Customize the client

Welcome to the fourth part of the Flower federated learning tutorial. In the previous parts of this tutorial, we introduced federated learning with PyTorch and Flower (part 1), we learned how strategies can be used to customize the execution on both the server and the clients (part 2), and we built our own custom strategy from scratch (part 3).

In this notebook, we revisit NumPyClient and introduce a new baseclass for building clients, simply named Client. In previous parts of this tutorial, we’ve based our client on NumPyClient, a convenience class which makes it easy to work with machine learning libraries that have good NumPy interoperability. With Client, we gain a lot of flexibility that we didn’t have before, but we’ll also have to do a few things the we didn’t have to do before.

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Let’s go deeper and see what it takes to move from NumPyClient to Client! 🌼

Step 0: Preparation

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

Installing dependencies

First, we install the necessary packages:

!pip install -q flwr[simulation] flwr-datasets[vision] torch torchvision scipy

Now that we have all dependencies installed, we can import everything we need for this tutorial:

from collections import OrderedDict
from typing import List

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from torch.utils.data import DataLoader

import flwr
from flwr.client import Client, ClientApp, NumPyClient
from flwr.common import Context
from flwr.server import ServerApp, ServerConfig, ServerAppComponents
from flwr.simulation import run_simulation
from flwr_datasets import FederatedDataset

DEVICE = torch.device("cpu")  # Try "cuda" to train on GPU
print(f"Training on {DEVICE}")
print(f"Flower {flwr.__version__} / PyTorch {torch.__version__}")

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, otherwise it’ll say Training on cpu.

Data loading

Let’s now define a loading function for the CIFAR-10 training and test set, partition them into num_partitions smaller datasets (each split into training and validation set), and wrap everything in their own DataLoader.

def load_datasets(partition_id: int, num_partitions: int):
    fds = FederatedDataset(dataset="cifar10", partitioners={"train": num_partitions})
    partition = fds.load_partition(partition_id)
    # Divide data on each node: 80% train, 20% test
    partition_train_test = partition.train_test_split(test_size=0.2, seed=42)
    pytorch_transforms = transforms.Compose(
        [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
    )

    def apply_transforms(batch):
        # Instead of passing transforms to CIFAR10(..., transform=transform)
        # we will use this function to dataset.with_transform(apply_transforms)
        # The transforms object is exactly the same
        batch["img"] = [pytorch_transforms(img) for img in batch["img"]]
        return batch

    partition_train_test = partition_train_test.with_transform(apply_transforms)
    trainloader = DataLoader(partition_train_test["train"], batch_size=32, shuffle=True)
    valloader = DataLoader(partition_train_test["test"], batch_size=32)
    testset = fds.load_split("test").with_transform(apply_transforms)
    testloader = DataLoader(testset, batch_size=32)
    return trainloader, valloader, testloader

Model training/evaluation

Let’s continue with the usual model definition (including set_parameters and get_parameters), training and test functions:

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


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)


def train(net, trainloader, epochs: int):
    """Train the network on the training set."""
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(net.parameters())
    net.train()
    for epoch in range(epochs):
        correct, total, epoch_loss = 0, 0, 0.0
        for batch in trainloader:
            images, labels = batch["img"], batch["label"]
            images, labels = images.to(DEVICE), labels.to(DEVICE)
            optimizer.zero_grad()
            outputs = net(images)
            loss = criterion(net(images), labels)
            loss.backward()
            optimizer.step()
            # Metrics
            epoch_loss += loss
            total += labels.size(0)
            correct += (torch.max(outputs.data, 1)[1] == labels).sum().item()
        epoch_loss /= len(trainloader.dataset)
        epoch_acc = correct / total
        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
    net.eval()
    with torch.no_grad():
        for batch in testloader:
            images, labels = batch["img"], batch["label"]
            images, labels = images.to(DEVICE), labels.to(DEVICE)
            outputs = net(images)
            loss += criterion(outputs, labels).item()
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    loss /= len(testloader.dataset)
    accuracy = correct / total
    return loss, accuracy

Step 1: Revisiting NumPyClient

So far, we’ve implemented our client by subclassing flwr.client.NumPyClient. The three methods we implemented are get_parameters, fit, and evaluate.

