def secure_mean(collected_inputs):
""" securely calculates the mean of the collected_inputs """
with tf.name_scope('secure_mean'):
aggr_inputs = [
tfe.add_n(inputs) / len(inputs) for inputs in collected_inputs
]
# Reveal aggregated values & cast to native tf.float32
aggr_inputs = [
tf.cast(inp.reveal().to_native(), tf.float32)
for inp in aggr_inputs
]
return aggr_inputs
def fit(self, training_players, summary=0, validation_split=None):
"""Trains the linear regressor.
Arguments:
training_players: Data owners used for joint training. Must implement the
compute_estimators as a tfe.local_computation.
summary: Controls what kind of summary statistics are generated after the
linear regression fit.
validation_split: Mimics the behavior of the Keras validation_split kwarg.
"""
if validation_split is not None:
raise NotImplementedError()
partial_estimators = [
player.compute_estimators(self.estimator_fn)
for player in training_players
]
for attr, partial_estimator in zip(self.components,
zip(*partial_estimators)):
setattr(self, attr, tfe.add_n(partial_estimator))
with tfe.Session() as sess:
for k in self.components:
op = getattr(self, k)
setattr(self, k, sess.run(op.reveal()))
tf_graph = tf.Graph()
with tf_graph.as_default():
self._inverted_covariate_square = tf.linalg.inv(
self.covariate_square)
self.coefficients = tf.matmul(self._inverted_covariate_square,
self.covariate_label_product)
with tf.Session(graph=tf_graph) as sess:
for k in ["_inverted_covariate_square", "coefficients"]:
setattr(self, k, sess.run(getattr(self, k)))
if not summary:
return self
return self.summarize(summary_level=summary)
def provide_input() -> tf.Tensor:
# pick random tensor to be averaged
return tf.random_normal(shape=(10, ))
if __name__ == '__main__':
# get input from inputters as private values
inputs = [
tfe.define_private_input('inputter-0', provide_input),
tfe.define_private_input('inputter-1', provide_input),
tfe.define_private_input('inputter-2', provide_input),
tfe.define_private_input('inputter-3', provide_input),
tfe.define_private_input('inputter-4', provide_input),
]
# sum all inputs and divide by count
result = tfe.add_n(inputs) / len(inputs)
def receive_output(average: tf.Tensor) -> tf.Operation:
# simply print average
return tf.print("Average:", average)
# send result to receiver
result_op = tfe.define_output('result-receiver', result, receive_output)
# run a few times
with tfe.Session() as sess:
sess.run(result_op, tag='average')
DataOwner("data-owner-0", "./data/train.tfrecord",
model_owner.build_update_step),
DataOwner("data-owner-1", "./data/train.tfrecord",
model_owner.build_update_step),
DataOwner("data-owner-2", "./data/train.tfrecord",
model_owner.build_update_step),
]
model_grads = zip(*(
data_owner.compute_gradient()
for data_owner in data_owners
))
with tf.name_scope('secure_aggregation'):
aggregated_model_grads = [
tfe.add_n(grads) / len(grads)
for grads in model_grads
]
iteration_op = model_owner.update_model(*aggregated_model_grads)
with tfe.Session(target=session_target) as sess:
sess.run(tf.global_variables_initializer(), tag='init')
for i in range(model_owner.ITERATIONS):
if i % 100 == 0:
print("Iteration {}".format(i))
sess.run(iteration_op, tag='iteration')
else:
sess.run(iteration_op)
data_owners = [
DataOwner("data-owner-0", "./data/train.tfrecord",
model_owner.build_update_step),
DataOwner("data-owner-1", "./data/train.tfrecord",
model_owner.build_update_step),
DataOwner("data-owner-2", "./data/train.tfrecord",
model_owner.build_update_step),
]
model_grads = zip(*(tfe.define_private_input(data_owner.player_name,
data_owner.compute_gradient)
for data_owner in data_owners))
with tf.name_scope('secure_aggregation'):
aggregated_model_grads = [
tfe.add_n(grads) / len(grads) for grads in model_grads
]
iteration_op = tfe.define_output(model_owner.player_name,
aggregated_model_grads,
model_owner.update_model)
with tfe.Session(target=session_target) as sess:
sess.run(tf.global_variables_initializer(), tag='init')
for i in range(model_owner.ITERATIONS):
if i % 100 == 0:
print("Iteration {}".format(i))
sess.run(iteration_op, tag='iteration')
else:
sess.run(iteration_op)