across two clients. Congratulations!

Next Steps#

The source code of this example was improved over time and can be found here: Quickstart JAX. Our example is somewhat over-simplified because both clients load the same dataset.

You’re now prepared to explore this topic further. How about using a more sophisticated model or using a different dataset? How about adding more clients?