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An Introduction to Federated Learning#

Welcome to the federated learning tutorial!

In this notebook, we’ll build a federated learning system using Flower and PyTorch. In part 1, we use PyTorch for the model training pipeline and data loading. In part 2, we continue to federate the PyTorch-based pipeline using Flower.

Part 0: Preparation#

Before we begin with any actual code, let’s make sure that we have everything we need.

Installing dependencies#

Next, we install the necessary packages for PyTorch (torch and torchvision) and Flower (flwr):

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!pip install flwr[simulation] torch torchvision matplotlib

Now that we have the necessary packages installed, we can import everything we need for this tutorial:

[ ]:
from collections import OrderedDict
from typing import List, Tuple

import flwr as fl
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torchvision
import torch.nn.functional as F
import torchvision.transforms as transforms
from flwr.common import Metrics
from import DataLoader, random_split
from torchvision.datasets import CIFAR10

print("flwr", fl.__version__)
print("numpy", np.__version__)
print("torch", torch.__version__)
print("torchvision", torchvision.__version__)

DEVICE = torch.device("cpu")
# DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"Training on {DEVICE}")

It is possible to switch to a runtime that has GPU acceleration enabled (on Google Colab: Runtime > Change runtime type > Hardware acclerator: GPU > Save). Note, however, that Google Colab is not always able to offer GPU acceleration. If you see an error related to GPU availability in one of the following sections, consider switching back to CPU-based execution by setting DEVICE = torch.device("cpu"). If the runtime has GPU acceleration enabled, you should see the output Training on cuda:0, otherwise it’ll say Training on cpu.

Loading the data#

Federated learning can be applied to many different types of tasks across different domains. In this tutorial, we introduce federated learning by training a simple convolutional neural network (CNN) on the popular CIFAR-10 dataset. CIFAR-10 can be used to train image classifiers that distinguish between images from ten different classes:

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CLASSES = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

We simulate having multiple datasets from multiple organizations (also called the “cross-silo” setting in federated learning) by splitting the original CIFAR-10 dataset into multiple partitions. Each partition will represent the data from a single organization. We’re doing this purely for experimentation purposes, in the real world there’s no need for data splitting because each organization already has their own data (so the data is naturally partitioned).

Each organization will act as a client in the federated learning system. So having ten organizations participate in a federation means having ten clients connected to the federated learning server:

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Let’s now load the CIFAR-10 training and test set, partition them into ten smaller datasets (each split into training and validation set), and wrap the resulting partitions by creating a PyTorch DataLoader for each of them:

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def load_datasets():
    # Download and transform CIFAR-10 (train and test)
    transform = transforms.Compose(
      [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
    trainset = CIFAR10("./dataset", train=True, download=True, transform=transform)
    testset = CIFAR10("./dataset", train=False, download=True, transform=transform)

    # Split training set into 10 partitions to simulate the individual dataset
    partition_size = len(trainset) // NUM_CLIENTS
    lengths = [partition_size] * NUM_CLIENTS
    datasets = random_split(trainset, lengths, torch.Generator().manual_seed(42))

    # Split each partition into train/val and create DataLoader
    trainloaders = []
    valloaders = []
    for ds in datasets:
        len_val = len(ds) // 10  # 10 % validation set
        len_train = len(ds) - len_val
        lengths = [len_train, len_val]
        ds_train, ds_val = random_split(ds, lengths, torch.Generator().manual_seed(42))
        trainloaders.append(DataLoader(ds_train, batch_size=BATCH_SIZE, shuffle=True))
        valloaders.append(DataLoader(ds_val, batch_size=BATCH_SIZE))
    testloader = DataLoader(testset, batch_size=BATCH_SIZE)
    return trainloaders, valloaders, testloader

trainloaders, valloaders, testloader = load_datasets()

We now have a list of ten training sets and ten validation sets (trainloaders and valloaders) representing the data of ten different organizations. Each trainloader/valloader pair contains 4500 training examples and 500 validation examples. There’s also a single testloader (we did not split the test set). Again, this is only necessary for building research or educational systems, actual federated learning systems have their data naturally distributed across multiple partitions.

