自定义客户端

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 final tutorial, 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 that 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! 🌼

准备工作

在开始实际代码之前，让我们先确保我们已经准备好了所需的一切。

安装依赖项

Note

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 .

Revisiting NumPyClient

So far, we've implemented our client by subclassing flwr.client.NumPyClient. The two methods that were implemented in client_app.py are fit and evaluate.

class FlowerClient(NumPyClient):
    def __init__(self, net, trainloader, valloader, local_epochs):
        self.net = net
        self.trainloader = trainloader
        self.valloader = valloader
        self.local_epochs = local_epochs
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        self.net.to(self.device)

    def fit(self, parameters, config):
        set_weights(self.net, parameters)
        train_loss = train(
            self.net,
            self.trainloader,
            self.local_epochs,
            self.device,
        )
        return (
            get_weights(self.net),
            len(self.trainloader.dataset),
            {"train_loss": train_loss},
        )

    def evaluate(self, parameters, config):
        set_weights(self.net, parameters)
        loss, accuracy = test(self.net, self.valloader, self.device)
        return loss, len(self.valloader.dataset), {"accuracy": accuracy}

Then, we have the function client_fn that is used by Flower to create the FlowerClient instances on demand. Finally, we create the ClientApp and pass the client_fn to it.

def client_fn(context: Context):
    # Load model and data
    net = Net()
    partition_id = context.node_config["partition-id"]
    num_partitions = context.node_config["num-partitions"]
    trainloader, valloader = load_data(partition_id, num_partitions)
    local_epochs = context.run_config["local-epochs"]

    # Return Client instance
    return FlowerClient(net, trainloader, valloader, local_epochs).to_client()


# Flower ClientApp
app = ClientApp(
    client_fn,
)

We've seen this before, there's nothing new so far. Next, in server_app.py, the number of federated learning rounds are preconfigured in the ServerConfig and in the same module, the ServerApp is created with this 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)

    # Define strategy
    strategy = FedAvg(
        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)


# Create ServerApp
app = ServerApp(server_fn=server_fn)

Finally, we run the simulation to see the output we get:

$ flwr run .

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, under the hood, Flower launches the ClientApp object which in turn calls the function client_fn to create an instance of our FlowerClient (along with loading the model and the data).

But here's the perhaps surprising part: Flower doesn't actually use the FlowerClient 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.

与其在 NumPyClient 上构建，我们可以直接在 Client 上构建。

Moving from NumPyClient to Client

Let's try to do the same thing using Client instead of NumPyClient. Create a new file called custom_client_app.py and copy the following code into it:

from typing import List

import numpy as np
import torch
from flwr.client import Client, ClientApp
from flwr.common import (
    Code,
    Context,
    EvaluateIns,
    EvaluateRes,
    FitIns,
    FitRes,
    GetParametersIns,
    GetParametersRes,
    Status,
    ndarrays_to_parameters,
    parameters_to_ndarrays,
)

from flower_tutorial.task import Net, get_weights, load_data, set_weights, test, train


class FlowerClient(Client):
    def __init__(self, partition_id, net, trainloader, valloader, local_epochs):
        self.partition_id = partition_id
        self.net = net
        self.trainloader = trainloader
        self.valloader = valloader
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        self.local_epochs = local_epochs

    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_weights(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_weights(self.net, ndarrays_original)
        train(self.net, self.trainloader, self.local_epochs, self.device)
        ndarrays_updated = get_weights(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_weights(self.net, ndarrays_original)
        loss, accuracy = test(self.net, self.valloader, self.device)

        # 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()
    partition_id = context.node_config["partition-id"]
    num_partitions = context.node_config["num-partitions"]
    local_epochs = context.run_config["local-epochs"]
    trainloader, valloader = load_data(partition_id, num_partitions)
    return FlowerClient(
        partition_id, net, trainloader, valloader, local_epochs
    ).to_client()


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

Next, we update the pyproject.toml so that Flower uses the new module:

[tool.flwr.app.components]
serverapp = "flower_tutorial.server_app:app"
clientapp = "flower_tutorial.custom_client_app:app"

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

$ flwr run .

就是这样，我们现在开始使用 Client。它看起来可能与我们使用 NumPyClient 所做的类似。那么有什么不同呢？

首先，它的代码更多。但为什么呢？区别在于 Client 希望我们处理参数的序列化和反序列化。Flower 要想通过网络发送参数，最终需要将这些参数转化为 字节。把参数（例如 NumPy 的 ndarray 参数）变成原始字节叫做序列化。将原始字节转换成更有用的东西（如 NumPy ``ndarray`）称为反序列化。Flower 需要同时做这两件事：它需要在服务器端序列化参数并将其发送到客户端，客户端需要反序列化参数以便将其用于本地训练，然后再次序列化更新后的参数并将其发送回服务器，服务器（最后）再次反序列化参数以便将其与从其他客户端接收到的更新汇总在一起。

Client 与 NumPyClient 之间的唯一**真正区别在于，NumPyClient 会为你处理序列化和反序列化。NumPyClient之所以能做到这一点，是因为它预计你会以NumPy ndarray的形式返回参数，而且它知道如何处理这些参数。这使得与具有良好 NumPy 支持的大多数机器学习库一起工作变得轻而易举。

在 API 方面，有一个主要区别：Client 中的所有方法都只接受一个参数（例如，Client.fit 中的 FitIns），并只返回一个值（例如，Client.fit 中的 FitRes）。另一方面，NumPyClient``中的方法有多个参数（例如，``NumPyClient.fit``中的``parameters``和``config）和多个返回值（例如，NumPyClient.fit``中的``parameters、num_example``和``metrics）。在 Client 中的这些 *Ins 和 *Res 对象封装了你在 NumPyClient 中习惯使用的所有单个值。

Custom serialization

下面我们将通过一个简单的示例来探讨如何实现自定义序列化。

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 send a Python object through the internet.

