nvflare.app_common.workflows.base_fedavg module¶
- class BaseFedAvg(*args, **kwargs)[source]¶
Bases:
ExperimentalClass
The base controller for FedAvg Workflow. Note: This class is based on the experimental ModelController.
Implements [FederatedAveraging](https://arxiv.org/abs/1602.05629). The model persistor (persistor_id) is used to load the initial global model which is sent to a list of clients. Each client sends it’s updated weights after local training which is aggregated. Next, the global model is updated. The model_persistor also saves the model after training.
- Provides the default implementations for the follow routines:
def sample_clients(self, min_clients)
def aggregate(self, results: List[FLModel], aggregate_fn=None) -> FLModel
def update_model(self, aggr_result)
The run routine needs to be implemented by the derived class:
def run(self)
FLModel based controller.
- Parameters:
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.
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.
- aggregate(results: List[FLModel], aggregate_fn=None) FLModel [source]¶
Called by the run routine to aggregate the training results of clients.
- Parameters:
results – a list of FLModel containing training results of the clients.
aggregate_fn – a function that turns the list of FLModel into one resulting (aggregated) FLModel.
Returns: aggregated FLModel.