Use a federated learning strategy

Welcome to the next part of the federated learning tutorial. In previous parts of this tutorial, we introduced federated learning with PyTorch and Flower (part 1).

In part 2, we’ll begin to customize the federated learning system we built in part 1 using the Flower framework, Flower Datasets, and PyTorch.

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Let’s move beyond FedAvg with Flower strategies! 🌼

Preparation

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

Installing dependencies

참고

If you’ve completed part 1 of the tutorial, you can skip this step.

First, we install the Flower package flwr:

# In a new Python environment
$ pip install -U "flwr[simulation]"

Then, we create a new Flower app called flower-tutorial using the PyTorch template. We also specify a username (flwrlabs) for the project:

$ flwr new flower-tutorial --framework pytorch --username flwrlabs

After running the command, a new directory called flower-tutorial will be created. It should have the following structure:

flower-tutorial
├── README.md
├── flower_tutorial
│   ├── __init__.py
│   ├── client_app.py   # Defines your ClientApp
│   ├── server_app.py   # Defines your ServerApp
│   └── task.py         # Defines your model, training and data loading
├── pyproject.toml      # Project metadata like dependencies and configs
└── README.md

Next, we install the project and its dependencies, which are specified in the pyproject.toml file:

$ cd flower-tutorial
$ pip install -e .

Strategy customization

So far, everything should look familiar if you’ve worked through the introductory tutorial. With that, we’re ready to introduce a number of new features.

Starting with a customized strategy

In part 1, we created a ServerApp (in server_app.py) using the server_fn. In it, we defined the strategy and number of training rounds.

The strategy encapsulates the federated learning approach/algorithm, for example, FedAvg or FedAdagrad. Let’s try to use a different strategy this time. Add this line to the top of your server_app.py: from flwr.server.strategy import FedAdagrad and replace the server_fn() with the following code:

def server_fn(context: Context):
    # Read from config
    num_rounds = context.run_config["num-server-rounds"]
    fraction_fit = context.run_config["fraction-fit"]

    # Initialize model parameters
    ndarrays = get_weights(Net())
    parameters = ndarrays_to_parameters(ndarrays)

    # Define strategy
    strategy = FedAdagrad(
        fraction_fit=fraction_fit,
        fraction_evaluate=1.0,
        min_available_clients=2,
        initial_parameters=parameters,
    )
    config = ServerConfig(num_rounds=num_rounds)

    return ServerAppComponents(strategy=strategy, config=config)

Next, run the training with the following command:

$ flwr run .

Server-side parameter evaluation

Flower can evaluate the aggregated model on the server-side or on the client-side. Client-side and server-side evaluation are similar in some ways, but different in others.

Centralized Evaluation (or server-side evaluation) is conceptually simple: it works the same way that evaluation in centralized machine learning does. If there is a server-side dataset that can be used for evaluation purposes, then that’s great. We can evaluate the newly aggregated model after each round of training without having to send the model to clients. We’re also fortunate in the sense that our entire evaluation dataset is available at all times.

Federated Evaluation (or client-side evaluation) is more complex, but also more powerful: it doesn’t require a centralized dataset and allows us to evaluate models over a larger set of data, which often yields more realistic evaluation results. In fact, many scenarios require us to use Federated Evaluation if we want to get representative evaluation results at all. But this power comes at a cost: once we start to evaluate on the client side, we should be aware that our evaluation dataset can change over consecutive rounds of learning if those clients are not always available. Moreover, the dataset held by each client can also change over consecutive rounds. This can lead to evaluation results that are not stable, so even if we would not change the model, we’d see our evaluation results fluctuate over consecutive rounds.