class FlowerNumPyClient(NumPyClient):
    def __init__(self, partition_id, net, trainloader, valloader):
        self.partition_id = partition_id
        self.net = net
        self.trainloader = trainloader
        self.valloader = valloader

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

    def fit(self, parameters, config):
        print(f"[Client {self.partition_id}] fit, config: {config}")
        set_parameters(self.net, parameters)
        train(self.net, self.trainloader, epochs=1)
        return get_parameters(self.net), len(self.trainloader), {}

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

Then, we define the function numpyclient_fn that is used by Flower to create the FlowerNumpyClient instances on demand. Finally, we create the ClientApp and pass the numpyclient_fn to it.

def numpyclient_fn(context: Context) -> Client:
    net = Net().to(DEVICE)
    partition_id = context.node_config["partition-id"]
    num_partitions = context.node_config["num-partitions"]
    trainloader, valloader, _ = load_datasets(partition_id, num_partitions)
    return FlowerNumPyClient(partition_id, net, trainloader, valloader).to_client()


# Create the ClientApp
numpyclient = ClientApp(client_fn=numpyclient_fn)

We’ve seen this before, there’s nothing new so far. The only tiny difference compared to the previous notebook is naming, we’ve changed FlowerClient to FlowerNumPyClient and client_fn to numpyclient_fn. Next, we configure the number of federated learning rounds using ServerConfig and create the ServerApp with this config:

def server_fn(context: Context) -> ServerAppComponents:
    # Configure the server for 3 rounds of training
    config = ServerConfig(num_rounds=3)
    return ServerAppComponents(config=config)


# Create ServerApp
server = ServerApp(server_fn=server_fn)

Finally, we specify the resources for each client and run the simulation to see the output we get:

# Specify the resources each of your clients need
# If set to none, by default, each client will be allocated 2x CPU and 0x GPUs
backend_config = {"client_resources": None}
if DEVICE.type == "cuda":
    backend_config = {"client_resources": {"num_gpus": 1}}

NUM_PARTITIONS = 10

# Run simulation
run_simulation(
    server_app=server,
    client_app=numpyclient,
    num_supernodes=NUM_PARTITIONS,
    backend_config=backend_config,
)

This works as expected, ten clients are training for three rounds of federated learning.

Let’s dive a little bit deeper and discuss how Flower executes this simulation. Whenever a client is selected to do some work, run_simulation launches the ClientApp object which in turn calls the function numpyclient_fn to create an instance of our FlowerNumPyClient (along with loading the model and the data).

But here’s the perhaps surprising part: Flower doesn’t actually use the FlowerNumPyClient object directly. Instead, it wraps the object to makes it look like a subclass of flwr.client.Client, not flwr.client.NumPyClient. In fact, the Flower core framework doesn’t know how to handle NumPyClient’s, it only knows how to handle Client’s. NumPyClient is just a convenience abstraction built on top of Client.

Instead of building on top of NumPyClient, we can directly build on top of Client.

Step 2: Moving from NumPyClient to Client

Let’s try to do the same thing using Client instead of NumPyClient.

from flwr.common import (
    Code,
    EvaluateIns,
    EvaluateRes,
    FitIns,
    FitRes,
    GetParametersIns,
    GetParametersRes,
    Status,
    ndarrays_to_parameters,
    parameters_to_ndarrays,
)


class FlowerClient(Client):
    def __init__(self, partition_id, net, trainloader, valloader):
        self.partition_id = partition_id
        self.net = net
        self.trainloader = trainloader
        self.valloader = valloader

    def get_parameters(self, ins: GetParametersIns) -> GetParametersRes:
        print(f"[Client {self.partition_id}] get_parameters")

        # Get parameters as a list of NumPy ndarray's
        ndarrays: List[np.ndarray] = get_parameters(self.net)

        # Serialize ndarray's into a Parameters object
        parameters = ndarrays_to_parameters(ndarrays)

        # Build and return response
        status = Status(code=Code.OK, message="Success")
        return GetParametersRes(
            status=status,
            parameters=parameters,
        )

    def fit(self, ins: FitIns) -> FitRes:
        print(f"[Client {self.partition_id}] fit, config: {ins.config}")

        # Deserialize parameters to NumPy ndarray's
        parameters_original = ins.parameters
        ndarrays_original = parameters_to_ndarrays(parameters_original)

