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

Federated Averaging strategy.

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

Parameters:

  • 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, ...)

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)

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]#

Configure the next round of evaluation.

configure_fit(server_round: int, parameters: Parameters, client_manager: ClientManager) → List[Tuple[ClientProxy, FitIns]][source]#

Configure the next round of training.

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.