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from .base_fedavg import BaseFedAvg
[docs]class FedAvg(BaseFedAvg):
"""Controller for FedAvg Workflow. *Note*: This class is based on the `WFController`.
Implements [FederatedAveraging](https://arxiv.org/abs/1602.05629).
Provides the implementations for the `run` routine, controlling the main workflow:
- def run(self)
The parent classes provide the default implementations for other routines.
Args:
min_clients (int, optional): The minimum number of clients responses before
Workflow starts to wait for `wait_time_after_min_received`. Note that the workflow will move forward
when all available clients have responded regardless of this value. Defaults to 1000.
num_rounds (int, optional): The total number of training rounds. Defaults to 5.
start_round (int, optional): The starting round number.
persistor_id (str, optional): ID of the persistor component. Defaults to "persistor".
ignore_result_error (bool, optional): whether this controller can proceed if client result has errors.
Defaults to False.
allow_empty_global_weights (bool, optional): whether to allow empty global weights. Some pipelines can have
empty global weights at first round, such that clients start training from scratch without any global info.
Defaults to False.
task_check_period (float, optional): interval for checking status of tasks. Defaults to 0.5.
persist_every_n_rounds (int, optional): persist the global model every n rounds. Defaults to 1.
If n is 0 then no persist.
"""
[docs] def run(self) -> None:
self.info("Start FedAvg.")
model = self.load_model()
model.start_round = self.start_round
model.total_rounds = self.num_rounds
for self.current_round in range(self.start_round, self.start_round + self.num_rounds):
self.info(f"Round {self.current_round} started.")
model.current_round = self.current_round
clients = self.sample_clients(self.min_clients)
results = self.send_model_and_wait(targets=clients, data=model)
aggregate_results = self.aggregate(
results, aggregate_fn=self.aggregate_fn
) # using default aggregate_fn with `WeightedAggregationHelper`. Can overwrite self.aggregrate_fn with signature Callable[List[FLModel], FLModel]
model = self.update_model(model, aggregate_results)
self.save_model(model)
self.info("Finished FedAvg.")