        # Update local model, train, get updated parameters
        set_parameters(self.net, ndarrays_original)
        train(self.net, self.trainloader, epochs=1)
        ndarrays_updated = get_parameters(self.net)

        # Serialize ndarray's into a Parameters object
        parameters_updated = ndarrays_to_parameters(ndarrays_updated)

        # Build and return response
        status = Status(code=Code.OK, message="Success")
        return FitRes(
            status=status,
            parameters=parameters_updated,
            num_examples=len(self.trainloader),
            metrics={},
        )

    def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
        print(f"[Client {self.partition_id}] evaluate, config: {ins.config}")

        # Deserialize parameters to NumPy ndarray's
        parameters_original = ins.parameters
        ndarrays_original = parameters_to_ndarrays(parameters_original)

        set_parameters(self.net, ndarrays_original)
        loss, accuracy = test(self.net, self.valloader)

        # Build and return response
        status = Status(code=Code.OK, message="Success")
        return EvaluateRes(
            status=status,
            loss=float(loss),
            num_examples=len(self.valloader),
            metrics={"accuracy": float(accuracy)},
        )


def client_fn(context: Context) -> Client:
    net = Net().to(DEVICE)
    partition_id = context.node_config["partition-id"]
    num_partitions = context.node_config["num-partitions"]
    trainloader, valloader, _ = load_datasets(partition_id, num_partitions)
    return FlowerClient(partition_id, net, trainloader, valloader).to_client()


# Create the ClientApp
client = ClientApp(client_fn=client_fn)

Before we discuss the code in more detail, let’s try to run it! Gotta make sure our new Client-based client works, right?

# Run simulation
run_simulation(
    server_app=server,
    client_app=client,
    num_supernodes=NUM_PARTITIONS,
    backend_config=backend_config,
)

That’s it, we’re now using Client. It probably looks similar to what we’ve done with NumPyClient. So what’s the difference?

First of all, it’s more code. But why? The difference comes from the fact that Client expects us to take care of parameter serialization and deserialization. For Flower to be able to send parameters over the network, it eventually needs to turn these parameters into bytes. Turning parameters (e.g., NumPy ndarray’s) into raw bytes is called serialization. Turning raw bytes into something more useful (like NumPy ndarray’s) is called deserialization. Flower needs to do both: it needs to serialize parameters on the server-side and send them to the client, the client needs to deserialize them to use them for local training, and then serialize the updated parameters again to send them back to the server, which (finally!) deserializes them again in order to aggregate them with the updates received from other clients.

The only real difference between Client and NumPyClient is that NumPyClient takes care of serialization and deserialization for you. It can do so because it expects you to return parameters as NumPy ndarray’s, and it knows how to handle these. This makes working with machine learning libraries that have good NumPy support (most of them) a breeze.

In terms of API, there’s one major difference: all methods in Client take exactly one argument (e.g., FitIns in Client.fit) and return exactly one value (e.g., FitRes in Client.fit). The methods in NumPyClient on the other hand have multiple arguments (e.g., parameters and config in NumPyClient.fit) and multiple return values (e.g., parameters, num_example, and metrics in NumPyClient.fit) if there are multiple things to handle. These *Ins and *Res objects in Client wrap all the individual values you’re used to from NumPyClient.

Step 3: Custom serialization

Here we will explore how to implement custom serialization with a simple example.

But first what is serialization? Serialization is just the process of converting an object into raw bytes, and equally as important, deserialization is the process of converting raw bytes back into an object. This is very useful for network communication. Indeed, without serialization, you could not just a Python object through the internet.

Federated Learning relies heavily on internet communication for training by sending Python objects back and forth between the clients and the server. This means that serialization is an essential part of Federated Learning.

In the following section, we will write a basic example where instead of sending a serialized version of our ndarrays containing our parameters, we will first convert the ndarray into sparse matrices, before sending them. This technique can be used to save bandwidth, as in certain cases where the weights of a model are sparse (containing many 0 entries), converting them to a sparse matrix can greatly improve their bytesize.

Our custom serialization/deserialization functions

This is where the real serialization/deserialization will happen, especially in ndarray_to_sparse_bytes for serialization and sparse_bytes_to_ndarray for deserialization.

