message: Create 2NN model (as in the original federated learning paper)
diff: @@ -40,7 +40,7 @@ def fc_compiled(input_shape=(28, 28, 1), num_classes=10) -> tf.keras.Model:
  def cnn_compiled(input_shape=(28, 28, 1), num_classes=10) -> tf.keras.Model:
  inputs = Input(shape=input_shape)
  x = Conv2D(32, kernel_size=3, activation="relu")(inputs)
  - x = Conv2D(64, (3, 3), activation="relu")(x)
  + x = Conv2D(64, kernel_size=3, activation="relu")(x)
  x = MaxPool2D(pool_size=(2, 2))(x)
  x = Dropout(0.25)(x)
  x = Flatten()(x)
  @@ -56,3 +56,20 @@ def cnn_compiled(input_shape=(28, 28, 1), num_classes=10) -> tf.keras.Model:
  metrics=["accuracy"],
  )
  return model
  +
  +
  +def fl_2nn_compiled(input_shape=(28, 28, 1), num_classes=10) -> tf.keras.Model:
  + inputs = Input(shape=input_shape)
  + x = Flatten()(inputs)
  + x = Dense(200, activation="relu")(x)
  + x = Dense(200, activation="relu")(x)
  + outputs = Dense(num_classes, activation="softmax")(x)
  +
  + model = tf.keras.Model(inputs=inputs, outputs=outputs)
  +
  + model.compile(
  + loss=tf.keras.losses.categorical_crossentropy,
  + optimizer=tf.keras.optimizers.Adam(),
  + metrics=["accuracy"],
  + )
  + return model
  