example_dataset = emnist_train.create_tf_dataset_for_client( emnist_train.client_ids[0])如何将该段代码修改为使用本地的fashion_mnist数据

集呢？

您可以尝试使用以下代码将 `example_dataset` 进行修改以使用本地的 fashion_mnist 数据集:

import tensorflow_federated as tff
from tensorflow import keras

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

def preprocess(dataset):
  def batch_format_fn(element):
    return (tf.reshape(element['pixels'],[-1,784]),tf.reshape(element['label'],[-1,1]))
  return dataset.repeat(NUM_EPOCHS).map(batch_format_fn).shuffle(SHUFFLE_BUFFER).batch(BATCH_SIZE)

def make_federated_data(client_data, client_ids):
  return [preprocess(client_data.create_tf_dataset_for_client(client_id)) for client_id in client_ids]

emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data()

fashion_mnist_train, fashion_mnist_test = fashion_mnist.load_data()
fashion_mnist_train_client = tff.simulation.ClientData.from_tensor_slices(
    {'pixels': train_images, 'label': train_labels})
fashion_mnist_test_client = tff.simulation.ClientData.from_tensor_slices(
    {'pixels': test_images, 'label': test_labels})

federated_train_data_fashion_mnist = make_federated_data(fashion_mnist_train_client,
                                               fashion_mnist_train_client.client_ids)

example_dataset = federated_train_data_fashion_mnist[0]

这段代码将使用 `tff.simulation.datasets.emnist.load_data()` 加载 EMNIST 数据集，并使用 `fashion_mnist.load_data()` 加载 fashion_mnist 数据集。然后，它将为 `fashion_mnist` 创建 `ClientData` 对象，然后将 `ClientData` 对象转换为可以在 TFF 训练环境中使用的联合数据集。最后，它将 `example_dataset` 赋值给第一个 "fashion_mnist" 客户端的数据集。