Note that we imported the scipy.sparse library in order to convert our arrays.

from io import BytesIO
from typing import cast

import numpy as np

from flwr.common.typing import NDArray, NDArrays, Parameters


def ndarrays_to_sparse_parameters(ndarrays: NDArrays) -> Parameters:
    """Convert NumPy ndarrays to parameters object."""
    tensors = [ndarray_to_sparse_bytes(ndarray) for ndarray in ndarrays]
    return Parameters(tensors=tensors, tensor_type="numpy.ndarray")


def sparse_parameters_to_ndarrays(parameters: Parameters) -> NDArrays:
    """Convert parameters object to NumPy ndarrays."""
    return [sparse_bytes_to_ndarray(tensor) for tensor in parameters.tensors]


def ndarray_to_sparse_bytes(ndarray: NDArray) -> bytes:
    """Serialize NumPy ndarray to bytes."""
    bytes_io = BytesIO()

    if len(ndarray.shape) > 1:
        # We convert our ndarray into a sparse matrix
        ndarray = torch.tensor(ndarray).to_sparse_csr()

        # And send it byutilizing the sparse matrix attributes
        # WARNING: NEVER set allow_pickle to true.
        # Reason: loading pickled data can execute arbitrary code
        # Source: https://numpy.org/doc/stable/reference/generated/numpy.save.html
        np.savez(
            bytes_io,  # type: ignore
            crow_indices=ndarray.crow_indices(),
            col_indices=ndarray.col_indices(),
            values=ndarray.values(),
            allow_pickle=False,
        )
    else:
        # WARNING: NEVER set allow_pickle to true.
        # Reason: loading pickled data can execute arbitrary code
        # Source: https://numpy.org/doc/stable/reference/generated/numpy.save.html
        np.save(bytes_io, ndarray, allow_pickle=False)
    return bytes_io.getvalue()


def sparse_bytes_to_ndarray(tensor: bytes) -> NDArray:
    """Deserialize NumPy ndarray from bytes."""
    bytes_io = BytesIO(tensor)
    # WARNING: NEVER set allow_pickle to true.
    # Reason: loading pickled data can execute arbitrary code
    # Source: https://numpy.org/doc/stable/reference/generated/numpy.load.html
    loader = np.load(bytes_io, allow_pickle=False)  # type: ignore

    if "crow_indices" in loader:
        # We convert our sparse matrix back to a ndarray, using the attributes we sent
        ndarray_deserialized = (
            torch.sparse_csr_tensor(
                crow_indices=loader["crow_indices"],
                col_indices=loader["col_indices"],
                values=loader["values"],
            )
            .to_dense()
            .numpy()
        )
    else:
        ndarray_deserialized = loader
    return cast(NDArray, ndarray_deserialized)

Client-side

To be able to serialize our ndarrays into sparse parameters, we will just have to call our custom functions in our flwr.client.Client.

Indeed, in get_parameters we need to serialize the parameters we got from our network using our custom ndarrays_to_sparse_parameters defined above.

In fit, we first need to deserialize the parameters coming from the server using our custom sparse_parameters_to_ndarrays and then we need to serialize our local results with ndarrays_to_sparse_parameters.

In evaluate, we will only need to deserialize the global parameters with our custom function.

from flwr.common import (
    Code,
    EvaluateIns,
    EvaluateRes,
    FitIns,
    FitRes,
    GetParametersIns,
    GetParametersRes,
    Status,
)


class FlowerClient(Client):
    def __init__(self, partition_id, net, trainloader, valloader):
        self.partition_id = partition_id
        self.net = net
        self.trainloader = trainloader
        self.valloader = valloader

    def get_parameters(self, ins: GetParametersIns) -> GetParametersRes:
        print(f"[Client {self.partition_id}] get_parameters")

        # Get parameters as a list of NumPy ndarray's
        ndarrays: List[np.ndarray] = get_parameters(self.net)

        # Serialize ndarray's into a Parameters object using our custom function
        parameters = ndarrays_to_sparse_parameters(ndarrays)

        # Build and return response
        status = Status(code=Code.OK, message="Success")
        return GetParametersRes(
            status=status,
            parameters=parameters,
        )

    def fit(self, ins: FitIns) -> FitRes:
        print(f"[Client {self.partition_id}] fit, config: {ins.config}")

