def _assert_server_update_with_all_ones(self, model_fn):
optimizer_fn = lambda: tf.keras.optimizers.SGD(learning_rate=0.1)
model = tf.keras.models.Sequential([
tf.keras.layers.Input(shape=(784,)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(units=10, kernel_initializer='zeros'),
tf.keras.layers.Softmax(),
])
optimizer = optimizer_fn()
state, optimizer_vars = server_init(model, optimizer)
weights_delta = tf.nest.map_structure(
tf.ones_like,
attacked_fedavg._get_weights(model).trainable)
for _ in range(2):
state = attacked_fedavg.server_update(model, optimizer, optimizer_vars,
state, weights_delta, ())
model_vars = self.evaluate(state.model)
train_vars = model_vars.trainable
# weights are initialized with all-zeros, weights_delta is all ones,
# SGD learning rate is 0.1. Updating server for 2 steps.
values = list(train_vars.values())
self.assertAllClose(
values, [np.ones_like(values[0]) * 0.2,
np.ones_like(values[1]) * 0.2])
def _assert_server_update_with_all_ones(self, model_fn):
optimizer_fn = lambda: tf.keras.optimizers.SGD(learning_rate=0.1)
model = model_fn()
optimizer = optimizer_fn()
state, optimizer_vars = server_init(model, optimizer)
weights_delta = tf.nest.map_structure(
tf.ones_like,
attacked_fedavg._get_weights(model).trainable)
for _ in range(2):
state = attacked_fedavg.server_update(model, optimizer,
optimizer_vars, state,
weights_delta, ())
model_vars = self.evaluate(state.model)
train_vars = model_vars.trainable
self.assertLen(train_vars, 2)
# weights are initialized with all-zeros, weights_delta is all ones,
# SGD learning rate is 0.1. Updating server for 2 steps.
values = list(train_vars.values())
self.assertAllClose(
values,
[np.ones_like(values[0]) * 0.2,
np.ones_like(values[1]) * 0.2])