通过在客户端和服务器之间来回发送 Python 对象，联合学习在很大程度上依赖于互联网通信进行训练。这意味着序列化是联邦学习的重要组成部分。

在下面的章节中，我们将编写一个基本示例，在发送包含参数的 ndarray 前，我们将首先把 ndarray 转换为稀疏矩阵，而不是发送序列化版本。这种技术可以用来节省带宽，因为在某些情况下，模型的参数是稀疏的（包含许多 0 条目），将它们转换成稀疏矩阵可以大大提高它们的字节数。

我们的定制序列化/反序列化功能

This is where the real serialization/deserialization will happen, especially in ndarray_to_sparse_bytes for serialization and sparse_bytes_to_ndarray for deserialization. First we add the following code to task.py:

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)

客户端

为了能够将我们的 ndarray 序列化为稀疏参数，我们只需在 flwr.client.Client 中调用我们的自定义函数。

事实上，在 get_parameters 中，我们需要使用上文定义的自定义 ndarrays_too_sparse_parameters 序列化从网络中获取的参数。

在 fit 中，我们首先需要使用自定义的 sparse_parameters_to_ndarrays 反序列化来自服务器的参数，然后使用 ndarrays_to_sparse_parameters 序列化本地结果。

In evaluate, we will only need to deserialize the global parameters with our custom function. In a new file called serde_client_app.py, copy the following code into it:

from typing import List

import numpy as np
import torch
from flwr.client import Client, ClientApp
from flwr.common import (
    Code,
    Context,
    EvaluateIns,
    EvaluateRes,
    FitIns,
    FitRes,
    GetParametersIns,
    GetParametersRes,
    Status,
)

from flower_tutorial.task import (
    Net,
    get_weights,
    load_data,
    ndarrays_to_sparse_parameters,
    set_weights,
    sparse_parameters_to_ndarrays,
    test,
    train,
)


class FlowerClient(Client):
    def __init__(self, partition_id, net, trainloader, valloader, local_epochs):
        self.partition_id = partition_id
        self.net = net
        self.trainloader = trainloader
        self.valloader = valloader
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        self.local_epochs = local_epochs

    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_weights(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_weights(self.net, ndarrays_original)
        train(self.net, self.trainloader, self.local_epochs, self.device)
        ndarrays_updated = get_weights(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_weights(self.net, ndarrays_original)
        loss, accuracy = test(self.net, self.valloader, self.device)

        # 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()
    partition_id = context.node_config["partition-id"]
    num_partitions = context.node_config["num-partitions"]
    local_epochs = context.run_config["local-epochs"]
    trainloader, valloader = load_data(partition_id, num_partitions)
    return FlowerClient(
        partition_id, net, trainloader, valloader, local_epochs
    ).to_client()


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

服务器端

在本例中，我们将只使用 FedAvg 作为策略。要改变这里的序列化和反序列化，我们只需重新实现 FedAvg 的 evaluate 和 aggregate_fit 函数。策略的其他函数将从超类 FedAvg 继承。

正如你所看到的，``evaluate``中只修改了一行：

parameters_ndarrays = sparse_parameters_to_ndarrays(parameters)

而对于 aggregate_fit，我们将首先反序列化收到的每个结果：

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

然后将汇总结果序列化：

parameters_aggregated = ndarrays_to_sparse_parameters(aggregate(weights_results))

In a new file called strategy.py, copy the following code into it:

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

from flower_tutorial.task import (
    ndarrays_to_sparse_parameters,
    sparse_parameters_to_ndarrays,
)

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 import our new FedSparse strategy into server_app.py and update our server_fn to use it:

from flower_tutorial.strategy import FedSparse


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

    return ServerAppComponents(
        strategy=FedSparse(), config=config  # <-- pass the new strategy here
    )


# Create ServerApp
app = ServerApp(server_fn=server_fn)

Finally, we run the simulation.

$ flwr run .

回顾

在本部分教程中，我们已经了解了如何通过子类化 NumPyClient 或 Client 来构建客户端。NumPyClient "是一个便捷的抽象类，可以让我们更容易地与具有良好NumPy互操作性的机器学习库一起工作。``Client``是一个更灵活的抽象类，允许我们做一些在`NumPyClient``中做不到的事情。为此，它要求我们自己处理参数序列化和反序列化。

Note

If you'd like to follow along with tutorial notebooks, check out the Tutorial notebooks. Note that the notebooks use the run_simulation approach, whereas the recommended way to run simulations in Flower is using the flwr run approach as shown in this tutorial.

接下来的步骤

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!

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