        # Deserialize parameters to NumPy ndarray's using our custom function
        parameters_original = ins.parameters
        ndarrays_original = sparse_parameters_to_ndarrays(parameters_original)

        # Update local model, train, get updated parameters
        set_parameters(self.net, ndarrays_original)
        train(self.net, self.trainloader, epochs=1)
        ndarrays_updated = get_parameters(self.net)

        # Serialize ndarray's into a Parameters object using our custom function
        parameters_updated = ndarrays_to_sparse_parameters(ndarrays_updated)

        # Build and return response
        status = Status(code=Code.OK, message="Success")
        return FitRes(
            status=status,
            parameters=parameters_updated,
            num_examples=len(self.trainloader),
            metrics={},
        )

    def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
        print(f"[Client {self.partition_id}] evaluate, config: {ins.config}")

        # Deserialize parameters to NumPy ndarray's using our custom function
        parameters_original = ins.parameters
        ndarrays_original = sparse_parameters_to_ndarrays(parameters_original)

        set_parameters(self.net, ndarrays_original)
        loss, accuracy = test(self.net, self.valloader)

        # Build and return response
        status = Status(code=Code.OK, message="Success")
        return EvaluateRes(
            status=status,
            loss=float(loss),
            num_examples=len(self.valloader),
            metrics={"accuracy": float(accuracy)},
        )


def client_fn(context: Context) -> Client:
    net = Net().to(DEVICE)
    partition_id = context.node_config["partition-id"]
    num_partitions = context.node_config["num-partitions"]
    trainloader, valloader, _ = load_datasets(partition_id, num_partitions)
    return FlowerClient(partition_id, net, trainloader, valloader).to_client()

Server-side

For this example, we will just use FedAvg as a strategy. To change the serialization and deserialization here, we only need to reimplement the evaluate and aggregate_fit functions of FedAvg. The other functions of the strategy will be inherited from the super class FedAvg.

As you can see only one line as change in evaluate:

parameters_ndarrays = sparse_parameters_to_ndarrays(parameters)

And for aggregate_fit, we will first deserialize every result we received:

weights_results = [
    (sparse_parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples)
    for _, fit_res in results
]

And then serialize the aggregated result:

parameters_aggregated = ndarrays_to_sparse_parameters(aggregate(weights_results))

from logging import WARNING
from typing import Callable, Dict, List, Optional, Tuple, Union

from flwr.common import FitRes, MetricsAggregationFn, NDArrays, Parameters, Scalar
from flwr.common.logger import log
from flwr.server.client_proxy import ClientProxy
from flwr.server.strategy import FedAvg
from flwr.server.strategy.aggregate import aggregate

WARNING_MIN_AVAILABLE_CLIENTS_TOO_LOW = """
Setting `min_available_clients` lower than `min_fit_clients` or
`min_evaluate_clients` can cause the server to fail when there are too few clients
connected to the server. `min_available_clients` must be set to a value larger
than or equal to the values of `min_fit_clients` and `min_evaluate_clients`.
"""


class FedSparse(FedAvg):
    def __init__(
        self,
        *,
        fraction_fit: float = 1.0,
        fraction_evaluate: float = 1.0,
        min_fit_clients: int = 2,
        min_evaluate_clients: int = 2,
        min_available_clients: int = 2,
        evaluate_fn: Optional[
            Callable[
                [int, NDArrays, Dict[str, Scalar]],
                Optional[Tuple[float, Dict[str, Scalar]]],
            ]
        ] = None,
        on_fit_config_fn: Optional[Callable[[int], Dict[str, Scalar]]] = None,
        on_evaluate_config_fn: Optional[Callable[[int], Dict[str, Scalar]]] = None,
        accept_failures: bool = True,
        initial_parameters: Optional[Parameters] = None,
        fit_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
        evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
    ) -> None:
        """Custom FedAvg strategy with sparse matrices.

