def run_with_num_hosts(self, num_hosts):
_, guest, *hosts = self.run_results(num_hosts)
expert = OrderDictWeights(guest[1]) * guest[2]
total_weights = guest[2]
aggregated = [guest[0]]
for host in hosts:
expert += OrderDictWeights(host[1]) * host[2]
total_weights += host[2]
aggregated.append(host[0])
expert /= total_weights
expert = expert.unboxed
aggregated = [w.unboxed for w in aggregated]
for k in expert:
for w in aggregated:
self.assertAlmostEqual(np.linalg.norm(expert[k] - w[k]), 0.0)
def secure_aggregator_call(job_id, role, ind, *args):
if role == consts.ARBITER:
agg = secure_mean_aggregator.Server()
model = agg.weighted_mean_model()
agg.send_aggregated_model(model)
else:
agg = secure_mean_aggregator.Client()
# disorder dit
order = list(range(5))
np.random.seed(random.SystemRandom().randint(1, 100))
np.random.shuffle(order)
raw = {k: np.random.rand(10, 10) for k in order}
w = OrderDictWeights(copy.deepcopy(raw))
d = random.random()
agg.send_weighted_model(w, weight=d)
aggregated = agg.get_aggregated_model()
return aggregated, raw, d
def call(cls, role, transfer_variable, ind, *args):
agg = aggregator.with_role(role,
transfer_variable,
enable_secure_aggregate=True)
if role == consts.ARBITER:
agg.aggregate_and_broadcast()
print(agg.aggregate_loss())
else:
# disorder dit
order = list(range(5))
np.random.seed(random.SystemRandom().randint(1, 100))
np.random.shuffle(order)
raw = {k: np.random.rand(10, 10) for k in order}
w = OrderDictWeights(copy.deepcopy(raw))
d = random.random()
aggregated = agg.aggregate_then_get(w, degree=d)
agg.send_loss(2.0)
return aggregated, raw, d
def get_model_weights(self) -> OrderDictWeights:
return OrderDictWeights(self._model.state_dict())
def get_model_weights(self) -> OrderDictWeights:
return OrderDictWeights(self._sess.run(self._trainable_weights))
def get_model_weights(self) -> OrderDictWeights:
"""
Return model's weights as OrderDictWeights.
:return: model's weights.
"""
return OrderDictWeights(self.session.run(self._aggregate_weights))
def get_model_weights(self) -> OrderDictWeights:
return OrderDictWeights(self.session.run(self._aggregate_weights))