def __init__(
self,
trans_var: SecureAggregatorTransVar = SecureAggregatorTransVar(),
enable_secure_aggregate=True):
self.enable_secure_aggregate = enable_secure_aggregate
self._aggregator = aggregator.Client(
trans_var=trans_var.aggregator_trans_var)
if enable_secure_aggregate:
self._random_padding_cipher: PadsCipher = \
random_padding_cipher.Client(trans_var=trans_var.random_padding_cipher_trans_var).create_cipher()
def __init__(self, max_num_aggregation, aggregate_every_n_epoch, name="default"):
super(PyTorchSAClientContext, self).__init__(
max_num_aggregation=max_num_aggregation, name=name
)
self.transfer_variable = SecureAggregatorTransVar()
self.aggregator = aggregator.Client(self.transfer_variable.aggregator_trans_var)
self.random_padding_cipher = random_padding_cipher.Client(
self.transfer_variable.random_padding_cipher_trans_var
)
self.aggregate_every_n_epoch = aggregate_every_n_epoch
self._params: list = None
self._should_stop = False
self.loss_summary = []
def aggregator_call(job_id, role, ind, *args):
server_model = args[0][0]
client_models = args[0][1:]
if role == consts.ARBITER:
agg = aggregator.Server()
models = agg.get_models()
agg.send_aggregated_model(server_model)
return models
else:
agg = aggregator.Client()
if role == consts.GUEST:
agg.send_model(client_models[0])
else:
agg.send_model(client_models[ind + 1])
return agg.get_aggregated_model()