        Parameters
        ----------
        fraction_fit : float, optional
            Fraction of clients used during training. Defaults to 0.1.
        fraction_evaluate : float, optional
            Fraction of clients used during validation. Defaults to 0.1.
        min_fit_clients : int, optional
            Minimum number of clients used during training. Defaults to 2.
        min_evaluate_clients : int, optional
            Minimum number of clients used during validation. Defaults to 2.
        min_available_clients : int, optional
            Minimum number of total clients in the system. Defaults to 2.
        evaluate_fn : Optional[Callable[[int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]]]]
            Optional function used for validation. Defaults to None.
        on_fit_config_fn : Callable[[int], Dict[str, Scalar]], optional
            Function used to configure training. Defaults to None.
        on_evaluate_config_fn : Callable[[int], Dict[str, Scalar]], optional
            Function used to configure validation. Defaults to None.
        accept_failures : bool, optional
            Whether or not accept rounds containing failures. Defaults to True.
        initial_parameters : Parameters, optional
            Initial global model parameters.
        """

        if (
            min_fit_clients > min_available_clients
            or min_evaluate_clients > min_available_clients
        ):
            log(WARNING, WARNING_MIN_AVAILABLE_CLIENTS_TOO_LOW)

        super().__init__(
            fraction_fit=fraction_fit,
            fraction_evaluate=fraction_evaluate,
            min_fit_clients=min_fit_clients,
            min_evaluate_clients=min_evaluate_clients,
            min_available_clients=min_available_clients,
            evaluate_fn=evaluate_fn,
            on_fit_config_fn=on_fit_config_fn,
            on_evaluate_config_fn=on_evaluate_config_fn,
            accept_failures=accept_failures,
            initial_parameters=initial_parameters,
            fit_metrics_aggregation_fn=fit_metrics_aggregation_fn,
            evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation_fn,
        )

    def evaluate(
        self, server_round: int, parameters: Parameters
    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
        """Evaluate model parameters using an evaluation function."""
        if self.evaluate_fn is None:
            # No evaluation function provided
            return None

        # We deserialize using our custom method
        parameters_ndarrays = sparse_parameters_to_ndarrays(parameters)

        eval_res = self.evaluate_fn(server_round, parameters_ndarrays, {})
        if eval_res is None:
            return None
        loss, metrics = eval_res
        return loss, metrics

    def aggregate_fit(
        self,
        server_round: int,
        results: List[Tuple[ClientProxy, FitRes]],
        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
        """Aggregate fit results using weighted average."""
        if not results:
            return None, {}
        # Do not aggregate if there are failures and failures are not accepted
        if not self.accept_failures and failures:
            return None, {}

        # We deserialize each of the results with our custom method
        weights_results = [
            (sparse_parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples)
            for _, fit_res in results
        ]

        # We serialize the aggregated result using our custom method
        parameters_aggregated = ndarrays_to_sparse_parameters(
            aggregate(weights_results)
        )

        # Aggregate custom metrics if aggregation fn was provided
        metrics_aggregated = {}
        if self.fit_metrics_aggregation_fn:
            fit_metrics = [(res.num_examples, res.metrics) for _, res in results]
            metrics_aggregated = self.fit_metrics_aggregation_fn(fit_metrics)
        elif server_round == 1:  # Only log this warning once
            log(WARNING, "No fit_metrics_aggregation_fn provided")

        return parameters_aggregated, metrics_aggregated

We can now run our custom serialization example!

def server_fn(context: Context) -> ServerAppComponents:
    # Configure the server for just 3 rounds of training
    config = ServerConfig(num_rounds=3)
    return ServerAppComponents(
        config=config,
        strategy=FedSparse(),  # <-- pass the new strategy here
    )


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

# Run simulation
run_simulation(
    server_app=server,
    client_app=client,
    num_supernodes=NUM_PARTITIONS,
    backend_config=backend_config,
)

Recap

In this part of the tutorial, we’ve seen how we can build clients by subclassing either NumPyClient or Client. NumPyClient is a convenience abstraction that makes it easier to work with machine learning libraries that have good NumPy interoperability. Client is a more flexible abstraction that allows us to do things that are not possible in NumPyClient. In order to do so, it requires us to handle parameter serialization and deserialization ourselves.

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

This is the final part of the Flower tutorial (for now!), congratulations! You’re now well equipped to understand the rest of the documentation. There are many topics we didn’t cover in the tutorial, we recommend the following resources:

• Read Flower Docs

• Check out Flower Code Examples

• Use Flower Baselines for your research

• Watch Flower AI Summit 2024 videos