def test_attack(self):
"""Test whether an attacker is doing the right attack."""
def model_fn():
return tff.learning.from_compiled_keras_model(
tff.simulation.models.mnist.create_simple_keras_model(),
sample_batch)
client_data = create_client_data()
batch = client_data()
train_data = [batch]
malicious_data = [batch]
client_type_list = [tf.constant(True)]
sample_batch = self.evaluate(next(iter(train_data[0])))
trainer = build_federated_averaging_process_attacked(
model_fn,
client_update_tf=attacked_fedavg.ClientExplicitBoosting(
boost_factor=-1.0))
state = trainer.initialize()
initial_weights = state.model.trainable
for _ in range(2):
state, _ = trainer.next(state, train_data, malicious_data,
client_type_list)
self.assertAllClose(initial_weights, state.model.trainable)
def test_self_contained_example(self):
client_data = create_client_data()
model = MnistModel()
optimizer = tf.keras.optimizers.SGD(0.1)
losses = []
client_update = attacked_fedavg.ClientExplicitBoosting(boost_factor=1.0)
for _ in range(2):
outputs = client_update(model, optimizer, client_data(), client_data(),
tf.constant(False),
attacked_fedavg._get_weights(model))
losses.append(outputs.model_output['loss'].numpy())
self.assertAllEqual(outputs.optimizer_output['num_examples'].numpy(), 2)
self.assertLess(losses[1], losses[0])
def test_attack(self):
"""Test whether an attacker is doing the right attack."""
client_data = create_client_data()
batch = client_data()
train_data = [batch]
malicious_data = [batch]
client_type_list = [tf.constant(True)]
trainer = build_federated_averaging_process_attacked(
_model_fn,
client_update_tf=attacked_fedavg.ClientExplicitBoosting(
boost_factor=-1.0))
state = trainer.initialize()
initial_weights = state.model.trainable
for _ in range(2):
state, _ = trainer.next(state, train_data, malicious_data,
client_type_list)
self.assertAllClose(initial_weights, state.model.trainable)
def test_attack(self):
"""Test whether an attacker is doing the right attack."""
self.skipTest('b/150215351 This test became flaky after TF change which '
'removed variable reads from control_outputs.')
client_data = create_client_data()
batch = client_data()
train_data = [batch]
malicious_data = [batch]
client_type_list = [tf.constant(True)]
trainer = build_federated_averaging_process_attacked(
_model_fn,
client_update_tf=attacked_fedavg.ClientExplicitBoosting(
boost_factor=-1.0))
state = trainer.initialize()
initial_weights = state.model.trainable
for _ in range(2):
state, _ = trainer.next(state, train_data, malicious_data,
client_type_list)
self.assertAllClose(initial_weights, state.model.trainable)