联邦学习评估

评估联合学习系统中的模型主要有两种方法：集中（或服务器端）评估和联邦（或客户端）评估。

集中评估

内置策略

所有内置策略都通过在初始化过程中提供一个评估函数来支持集中评估。评估函数是任何可以将当前全局模型参数作为输入并返回评估结果的函数：

from flwr.common import Context, NDArrays, Scalar
from flwr.server import ServerApp, ServerAppComponents, ServerConfig
from flwr.server.strategy import FedAvg

from typing import Dict, Optional, Tuple


def get_evaluate_fn(model):
    """Return an evaluation function for server-side evaluation."""

    # Load data and model here to avoid the overhead of doing it in `evaluate` itself
    (x_train, y_train), _ = tf.keras.datasets.cifar10.load_data()

    # Use the last 5k training examples as a validation set
    x_val, y_val = x_train[45000:50000], y_train[45000:50000]

    # The `evaluate` function will be called after every round
    def evaluate(
        server_round: int, parameters: NDArrays, config: Dict[str, Scalar]
    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
        model.set_weights(parameters)  # Update model with the latest parameters
        loss, accuracy = model.evaluate(x_val, y_val)
        return loss, {"accuracy": accuracy}

    return evaluate


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

    # Load and compile model for server-side parameter evaluation
    model = tf.keras.applications.EfficientNetB0(
        input_shape=(32, 32, 3), weights=None, classes=10
    )
    model.compile("adam", "sparse_categorical_crossentropy", metrics=["accuracy"])

    # Create strategy
    strategy = FedAvg(
        # ... other FedAvg arguments
        evaluate_fn=get_evaluate_fn(model),
    )

    return ServerAppComponents(strategy=strategy, config=config)


# Create ServerApp
app = ServerApp(server_fn=server_fn)

定制策略

The Strategy abstraction provides a method called evaluate that can directly be used to evaluate the current global model parameters. The current server implementation calls evaluate after parameter aggregation and before federated evaluation (see next paragraph).

联邦评估

实现联邦评估

Client-side evaluation happens in the Client.evaluate method and can be configured from the server side.

from flwr.client import NumPyClient


class FlowerClient(NumPyClient):
    def __init__(self, model, x_train, y_train, x_test, y_test):
        self.model = model
        self.x_train, self.y_train = x_train, y_train
        self.x_test, self.y_test = x_test, y_test

    def fit(self, parameters, config):
        # ...
        pass

    def evaluate(self, parameters, config):
        """Evaluate parameters on the locally held test set."""

        # Update local model with global parameters
        self.model.set_weights(parameters)

        # Get config values
        steps: int = config["val_steps"]

        # Evaluate global model parameters on the local test data and return results
        loss, accuracy = self.model.evaluate(self.x_test, self.y_test, 32, steps=steps)
        num_examples_test = len(self.x_test)
        return loss, num_examples_test, {"accuracy": accuracy}

配置联邦评估

联邦评估可从服务器端进行配置。内置策略支持以下参数：

  • fraction_evaluate: a float defining the fraction of clients that will be selected for evaluation. If fraction_evaluate is set to 0.1 and 100 clients are connected to the server, then 10 will be randomly selected for evaluation. If fraction_evaluate is set to 0.0, federated evaluation will be disabled.

  • min_evaluate_clients: an int: the minimum number of clients to be selected for evaluation. If fraction_evaluate is set to 0.1, min_evaluate_clients is set to 20, and 100 clients are connected to the server, then 20 clients will be selected for evaluation.

  • min_available_clients: an int that defines the minimum number of clients which need to be connected to the server before a round of federated evaluation can start. If fewer than min_available_clients are connected to the server, the server will wait until more clients are connected before it continues to sample clients for evaluation.

  • on_evaluate_config_fn: a function that returns a configuration dictionary which will be sent to the selected clients. The function will be called during each round and provides a convenient way to customize client-side evaluation from the server side, for example, to configure the number of validation steps performed.

from flwr.common import Context
from flwr.server import ServerApp, ServerAppComponents, ServerConfig
from flwr.server.strategy import FedAvg


def evaluate_config(server_round: int):
    """Return evaluation configuration dict for each round.
    Perform five local evaluation steps on each client (i.e., use five
    batches) during rounds, one to three, then increase to ten local
    evaluation steps.
    """
    val_steps = 5 if server_round < 4 else 10
    return {"val_steps": val_steps}


# Create strategy
strategy = FedAvg(
    # ... other FedAvg arguments
    fraction_evaluate=0.2,
    min_evaluate_clients=2,
    min_available_clients=10,
    on_evaluate_config_fn=evaluate_config,
)


def server_fn(context: Context):
    num_rounds = context.run_config["num-server-rounds"]
    config = ServerConfig(num_rounds=num_rounds)
    return ServerAppComponents(strategy=strategy, config=config)


# Create ServerApp
app = ServerApp(server_fn=server_fn)

评估训练期间的本地模型更新

Model parameters can also be evaluated during training. Client.fit can return arbitrary evaluation results as a dictionary:

from flwr.client import NumPyClient


class FlowerClient(NumPyClient):
    def __init__(self, model, x_train, y_train, x_test, y_test):
        self.model = model
        self.x_train, self.y_train = x_train, y_train
        self.x_test, self.y_test = x_test, y_test

    def fit(self, parameters, config):
        """Train parameters on the locally held training set."""

        # Update local model parameters
        self.model.set_weights(parameters)

        # Train the model using hyperparameters from config
        history = self.model.fit(
            self.x_train, self.y_train, batch_size=32, epochs=2, validation_split=0.1
        )

        # Return updated model parameters and validation results
        parameters_prime = self.model.get_weights()
        num_examples_train = len(self.x_train)
        results = {
            "loss": history.history["loss"][0],
            "accuracy": history.history["accuracy"][0],
            "val_loss": history.history["val_loss"][0],
            "val_accuracy": history.history["val_accuracy"][0],
        }
        return parameters_prime, num_examples_train, results

    def evaluate(self, parameters, config):
        # ...
        pass

完整代码示例

For a full code example that uses both centralized and federated evaluation, see the Advanced TensorFlow Example (the same approach can be applied to workloads implemented in any other framework).