def load_trainer(self):
"""Setting up the global model to be trained via federated learning."""
if self.trainer is None:
self.trainer = trainers_registry.get(model=self.model)
self.trainer.set_client_id(0)
if self.algorithm is None:
self.algorithm = algorithms_registry.get(self.trainer)
def configure(self) -> None:
"""Prepare this client for training."""
super().configure()
if self.trainer is None:
self.trainer = trainers_registry.get(self.model)
self.trainer.set_client_id(self.client_id)
if self.algorithm is None:
self.algorithm = algorithms_registry.get(self.trainer)
self.algorithm.set_client_id(self.client_id)
# Pass inbound and outbound data payloads through processors for
# additional data processing
self.outbound_processor, self.inbound_processor = processor_registry.get(
"Client", client_id=self.client_id, trainer=self.trainer)