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.