tensorflow_federated手写数字识别mnist数据集联邦学习代码

以下是使用TensorFlow Federated进行手写数字识别MNIST数据集联邦学习的代码：

import tensorflow as tf
import tensorflow_federated as tff

# 加载MNIST数据集
mnist_train, mnist_test = tf.keras.datasets.mnist.load_data()

# 定义模型
def create_keras_model():
    return tf.keras.models.Sequential([
        tf.keras.layers.Input(shape=(28, 28)),
        tf.keras.layers.Reshape(target_shape=(28, 28, 1)),
        tf.keras.layers.Conv2D(32, 3, activation='relu'),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(10, activation='softmax')
    ])

# 定义TFF模型
def model_fn():
    keras_model = create_keras_model()
    return tff.learning.from_keras_model(
        keras_model,
        input_spec=mnist_train[0].shape,
        loss=tf.keras.losses.SparseCategoricalCrossentropy(),
        metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])

# 定义TFF联邦平均算法
iterative_process = tff.learning.build_federated_averaging_process(
    model_fn,
    client_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=0.02),
    server_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=1.0))

# 训练模型
state = iterative_process.initialize()
for round_num in range(10):
    state, metrics = iterative_process.next(state, [mnist_train])
    print('round {:2d}, metrics={}'.format(round_num, metrics))

# 评估模型
evaluation = tff.learning.build_federated_evaluation(model_fn)
test_metrics = evaluation(state.model, [mnist_test])
print('Test metrics: {}'.format(test_metrics))