Let’s take a look at the first batch of images and labels in the first training set (i.e., trainloaders[0]) before we move on:

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def imshow(img):
    img = img / 2 + 0.5  # unnormalize
    plt.imshow(np.transpose(img.numpy(), (1, 2, 0)))

images, labels = iter(trainloaders[0]).next()
print(' '.join('%5s' % CLASSES[labels[j]] for j in range(32)))

The output above shows a random batch of images from the first trainloader in our list of ten trainloaders. It also prints the labels associated with each image (i.e., one of the ten possible labels we’ve seen above). If you run the cell again, you should see another batch of images.

Part 1: Centralized Training with PyTorch#

Next, we’re going to use PyTorch to define a simple convolutional neural network. This introduction assumes basic familiarity with PyTorch, so it doesn’t cover the PyTorch-related aspects in full detail. If you want to dive deeper into PyTorch, we recommend DEEP LEARNING WITH PYTORCH: A 60 MINUTE BLITZ.

Defining the model#

We use the simple CNN described in the PyTorch tutorial:

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class Net(nn.Module):
    def __init__(self) -> None:
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

Let’s continue with the usual training and test functions:

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def train(net, trainloader, epochs: int, verbose=False):
    """Train the network on the training set."""
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(net.parameters())
    for epoch in range(epochs):
        correct, total, epoch_loss = 0, 0, 0.0
        for images, labels in trainloader:
            images, labels =,
            outputs = net(images)
            loss = criterion(net(images), labels)
            # Metrics
            epoch_loss += loss
            total += labels.size(0)
            correct += (torch.max(, 1)[1] == labels).sum().item()
        epoch_loss /= len(testloader.dataset)
        epoch_acc = correct / total
        if verbose:
            print(f"Epoch {epoch+1}: train loss {epoch_loss}, accuracy {epoch_acc}")

def test(net, testloader):
    """Evaluate the network on the entire test set."""
    criterion = torch.nn.CrossEntropyLoss()
    correct, total, loss = 0, 0, 0.0
    with torch.no_grad():
        for images, labels in testloader:
            images, labels =,
            outputs = net(images)
            loss += criterion(outputs, labels).item()
            _, predicted = torch.max(, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    loss /= len(testloader.dataset)
    accuracy = correct / total
    return loss, accuracy

Training the model#

We now have all the basic building blocks we need: a dataset, a model, a training function, and a test function. Let’s put them together to train the model on the dataset of one of our organizations (trainloaders[0]). This simulates the reality of most machine learning projects today: each organization has their own data and trains models only on this internal data:

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trainloader = trainloaders[0]
valloader = valloaders[0]
net = Net().to(DEVICE)

for epoch in range(5):
    train(net, trainloader, 1)
    loss, accuracy = test(net, valloader)
    print(f"Epoch {epoch+1}: validation loss {loss}, accuracy {accuracy}")

loss, accuracy = test(net, testloader)
print(f"Final test set performance:\n\tloss {loss}\n\taccuracy {accuracy}")

Training the simple CNN on our CIFAR-10 split for 5 epochs should result in a test set accuracy of about 41%, which is not good, but at the same time, it doesn’t really matter for the purposes of this tutorial. The intent was just to show a simplistic centralized training pipeline that sets the stage for what comes next - federated learning!

Part 2: Federated Learning with Flower#

Part 1 demonstrated a simple centralized training pipeline. All data was in one place (i.e., a single trainloader and a single valloader). Next, we’ll simulate a situation where we have multiple datasets in multiple organizations and where we train a model over these organizations using federated learning.

Updating model parameters#

In federated learning, the server sends the global model parameters to the client, and the client updates the local model with the parameters received from the server. It then trains the model on the local data (which changes the model parameters locally) and sends the updated/changed model parameters back to the server (or, alternatively, it sends just the gradients back to the server, not the full model parameters).

We need two helper functions to update the local model with parameters received from the server and to get the updated model parameters from the local model: set_parameters and get_parameters. The following two functions do just that for the PyTorch model above.

