nvflare.app_opt.pt.fedavg_early_stopping module¶
- class PTFedAvgEarlyStopping(*args, stop_cond: str | None = None, save_filename: str = 'FL_global_model.pt', initial_model=None, **kwargs)[source]¶
Bases:
BaseFedAvgController for FedAvg Workflow with Early Stopping and Model Selection.
- 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.
stop_cond (str, optional) – early stopping condition based on metric. string literal in the format of “<key> <op> <value>” (e.g. “accuracy >= 80”)
save_filename (str, optional) – filename for saving model
initial_model (nn.Module, optional) – initial PyTorch model
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.
- is_curr_model_better(best_model: FLModel, curr_model: FLModel, target_metric: str, op_fn: Callable) bool[source]¶
- load_model(filepath='')[source]¶
Load initial model from persistor. If persistor is not configured, returns empty FLModel.
- Returns:
FLModel