QFedAvgยถ

class QFedAvg(*, q_param: float = 0.2, qffl_learning_rate: float = 0.1, fraction_fit: float = 1.0, fraction_evaluate: float = 1.0, min_fit_clients: int = 1, min_evaluate_clients: int = 1, min_available_clients: int = 1, 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, fit_metrics_aggregation_fn: Callable[[list[tuple[int, dict[str, bool | bytes | float | int | str]]]], dict[str, bool | bytes | float | int | str]] | None = None, evaluate_metrics_aggregation_fn: Callable[[list[tuple[int, dict[str, bool | bytes | float | int | str]]]], dict[str, bool | bytes | float | int | str]] | None = None)[์†Œ์Šค]ยถ

๊ธฐ๋ฐ˜ ํด๋ž˜์Šค: FedAvg

Configurable QFedAvg strategy implementation.

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

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]][์†Œ์Šค]ยถ

Aggregate fit results using weighted average.

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

Configure the next round of evaluation.

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

Configure the next round of training.

evaluate(server_round: int, parameters: Parameters) tuple[float, dict[str, bool | bytes | float | int | str]] | Noneยถ

Evaluate model parameters using an evaluation function.

initialize_parameters(client_manager: ClientManager) Parameters | Noneยถ

Initialize global model parameters.

num_evaluation_clients(num_available_clients: int) tuple[int, int][์†Œ์Šค]ยถ

Use a fraction of available clients for evaluation.

num_fit_clients(num_available_clients: int) tuple[int, int][์†Œ์Šค]ยถ

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