The details of how this works are not really important here (feel free to consult the PyTorch documentation if you want to learn more). In essence, we use state_dict to access PyTorch model parameter tensors. The parameter tensors are then converted to/from a list of NumPy ndarray’s (which Flower knows how to serialize/deserialize):

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def get_parameters(net) -> List[np.ndarray]:
    return [val.cpu().numpy() for _, val in net.state_dict().items()]

def set_parameters(net, parameters: List[np.ndarray]):
    params_dict = zip(net.state_dict().keys(), parameters)
    state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
    net.load_state_dict(state_dict, strict=True)

Implementing a Flower client#

With that out of the way, let’s move on to the interesting part. Federated learning systems consist of a server and multiple clients. In Flower, we create clients by implementing subclasses of flwr.client.Client or flwr.client.NumPyClient. We use NumPyClient in this tutorial because it is easier to implement and requires us to write less boilerplate.

To implement the Flower client, we create a subclass of flwr.client.NumPyClient and implement the three methods get_parameters, fit, and evaluate:

  • get_parameters: Return the current local model parameters

  • fit: Receive model parameters from the server, train the model parameters on the local data, and return the (updated) model parameters to the server

  • evaluate: Receive model parameters from the server, evaluate the model parameters on the local data, and return the evaluation result to the server

We mentioned that our clients will use the previously defined PyTorch components for model training and evaluation. Let’s see a simple Flower client implementation that brings everything together:

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class FlowerClient(fl.client.NumPyClient):
    def __init__(self, net, trainloader, valloader): = net
        self.trainloader = trainloader
        self.valloader = valloader

    def get_parameters(self, config):
        return get_parameters(

    def fit(self, parameters, config):
        set_parameters(, parameters)
        train(, self.trainloader, epochs=1)
        return get_parameters(, len(self.trainloader), {}

    def evaluate(self, parameters, config):
        set_parameters(, parameters)
        loss, accuracy = test(, self.valloader)
        return float(loss), len(self.valloader), {"accuracy": float(accuracy)}

Our class FlowerClient defines how local training/evaluation will be performed and allows Flower to call the local training/evaluation through fit and evaluate. Each instance of FlowerClient represents a single client in our federated learning system. Federated learning systems have multiple clients (otherwise there’s not much to federate), so each client will be represented by its own instance of FlowerClient. If we have, for example, three clients in our workload, then we’d have three instances of FlowerClient. Flower calls on the respective instance when the server selects a particular client for training (and FlowerClient.evaluate for evaluation).

Using the Virtual Client Engine#

In this notebook, we want to simulate a federated learning system with 10 clients on a single machine. This means that the server and all 10 clients will live on a single machine and share resources such as CPU, GPU, and memory. Having 10 clients would mean having 10 instances of FlowerClient im memory. Doing this on a single machine can quickly exhaust the available memory resources, even if only a subset of these clients participates in a single round of federated learning.

In addition to the regular capabilities where server and clients run on multiple machines, Flower therefore provides special simulation capabilities that create FlowerClient instances only when they are actually necessary for training or evaluation. To enable the Flower framework to create clients when necessary, we need to implement a function called client_fn that creates a FlowerClient instance on demand. Flower calls client_fn whenever it needs an instance of one particular client to call fit or evaluate (those instances are usually discarded after use, so they should not keep any local state). Clients are identified by a client ID, or short cid. The cid can be used, for example, to load different local data partitions for different clients, as can be seen below:

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def client_fn(cid: str) -> FlowerClient:
    """Create a Flower client representing a single organization."""

    # Load model
    net = Net().to(DEVICE)

    # Load data (CIFAR-10)
    # Note: each client gets a different trainloader/valloader, so each client
    # will train and evaluate on their own unique data
    trainloader = trainloaders[int(cid)]
    valloader = valloaders[int(cid)]

    # Create a  single Flower client representing a single organization
    return FlowerClient(net, trainloader, valloader)

Starting the training#

We now have the class FlowerClient which defines client-side training/evaluation and client_fn which allows Flower to create FlowerClient instances whenever it needs to call fit or evaluate on one particular client. The last step is to start the actual simulation using flwr.simulation.start_simulation.

