def test_secure_aggregator(self):
aggregator = model_update_aggregator.secure_aggregator().create(
_float_matrix_type, _float_type)
mrf = self._check_aggregated_scalar_count(aggregator, 60000 * 1.01, 60000)
# The MapReduceForm should be using secure aggregation.
self.assertTrue(mrf.securely_aggregates_tensors)
def test_secure_aggregator(self, zeroing, clipping):
factory_ = model_update_aggregator.secure_aggregator(zeroing, clipping)
self.assertIsInstance(factory_, factory.WeightedAggregationFactory)
process = factory_.create(_float_type, _float_type)
self.assertIsInstance(process, aggregation_process.AggregationProcess)
self.assertLen(process.next.type_signature.parameter, 3)
def test_secure_aggregator_unweighted(self, zeroing, clipping):
factory_ = model_update_aggregator.secure_aggregator(
zeroing=zeroing, clipping=clipping, weighted=False)
self.assertIsInstance(factory_, factory.UnweightedAggregationFactory)
process = factory_.create(_float_type)
self.assertIsInstance(process, aggregation_process.AggregationProcess)
self.assertFalse(process.is_weighted)
def test_weighted_secure_aggregator_only_contains_secure_aggregation(self):
aggregator = model_update_aggregator.secure_aggregator(
weighted=True).create(_float_matrix_type, _float_type)
try:
static_assert.assert_not_contains_unsecure_aggregation(
aggregator.next)
except: # pylint: disable=bare-except
self.fail('Secure aggregator contains non-secure aggregation.')
def test_no_unsecure_aggregation_with_secure_aggregator(self):
model_fn = model_examples.LinearRegression
learning_process = fed_sgd.build_fed_sgd(
model_fn,
model_aggregator=model_update_aggregator.secure_aggregator(),
metrics_aggregator=aggregator.secure_sum_then_finalize)
static_assert.assert_not_contains_unsecure_aggregation(
learning_process.next)
def test_unweighted_mime_lite_with_only_secure_aggregation(self):
aggregator = model_update_aggregator.secure_aggregator(weighted=False)
learning_process = mime.build_unweighted_mime_lite(
model_examples.LinearRegression,
base_optimizer=sgdm.build_sgdm(learning_rate=0.01, momentum=0.9),
model_aggregator=aggregator,
full_gradient_aggregator=aggregator,
metrics_aggregator=metrics_aggregator.secure_sum_then_finalize)
static_assert.assert_not_contains_unsecure_aggregation(
learning_process.next)
def test_unweighted_fed_avg_with_only_secure_aggregation(self):
model_fn = model_examples.LinearRegression
learning_process = fed_avg.build_unweighted_fed_avg(
model_fn,
client_optimizer_fn=lambda: tf.keras.optimizers.SGD(1.0),
model_aggregator=model_update_aggregator.secure_aggregator(
weighted=False),
metrics_aggregator=aggregator.secure_sum_then_finalize)
static_assert.assert_not_contains_unsecure_aggregation(
learning_process.next)
def test_construction_with_only_secure_aggregation(self):
model_fn = model_examples.LinearRegression
learning_process = fed_avg_with_optimizer_schedule.build_weighted_fed_avg_with_optimizer_schedule(
model_fn,
client_learning_rate_fn=lambda x: 0.5,
client_optimizer_fn=tf.keras.optimizers.SGD,
model_aggregator=model_update_aggregator.secure_aggregator(
weighted=True),
metrics_aggregator=aggregator.secure_sum_then_finalize)
static_assert.assert_not_contains_unsecure_aggregation(
learning_process.next)
def test_weighted_fed_avg_with_only_secure_aggregation(self):
model = test_models.build_functional_linear_regression()
learning_process = fed_avg.build_weighted_fed_avg(
model_fn=None,
model=model,
client_optimizer_fn=sgdm.build_sgdm(learning_rate=0.1),
model_aggregator=model_update_aggregator.secure_aggregator(
weighted=True),
metrics_aggregator=aggregator.secure_sum_then_finalize)
static_assert.assert_not_contains_unsecure_aggregation(
learning_process.next)