We’ve seen how federated evaluation works on the client side (i.e., by implementing the evaluate method in FlowerClient). Now let’s see how we can evaluate aggregated model parameters on the server-side. First we define a new function evaluate in task.py:

from datasets import load_dataset


def evaluate(
    server_round: int,
    parameters,
    config,
):
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    net = Net().to(device)

    # Load the entire CIFAR10 test dataset
    # It's a huggingface dataset, so we can load it directly and apply transforms
    cifar10_test = load_dataset("cifar10", split="test")
    pytorch_transforms = Compose(
        [ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
    )

    def apply_transforms(batch):
        batch["img"] = [pytorch_transforms(img) for img in batch["img"]]
        return batch

    testset = cifar10_test.with_transform(apply_transforms)
    testloader = DataLoader(testset, batch_size=64)

    set_weights(net, parameters)  # Update model with the latest parameters
    loss, accuracy = test(net, testloader, device)
    return loss, {"accuracy": accuracy}

Next, in server_app.py, we pass the evaluate function to the evaluate_fn parameter of the FedAvg strategy:

def server_fn(context: Context) -> ServerAppComponents:
    # Read from config
    num_rounds = context.run_config["num-server-rounds"]
    fraction_fit = context.run_config["fraction-fit"]

    # Initialize model parameters
    ndarrays = get_weights(Net())
    parameters = ndarrays_to_parameters(ndarrays)

    strategy = FedAvg(
        fraction_fit=fraction_fit,
        fraction_evaluate=1.0,
        min_available_clients=2,
        initial_parameters=parameters,
        evaluate_fn=evaluate,
    )
    config = ServerConfig(num_rounds=num_rounds)

    return ServerAppComponents(strategy=strategy, config=config)


# Create ServerApp
app = ServerApp(server_fn=server_fn)

Finally, we run the simulation.

$ flwr run .

Sending configurations to clients from strategies

In some situations, we want to configure client-side execution (training, evaluation) from the server-side. One example for that is the server asking the clients to train for a certain number of local epochs. Flower provides a way to send configuration values from the server to the clients using a dictionary. Let’s look at an example where the clients receive values from the server through the config parameter in fit (config is also available in evaluate). The fit method receives the configuration dictionary through the config parameter and can then read values from this dictionary. In this example, it reads server_round and local_epochs and uses those values to improve the logging and configure the number of local training epochs. In our client_app.py, replace the FlowerClient() class and client_fn() with the following code:

class FlowerClient(NumPyClient):
    def __init__(self, pid, net, trainloader, valloader):
        self.pid = pid  # partition ID of a client
        self.net = net
        self.trainloader = trainloader
        self.valloader = valloader
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        self.net.to(self.device)

    def get_weights(self, config):
        print(f"[Client {self.pid}] get_weights")
        return get_weights(self.net)

    def fit(self, parameters, config):
        # Read values from config
        server_round = config["server_round"]
        local_epochs = config["local_epochs"]

        # Use values provided by the config
        print(f"[Client {self.pid}, round {server_round}] fit, config: {config}")
        set_weights(self.net, parameters)
        train(self.net, self.trainloader, epochs=local_epochs, device=self.device)
        return get_weights(self.net), len(self.trainloader), {}

    def evaluate(self, parameters, config):
        print(f"[Client {self.pid}] evaluate, config: {config}")
        set_weights(self.net, parameters)
        loss, accuracy = test(self.net, self.valloader, device=self.device)
        return float(loss), len(self.valloader), {"accuracy": float(accuracy)}


def client_fn(context: Context):
    net = Net()
    partition_id = context.node_config["partition-id"]
    num_partitions = context.node_config["num-partitions"]
    trainloader, valloader = load_data(partition_id, num_partitions)

    return FlowerClient(partition_id, net, trainloader, valloader).to_client()

So how can we send this config dictionary from server to clients? The built-in Flower Strategies provide way to do this, and it works similarly to the way server-side evaluation works. We provide a callback to the strategy, and the strategy calls this callback for every round of federated learning. Add the following to your server_app.py:

def fit_config(server_round: int):
    """Return training configuration dict for each round.

