nvflare.app_opt.sklearn.recipes.fedavg module

class SklearnFedAvgRecipe(*, name: str = 'sklearn_fedavg', min_clients: int, num_rounds: int = 2, model_params: dict | None = None, model_path: str | None = None, train_script: str, train_args: str = '', aggregator: Aggregator | None = None, aggregator_data_kind: DataKind = DataKind.WEIGHTS, launch_external_process: bool = False, command: str = 'python3 -u', per_site_config: dict[str, dict] | None = None, key_metric: str = 'accuracy', launch_once: bool = True, shutdown_timeout: float = 0.0)[source]

Bases: FedAvgRecipe

A recipe for implementing Federated Averaging (FedAvg) with Scikit-learn.

This recipe sets up a complete federated learning workflow with memory-efficient InTime aggregation, specifically designed for scikit-learn models.

The recipe configures: - A federated job with initial parameters - FedAvg controller with InTime aggregation for memory efficiency - Optional early stopping and model selection - Script runners for client-side training execution

Parameters:

  • name – Name of the federated learning job. Defaults to “sklearn_fedavg”.

  • min_clients – Minimum number of clients required to start a training round.

  • num_rounds – Number of federated training rounds to execute. Defaults to 2.

  • model_params – Model hyperparameters as a dictionary. For SGDClassifier, can include: n_classes, learning_rate, eta0, loss, penalty, fit_intercept, etc.

  • model_path – Optional absolute path to a saved model file (.joblib, .pkl). If provided, the model is loaded from this path at runtime (file must exist). Takes precedence over model_params when loading.

  • train_script – Path to the training script that will be executed on each client.

  • train_args – Command line arguments to pass to the training script.

  • aggregator – Custom aggregator for combining client updates. If None, uses InTimeAccumulateWeightedAggregator with aggregator_data_kind.

  • aggregator_data_kind – Data kind to use for the aggregator. Defaults to DataKind.WEIGHTS.

  • launch_external_process – Whether to launch the script in external process. Defaults to False.

  • command – If launch_external_process=True, command to run script (prepended to script). Defaults to “python3 -u”.

  • per_site_config – Per-site configuration for the federated learning job. Dictionary mapping site names to configuration dicts. If not provided, the same configuration will be used for all clients.

  • key_metric – Metric used to determine if the model is globally best. If validation metrics are a dict, key_metric selects the metric used for global model selection. Defaults to “accuracy”.

  • launch_once – Whether the external process will be launched only once at the beginning or on each task. Only used if launch_external_process is True. Defaults to True.

  • shutdown_timeout – If provided, will wait for this number of seconds before shutdown. Only used if launch_external_process is True. Defaults to 0.0.

Example

Basic usage with same config for all clients:

```python recipe = SklearnFedAvgRecipe(

name=”sklearn_linear”, min_clients=5, num_rounds=50, model_params={

“n_classes”: 2, “learning_rate”: “constant”, “eta0”: 1e-4, “loss”: “log_loss”, “penalty”: “l2”, “fit_intercept”: 1,

}, train_script=”client.py”, train_args=”–data_path /tmp/data/HIGGS.csv”,

)

from nvflare.recipe import SimEnv env = SimEnv(num_clients=5) run = recipe.execute(env) print(“Result:”, run.get_result()) ```

Per-site configuration:

```python from nvflare.app_opt.sklearn import SklearnFedAvgRecipe

recipe = SklearnFedAvgRecipe(

name=”sklearn_linear”, min_clients=3, num_rounds=50, model_params={“n_classes”: 2, “learning_rate”: “constant”, “eta0”: 1e-4}, train_script=”client.py”, per_site_config={

“site-1”: {“train_args”: “–data_path /tmp/data/site1.csv”}, “site-2”: {“train_args”: “–data_path /tmp/data/site2.csv”}, “site-3”: {“train_args”: “–data_path /tmp/data/site3.csv”},

},

)

Note

By default, this recipe implements the standard FedAvg algorithm where model updates are aggregated using weighted averaging based on the number of training samples provided by each client.

If you want to use a custom aggregator, you can pass it in the aggregator parameter. The custom aggregator must be a subclass of the Aggregator class.

This is base class of a recipe. Recipes are implemented by jobs. A concrete recipe must provide the job for recipe implementation.

param job:

the job that implements the recipe.