使用联邦学习策略

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! 🌼

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 .

策略定制

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.

从定制战略开始

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 .

服务器端参数**评估**

Flower 可以在服务器端或客户端评估聚合模型。客户端和服务器端评估在某些方面相似，但也有不同之处。

**集中评估**（或*服务器端评估*）在概念上很简单：它的工作方式与集中式机器学习中的评估方式相同。如果有一个服务器端数据集可用于评估目的，那就太好了。我们可以在每一轮训练后对新聚合的模型进行评估，而无需将模型发送给客户端。我们也很幸运，因为我们的整个评估数据集随时可用。

联邦评估**（或*客户端评估*）更为复杂，但也更为强大：它不需要集中的数据集，允许我们在更大的数据集上对模型进行评估，这通常会产生更真实的评估结果。事实上，如果我们想得到有代表性的评估结果，很多情况下都需要使用**联邦评估。但是，这种能力是有代价的：一旦我们开始在客户端进行评估，我们就应该意识到，如果这些客户端并不总是可用，我们的评估数据集可能会在连续几轮学习中发生变化。此外，每个客户端所拥有的数据集也可能在连续几轮学习中发生变化。这可能会导致评估结果不稳定，因此即使我们不改变模型，也会看到评估结果在连续几轮中波动。

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 .

我们可以看到，客户端日志现在包含了当前一轮的联邦学习（从 config 字典中读取）。我们还可以将本地训练配置为在第一轮和第二轮联邦学习期间运行一个遍历，然后在第三轮联邦学习期间运行两个遍历。

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.

扩大联邦学习的规模

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 .

回顾

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!

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!

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