nvflare.app_common.workflows.base_fedavg module

class BaseFedAvg(*args, min_clients: int = 1000, num_rounds: int = 5, start_round: int = 0, persist_every_n_rounds: int = 1, **kwargs)[source]

Bases: WFController

The base controller for FedAvg Workflow. Note: This class is based on the WFController.

Implements [FederatedAveraging](https://arxiv.org/abs/1602.05629).

A model persistor can be configured via the persistor_id argument of the WFController. The model persistor 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 will also save 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)

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.

  • start_round (int, optional) – The starting round number.

  • 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.

sample_clients(num_clients)[source]

Called by the run routine to get a list of available clients.

Parameters:

min_clients – number of clients to return.

Returns: list of clients.

save_model(model: FLModel)[source]

Saves model with persistor. If persistor is not configured, does not save.

Parameters:

model (FLModel) – model to save.

Returns:

None

update_model(model, aggr_result)[source]

Called by the run routine to update the current global model (self.model) given the aggregated result.

Parameters:

  • model – FLModel to be updated.

  • aggr_result – aggregated FLModel.

Returns: None.