FedAvgAndroid#

class FedAvgAndroid(*, 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 | None = None)[소스]#

기반 클래스: Strategy

Federated Averaging strategy.

Implementation based on https://arxiv.org/abs/1602.05629

매개변수:

  • fraction_fit (Optional[float]) – Fraction of clients used during training. Defaults to 1.0.

  • fraction_evaluate (Optional[float]) – Fraction of clients used during validation. Defaults to 1.0.

  • min_fit_clients (Optional[int]) – Minimum number of clients used during training. Defaults to 2.

  • min_evaluate_clients (Optional[int]) – Minimum number of clients used during validation. Defaults to 2.

  • min_available_clients (Optional[int]) – Minimum number of total clients in the system. Defaults to 2.

  • evaluate_fn (Optional[Callable[[int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]]]]) – Optional function used for validation. Defaults to None.

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

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

  • accept_failures (Optional[bool]) – Whether or not accept rounds containing failures. Defaults to True.

  • initial_parameters (Optional[Parameters]) – Initial global model parameters.

메소드

aggregate_evaluate(server_round, results, ...)

Aggregate evaluation losses using weighted average.

aggregate_fit(server_round, results, failures)

Aggregate fit results using weighted average.

bytes_to_ndarray(tensor)

Deserialize NumPy array from bytes.

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.

ndarray_to_bytes(ndarray)

Serialize NumPy array to bytes.

ndarrays_to_parameters(ndarrays)

NumPy 배열을 매개변수 객체로 변환합니다.

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.

parameters_to_ndarrays(parameters)

Convert parameters object to NumPy weights.
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]]][소스]#

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.

bytes_to_ndarray(tensor: bytes) → ndarray[Any, dtype[Any]][소스]#

Deserialize NumPy array from bytes.

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

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]][소스]#

Configure the next round of training.

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

Evaluate model parameters using an evaluation function.

initialize_parameters(client_manager: ClientManager) → Parameters | None[소스]#

Initialize global model parameters.

ndarray_to_bytes(ndarray: ndarray[Any, dtype[Any]]) → bytes[소스]#

Serialize NumPy array to bytes.

ndarrays_to_parameters(ndarrays: list[numpy.ndarray[Any, numpy.dtype[Any]]]) → Parameters[소스]#

NumPy 배열을 매개변수 객체로 변환합니다.

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

Use a fraction of available clients for evaluation.

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

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

parameters_to_ndarrays(parameters: Parameters) → list[numpy.ndarray[Any, numpy.dtype[Any]]][소스]#

Convert parameters object to NumPy weights.