nvflare.app_opt.pt.recipes.fedavg module

class FedAvgRecipe(*, name: str = 'fedavg', model: Any | dict[str, Any] | None = None, initial_ckpt: str | None = None, min_clients: int, num_rounds: int = 2, train_script: str, train_args: str = '', aggregator: Aggregator | None = None, aggregator_data_kind: DataKind | None = DataKind.WEIGHTS, launch_external_process: bool = False, command: str = 'python3 -u', server_expected_format: ExchangeFormat = ExchangeFormat.NUMPY, params_transfer_type: TransferType = TransferType.FULL, model_persistor: ModelPersistor | None = None, model_locator: ModelLocator | None = None, per_site_config: dict[str, dict] | None = None, launch_once: bool = True, shutdown_timeout: float = 0.0, key_metric: str = 'accuracy', stop_cond: str | None = None, patience: int | None = None, best_model_filename: str | None = None, save_filename: str | None = None, exclude_vars: str | None = None, aggregation_weights: dict[str, float] | None = None, server_memory_gc_rounds: int = 0, enable_tensor_disk_offload: bool = False, client_memory_gc_rounds: int = 0, cuda_empty_cache: bool = False)[source]

Bases: FedAvgRecipe

A recipe for implementing Federated Averaging (FedAvg) for PyTorch.

Recipe parameters, including train_args and nested per_site_config values, must never contain actual secrets. Read secrets from site environment variables or mounted files; references are supported only where documented in nvflare.recipe.secrets.

FedAvg is a fundamental federated learning algorithm that aggregates model updates from multiple clients by computing a weighted average based on the amount of local training data. This recipe sets up a complete federated learning workflow with memory-efficient InTime aggregation.

The recipe configures: - A federated job with initial model (optional) - 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 “fedavg”.

  • model – Initial model to start federated training with. Can be: - nn.Module instance - Dict config: {“class_path”: “module.ClassName”, “args”: {“param”: value}} - None: no initial model

  • initial_ckpt – Absolute path to a pre-trained checkpoint file. The file may not exist locally as it could be on the server. Used to load initial weights. Note: PyTorch requires model when using initial_ckpt (for architecture).

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

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

  • 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 (ModelAggregator) for combining client model updates. Must implement accept_model(), aggregate_model(), reset_stats() methods. If None, uses built-in memory-efficient weighted averaging.

  • aggregator_data_kind – Data kind to use for the aggregator. When a custom aggregator declares expected_data_kind, the declaration must match. Defaults to DataKind.WEIGHTS.

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

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

  • server_expected_format (str) – What format to exchange the parameters between server and client.

  • params_transfer_type (str) – How to transfer the parameters. DIFF enables automatic difference calculation for full-model client results. A client’s FLModel.params_type remains authoritative. Defaults to TransferType.FULL.

  • model_persistor – Custom model persistor. If None, PTFileModelPersistor will be used.

  • model_locator – Custom model locator. If None, PTFileModelLocator will be used.

  • per_site_config – Deprecated constructor form. New code should call set_per_site_config(recipe, config) immediately after construction.

  • launch_once – Whether external process is launched once or per task. Defaults to True.

  • shutdown_timeout – Seconds to wait before shutdown. Defaults to 0.0.

  • key_metric – Metric used to determine if the model is globally best. Defaults to “accuracy”.

  • stop_cond – Early stopping condition based on metric. String literal in the format of ‘<key> <op> <value>’ (e.g. “accuracy >= 80”). If None, early stopping is disabled.

  • patience – Number of rounds with no improvement after which FL will be stopped.

  • best_model_filename – Filename for saving the best model. If unset, the default PyTorch persistor uses DefaultCheckpointFileName.BEST_GLOBAL_MODEL.

  • save_filename – Deprecated alias for best_model_filename. If both are specified, they must match.

  • exclude_vars – Regex pattern for variables to exclude from aggregation.

  • aggregation_weights – Per-client aggregation weights dict. Defaults to equal weights.

  • enable_tensor_disk_offload – Enable disk-backed tensor offload for incoming streamed payloads.

Example

Basic usage with early stopping:

```python recipe = FedAvgRecipe(

name=”my_fedavg_job”, model=pretrained_model, min_clients=2, num_rounds=10, train_script=”client.py”, train_args=”–epochs 5 –batch_size 32”, stop_cond=”accuracy >= 95”, patience=3

)

Note

This recipe uses InTime (streaming) aggregation for memory efficiency - each client result is aggregated immediately upon receipt rather than collecting all results first. Memory usage is constant regardless of the number of clients.

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

Security contract – no secrets in recipe parameters:

Recipe parameters (train_args, task_args, eval_args, per_site_config, config overrides, dicts passed to add_client_config/add_server_config, exec params, etc.) can be written in clear text into generated job configuration. These parameters and their nested values must never contain actual passwords, API keys, tokens, private keys, or other credentials. Instead, read secrets from site environment variables or mounted secret files inside your code, or pass a placeholder created with nvflare.recipe.secrets.secret_ref() or nvflare.recipe.secrets.secret_file_ref() at a supported runtime boundary. See nvflare.recipe.secrets for the supported parameter locations.

Before export or run, recipes scan their parameters with heuristics and emit nvflare.recipe.secrets.PotentialSecretWarning when a value looks like an actual secret. The scan is best-effort: absence of a warning does not prove a parameter is safe to share.

param job:

the job that implements the recipe.