The function start_simulation accepts a number of arguments, amongst them the client_fn used to create FlowerClient instances, the number of clients to simulate (num_clients), the number of federated learning rounds (num_rounds), and the strategy. The strategy encapsulates the federated learning approach/algorithm, for example, Federated Averaging (FedAvg).

Flower has a number of built-in strategies, but we can also use our own strategy implementations to customize nearly all aspects of the federated learning approach. For this example, we use the built-in FedAvg implementation and customize it using a few basic parameters. The last step is the actual call to start_simulation which - you guessed it - starts the simulation:

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# Create FedAvg strategy
strategy = fl.server.strategy.FedAvg(
        fraction_fit=1.0,  # Sample 100% of available clients for training
        fraction_evaluate=0.5,  # Sample 50% of available clients for evaluation
        min_fit_clients=10,  # Never sample less than 10 clients for training
        min_evaluate_clients=5,  # Never sample less than 5 clients for evaluation
        min_available_clients=10,  # Wait until all 10 clients are available

# Start simulation

Behind the scenes#

So how does this work? How does Flower execute this simulation?

When we call start_simulation, we tell Flower that there are 10 clients (num_clients=10). Flower then goes ahead an asks the FedAvg strategy to select clients. FedAvg knows that it should select 100% of the available clients (fraction_fit=1.0), so it goes ahead and selects 10 random clients (i.e., 100% of 10).

Flower then asks the selected 10 clients to train the model. When the server receives the model parameter updates from the clients, it hands those updates over to the strategy (FedAvg) for aggregation. The strategy aggregates those updates and returns the new global model, which then gets used in the next round of federated learning.

Where’s the accuracy?#

You may have noticed that all metrics except for losses_distributed are empty. Where did the {"accuracy": float(accuracy)} go?

Flower can automatically aggregate losses returned by individual clients, but it cannot do the same for metrics in the generic metrics dictionary (the one with the accuracy key). Metrics dictionaries can contain very different kinds of metrics and even key/value pairs that are not metrics at all, so the framework does not (and can not) know how to handle these automatically.

As users, we need to tell the framework how to handle/aggregate these custom metrics, and we do so by passing metric aggregation functions to the strategy. The strategy will then call these functions whenever it receives fit or evaluate metrics from clients. The two possible functions are fit_metrics_aggregation_fn and evaluate_metrics_aggregation_fn.

Let’s create a simple weighted averaging function to aggregate the accuracy metric we return from evaluate:

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def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
    # Multiply accuracy of each client by number of examples used
    accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics]
    examples = [num_examples for num_examples, _ in metrics]

    # Aggregate and return custom metric (weighted average)
    return {"accuracy": sum(accuracies) / sum(examples)}

The only thing left to do is to tell the strategy to call this function whenever it receives evaluation metric dictionaries from the clients:

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# Create FedAvg strategy
strategy = fl.server.strategy.FedAvg(
        evaluate_metrics_aggregation_fn=weighted_average,  # <-- pass the metric aggregation function

# Start simulation

We now have a full system that performs federated training and federated evaluation. It uses the weighted_average function to aggregate custom evaluation metrics and calculates a single accuracy metric across all clients on the server side.

The other two categories of metrics (losses_centralized and metrics_centralized) are still empty because they only apply when centralized evaluation is being used. Part two of the Flower tutorial will cover centralized evaluation.

Final remarks#

Congratulations, you just trained a convolutional neural network, federated over 10 clients! With that, you understand the basics of federated learning with Flower. The same approach you’ve seen can be used with other machine learning frameworks (not just PyTorch) and tasks (not just CIFAR-10 images classification), for example NLP with Hugging Face Transformers or speech with SpeechBrain.

In the next notebook, we’re going to cover some more advanced concepts. Want to customize your strategy? Initialize parameters on the server side? Or evaluate the aggregated model on the server side? We’ll cover all this and more in the next tutorial.

Next steps#

Before you continue, make sure to join the Flower community on Slack:

There’s a dedicated #questions channel if you need help, but we’d also love to hear who you are in #introductions!

Part two of the Flower tutorial goes into more depth about strategies and all the advanced things you can build with them.

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