Bulyan

class Bulyan(*, 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, num_malicious_clients: int = 0, evaluate_fn: ~typing.Callable[[int, list[~numpy.ndarray[~typing.Any, ~numpy.dtype[~typing.Any]]], dict[str, bool | bytes | float | int | str]], tuple[float, dict[str, bool | bytes | float | int | str]] | None] | None = None, on_fit_config_fn: ~typing.Callable[[int], dict[str, bool | bytes | float | int | str]] | None = None, on_evaluate_config_fn: ~typing.Callable[[int], dict[str, bool | bytes | float | int | str]] | None = None, accept_failures: bool = True, initial_parameters: ~flwr.common.typing.Parameters | None = None, fit_metrics_aggregation_fn: ~typing.Callable[[list[tuple[int, dict[str, bool | bytes | float | int | str]]]], dict[str, bool | bytes | float | int | str]] | None = None, evaluate_metrics_aggregation_fn: ~typing.Callable[[list[tuple[int, dict[str, bool | bytes | float | int | str]]]], dict[str, bool | bytes | float | int | str]] | None = None, first_aggregation_rule: ~typing.Callable = <function aggregate_krum>, **aggregation_rule_kwargs: ~typing.Any)[source]

Bases : FedAvg

Bulyan strategy.

Implementation based on https://arxiv.org/abs/1802.07927.

Paramètres:

  • 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.

  • num_malicious_clients (int, optional) – Number of malicious clients in the system. Defaults to 0.

  • 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 (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, optional) – Initial global model parameters.

  • first_aggregation_rule (Callable) – Byzantine resilient aggregation rule that is used as the first step of the Bulyan (e.g., Krum)

  • **aggregation_rule_kwargs (Any) – arguments to the first_aggregation rule

Methods

aggregate_evaluate(server_round, results, ...)

Aggregate evaluation losses using weighted average.

aggregate_fit(server_round, results, failures)

Aggregate fit results using Bulyan.

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

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 Bulyan.

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

Configure the next round of evaluation.

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

Configure the next round of training.

evaluate(server_round: int, parameters: Parameters) → tuple[float, dict[str, 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.