    Perform two rounds of training with one local epoch, increase to two local
    epochs afterwards.
    """
    config = {
        "server_round": server_round,  # The current round of federated learning
        "local_epochs": 1 if server_round < 2 else 2,
    }
    return config

Next, we’ll pass this function to the FedAvg strategy before starting the simulation. Change the server_fn() function in server_app.py to the following:

def server_fn(context: Context):
    # Read from config
    num_rounds = context.run_config["num-server-rounds"]
    fraction_fit = context.run_config["fraction-fit"]

    # Initialize model parameters
    ndarrays = get_weights(Net())
    parameters = ndarrays_to_parameters(ndarrays)

    strategy = FedAvg(
        fraction_fit=fraction_fit,
        fraction_evaluate=1.0,
        min_available_clients=2,
        initial_parameters=parameters,
        evaluate_fn=evaluate,
        on_fit_config_fn=fit_config,
    )
    config = ServerConfig(num_rounds=num_rounds)

    return ServerAppComponents(strategy=strategy, config=config)

Finally, run the training with the following command:

$ flwr run .

As we can see, the client logs now include the current round of federated learning (which they read from the config dictionary). We can also configure local training to run for one epoch during the first and second round of federated learning, and then for two epochs during the third round.

Clients can also return arbitrary values to the server. To do so, they return a dictionary from fit and/or evaluate. We have seen and used this concept throughout this tutorial without mentioning it explicitly: our FlowerClient returns a dictionary containing a custom key/value pair as the third return value in evaluate.

Scaling federated learning

As a last step in this tutorial, let’s see how we can use Flower to experiment with a large number of clients. In the pyproject.toml, increase the number of SuperNodes to 1000:

[tool.flwr.federations.local-simulation]
options.num-supernodes = 1000

Note that we can reuse the ClientApp for different num-supernodes since the Context is defined by the num-partitions argument in the client_fn() and for simulations with Flower, the number of partitions is equal to the number of SuperNodes.

We now have 1000 partitions, each holding 45 training and 5 validation examples. Given that the number of training examples on each client is quite small, we should probably train the model a bit longer, so we configure the clients to perform 3 local training epochs. We should also adjust the fraction of clients selected for training during each round (we don’t want all 1000 clients participating in every round), so we adjust fraction_fit to 0.025, which means that only 2.5% of available clients (so 25 clients) will be selected for training each round. We update the fraction-fit value in the pyproject.toml:

[tool.flwr.app.config]
fraction-fit = 0.025

Then, we update the fit_config and server_fn functions in server_app.py to the following:

def fit_config(server_round: int):
    config = {
        "server_round": server_round,
        "local_epochs": 3,
    }
    return config


def server_fn(context: Context):
    # Read from config
    num_rounds = context.run_config["num-server-rounds"]
    fraction_fit = context.run_config["fraction-fit"]

    # Initialize model parameters
    ndarrays = get_weights(Net())
    parameters = ndarrays_to_parameters(ndarrays)

    # Create FedAvg strategy
    strategy = FedAvg(
        fraction_fit=fraction_fit,  # Train on 25 clients (each round)
        fraction_evaluate=0.05,  # Evaluate on 50 clients (each round)
        min_fit_clients=20,
        min_evaluate_clients=40,  # Optional config
        min_available_clients=1000,  # Optional config
        initial_parameters=parameters,
        on_fit_config_fn=fit_config,
    )
    config = ServerConfig(num_rounds=num_rounds)

    return ServerAppComponents(strategy=strategy, config=config)


# Create the ServerApp
server = ServerApp(server_fn=server_fn)

Finally, run the simulation with the following command:

$ flwr run .

Recap

In this tutorial, we’ve seen how we can gradually enhance our system by customizing the strategy, initializing parameters on the server side, choosing a different strategy, and evaluating models on the server-side. That’s quite a bit of flexibility with so little code, right?

In the later sections, we’ve seen how we can communicate arbitrary values between server and clients to fully customize client-side execution. With that capability, we built a large-scale Federated Learning simulation using the Flower Virtual Client Engine and ran an experiment involving 1000 clients in the same workload - all in the same Flower project!

다음 단계

Before you continue, make sure to join the Flower community on Flower Discuss (Join Flower Discuss) and on Slack (Join Slack).

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

The Flower Federated Learning Tutorial - Part 3 shows how to build a fully custom Strategy from scratch.