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[ndarray[Any, dtype[Any]]], dict[str, bool | bytes | float | int | str]], tuple[float, dict[str, bool | bytes | float | int | str]] | None] | None = None, on_fit_config_fn: Callable[[int], dict[str, bool | bytes | float | int | str]] | None = None, on_evaluate_config_fn: Callable[[int], dict[str, bool | bytes | float | int | str]] | None = None, accept_failures: bool = True, initial_parameters: Parameters | None = None)[source]

Bases: Strategy

联邦平均策略。

实施基于 https://arxiv.org/abs/1602.05629

参数:

  • 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.

Methods

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, ...)

配置下一轮评估。

configure_fit(server_round, parameters, ...)

配置下一轮训练。

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)

Convert NumPy ndarrays to parameters object.

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[ClientProxy, EvaluateRes]], failures: list[tuple[ClientProxy, EvaluateRes] | BaseException]) → tuple[float | None, dict[str, bool | bytes | float | int | str]][source]

Aggregate evaluation losses using weighted average.

aggregate_fit(server_round: int, results: list[tuple[ClientProxy, FitRes]], failures: list[tuple[ClientProxy, FitRes] | BaseException]) → tuple[Parameters | None, dict[str, bool | bytes | float | int | str]][source]

Aggregate fit results using weighted average.

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

Deserialize NumPy array from bytes.

configure_evaluate(server_round: int, parameters: Parameters, client_manager: ClientManager) → list[tuple[ClientProxy, EvaluateIns]][source]

配置下一轮评估。

configure_fit(server_round: int, parameters: Parameters, client_manager: ClientManager) → list[tuple[ClientProxy, FitIns]][source]

配置下一轮训练。

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

Evaluate model parameters using an evaluation function.

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

Initialize global model parameters.

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

Serialize NumPy array to bytes.

ndarrays_to_parameters(ndarrays: list[ndarray[Any, dtype[Any]]]) → Parameters[source]

Convert NumPy ndarrays to parameters object.

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

Use a fraction of available clients for evaluation.

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

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

parameters_to_ndarrays(parameters: Parameters) → list[ndarray[Any, dtype[Any]]][source]

Convert parameters object to NumPy weights.