FedAdamยถ
- class FedAdam(*, 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, 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, eta: float = 0.1, eta_l: float = 0.1, beta_1: float = 0.9, beta_2: float = 0.99, tau: float = 1e-09)[์์ค]ยถ
๊ธฐ๋ฐ ํด๋์ค:
FedOpt
FedAdam - Adaptive Federated Optimization using Adam.
Implementation based on https://arxiv.org/abs/2003.00295v5
- ๋งค๊ฐ๋ณ์:
fraction_fit (float, optional) โ Fraction of clients used during training. Defaults to 1.0.
fraction_evaluate (float, optional) โ Fraction of clients used during validation. Defaults to 1.0.
min_fit_clients (int, optional) โ Minimum number of clients used during training. Defaults to 2.
min_evaluate_clients (int, optional) โ Minimum number of clients used during validation. Defaults to 2.
min_available_clients (int, optional) โ 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]]]]]) โ Optional function used for validation. Defaults to None.
on_fit_config_fn (Callable[[int], Dict[str, Scalar]], optional) โ Function used to configure training. Defaults to None.
on_evaluate_config_fn (Callable[[int], Dict[str, Scalar]], optional) โ Function used to configure validation. Defaults to None.
accept_failures (bool, optional) โ Whether or not accept rounds containing failures. Defaults to True.
initial_parameters (Parameters) โ Initial global model parameters.
fit_metrics_aggregation_fn (Optional[MetricsAggregationFn]) โ Metrics aggregation function, optional.
evaluate_metrics_aggregation_fn (Optional[MetricsAggregationFn]) โ Metrics aggregation function, optional.
eta (float, optional) โ Server-side learning rate. Defaults to 1e-1.
eta_l (float, optional) โ Client-side learning rate. Defaults to 1e-1.
beta_1 (float, optional) โ Momentum parameter. Defaults to 0.9.
beta_2 (float, optional) โ Second moment parameter. Defaults to 0.99.
tau (float, optional) โ Controls the algorithmโs degree of adaptability. Defaults to 1e-9.
๋ฉ์๋
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]] 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]] [์์ค]ยถ
Aggregate fit results using weighted average.
- configure_evaluate(server_round: int, parameters: Parameters, client_manager: ClientManager) list[tuple[ClientProxy, EvaluateIns]] ยถ
Configure the next round of evaluation.
- configure_fit(server_round: int, parameters: Parameters, client_manager: ClientManager) list[tuple[ClientProxy, FitIns]] ยถ
Configure the next round of training.
- evaluate(server_round: int, parameters: Parameters) tuple[float, dict[str, bool | bytes | float | int | str]] | None ยถ
Evaluate model parameters using an evaluation function.
- initialize_parameters(client_manager: ClientManager) Parameters | None ยถ
Initialize global model parameters.
- num_evaluation_clients(num_available_clients: int) tuple[int, int] ยถ
Use a fraction of available clients for evaluation.
- num_fit_clients(num_available_clients: int) tuple[int, int] ยถ
Return the sample size and the required number of available clients.