def __init__(self, max_num_aggregation, eps=0.0, name="default"):
super(PyTorchSAServerContext,
self).__init__(max_num_aggregation=max_num_aggregation,
name=name)
self.transfer_variable = SecureAggregatorTransVar()
self.aggregator = aggregator.Server(
self.transfer_variable.aggregator_trans_var)
self.random_padding_cipher = random_padding_cipher.Server(
self.transfer_variable.random_padding_cipher_trans_var)
self._eps = eps
self._loss = math.inf
def __init__(self,
server=(consts.ARBITER, ),
clients=(consts.GUEST, consts.HOST),
prefix=None):
super().__init__(server=server, clients=clients, prefix=prefix)
self.secure_aggregator_trans_var = SecureAggregatorTransVar(
server=server, clients=clients, prefix=self.prefix)
self.loss_scatter_trans_var = LossScatterTransVar(server=server,
clients=clients,
prefix=self.prefix)
self.has_converged_trans_var = HasConvergedTransVar(server=server,
clients=clients,
prefix=self.prefix)
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 = []