FedYogi#

class FedYogi(*, fraction_fit: float = 1.0, fraction_evaluate: float = 1.0, min_fit_clients: int = 2, min_evaluate_clients: int = 2, min_available_clients: int = 2, evaluate_fn: Callable[[int, list[numpy.ndarray[Any, numpy.dtype[Any]]], dict[str, Union[bool, bytes, float, int, str]]], tuple[float, dict[str, Union[bool, bytes, float, int, str]]] | None] | None = None, on_fit_config_fn: Callable[[int], dict[str, Union[bool, bytes, float, int, str]]] | None = None, on_evaluate_config_fn: Callable[[int], dict[str, Union[bool, bytes, float, int, str]]] | None = None, accept_failures: bool = True, initial_parameters: Parameters, fit_metrics_aggregation_fn: Callable[[list[tuple[int, dict[str, Union[bool, bytes, float, int, str]]]]], dict[str, Union[bool, bytes, float, int, str]]] | None = None, evaluate_metrics_aggregation_fn: Callable[[list[tuple[int, dict[str, Union[bool, bytes, float, int, str]]]]], dict[str, Union[bool, bytes, float, int, str]]] | None = None, eta: float = 0.01, eta_l: float = 0.0316, beta_1: float = 0.9, beta_2: float = 0.99, tau: float = 0.001)[소스]#

기반 클래스: FedOpt

FedYogi [Reddi et al., 2020] strategy.

Implementation based on https://arxiv.org/abs/2003.00295v5

매개변수:

  • fraction_fit (float, optional) – Fraction of clients used during training. Defaults to 1.0.

  • fraction_evaluate (float, optional) – Fraction of clients used during validation. Defaults to 1.0.

  • min_fit_clients (int, optional) – Minimum number of clients used during training. Defaults to 2.

  • min_evaluate_clients (int, optional) – Minimum number of clients used during validation. Defaults to 2.

  • min_available_clients (int, optional) – Minimum number of total clients in the system. Defaults to 2.

  • evaluate_fn (Optional[Callable[[int, NDArrays, Dict[str, Scalar]], Optional[) –

  • Tuple[float – Optional function used for validation. Defaults to None.

  • Dict[str – Optional function used for validation. Defaults to None.

  • Scalar]]]]] – Optional function used for validation. Defaults to None.

  • on_fit_config_fn (Callable[[int], Dict[str, Scalar]], optional) – Function used to configure training. Defaults to None.

  • on_evaluate_config_fn (Callable[[int], Dict[str, Scalar]], optional) – Function used to configure validation. Defaults to None.

  • accept_failures (bool, optional) – Whether or not accept rounds containing failures. Defaults to True.

  • initial_parameters (Parameters) – Initial global model parameters.

  • fit_metrics_aggregation_fn (Optional[MetricsAggregationFn]) – Metrics aggregation function, optional.

  • evaluate_metrics_aggregation_fn (Optional[MetricsAggregationFn]) – Metrics aggregation function, optional.

  • eta (float, optional) – Server-side learning rate. Defaults to 1e-2.

  • eta_l (float, optional) – Client-side learning rate. Defaults to 0.0316.

  • beta_1 (float, optional) – Momentum parameter. Defaults to 0.9.

  • beta_2 (float, optional) – Second moment parameter. Defaults to 0.99.

  • tau (float, optional) – Controls the algorithm’s degree of adaptability. Defaults to 1e-3.

메소드

aggregate_evaluate(server_round, results, ...)

Aggregate evaluation losses using weighted average.

aggregate_fit(server_round, results, failures)

Aggregate fit results using weighted average.

configure_evaluate(server_round, parameters, ...)

Configure the next round of evaluation.

configure_fit(server_round, parameters, ...)

Configure the next round of training.

evaluate(server_round, parameters)

Evaluate model parameters using an evaluation function.

initialize_parameters(client_manager)

Initialize global model parameters.

num_evaluation_clients(num_available_clients)

Use a fraction of available clients for evaluation.

num_fit_clients(num_available_clients)

Return the sample size and the required number of available clients.
aggregate_evaluate(server_round: int, results: list[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.EvaluateRes]], failures: list[Union[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.EvaluateRes], BaseException]]) → tuple[Optional[float], dict[str, Union[bool, bytes, float, int, str]]]#

Aggregate evaluation losses using weighted average.

aggregate_fit(server_round: int, results: list[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.FitRes]], failures: list[Union[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.FitRes], BaseException]]) → tuple[Optional[flwr.common.typing.Parameters], dict[str, Union[bool, bytes, float, int, str]]][소스]#

Aggregate fit results using weighted average.

configure_evaluate(server_round: int, parameters: Parameters, client_manager: ClientManager) → list[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.EvaluateIns]]#

Configure the next round of evaluation.

configure_fit(server_round: int, parameters: Parameters, client_manager: ClientManager) → list[tuple[flwr.server.client_proxy.ClientProxy, flwr.common.typing.FitIns]]#

Configure the next round of training.

evaluate(server_round: int, parameters: Parameters) → tuple[float, dict[str, Union[bool, bytes, float, int, str]]] | None#

Evaluate model parameters using an evaluation function.

initialize_parameters(client_manager: ClientManager) → Parameters | None#

Initialize global model parameters.

num_evaluation_clients(num_available_clients: int) → tuple[int, int]#

Use a fraction of available clients for evaluation.

num_fit_clients(num_available_clients: int) → tuple[int, int]#

Return the sample size and the required number of available clients.