nvflare.app_common.workflows.scaffold module

class Scaffold(*args, num_clients: int = 3, num_rounds: int = 5, start_round: int = 0, **kwargs)[source]

Bases: BaseFedAvg

Controller for Scaffold Workflow. Note: This class is based on ModelController. Implements [SCAFFOLD](https://proceedings.mlr.press/v119/karimireddy20a.html).

Provides the implementations for the run routine, controlling the main workflow:

  • def run(self)

The parent classes provide the default implementations for other routines.

Parameters:

  • num_clients (int, optional) – The number of clients. Defaults to 3.

  • 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.

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

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

A model persistor can be configured via the persistor_id argument of the ModelController. 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 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:

  • num_clients (int, optional) – The number of clients. Defaults to 3.

  • num_rounds (int, optional) – The total number of training rounds. Defaults to 5.

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

initialize(fl_ctx)[source]

Called by the framework to initialize the Learner object. This is called before the Learner can train or validate. This is called only once.

run() → None[source]

Main run routine for the controller workflow.

scaffold_aggregate_fn(results: List[FLModel]) → FLModel[source]