fl4health.strategies.flash module¶
- class Flash(*, fraction_fit=1.0, fraction_evaluate=1.0, min_fit_clients=2, min_evaluate_clients=2, min_available_clients=2, evaluate_fn=None, on_fit_config_fn=None, on_evaluate_config_fn=None, accept_failures=True, initial_parameters, fit_metrics_aggregation_fn=None, evaluate_metrics_aggregation_fn=None, eta=0.1, eta_l=0.1, beta_1=0.9, beta_2=0.99, tau=1e-09, weighted_aggregation=False, weighted_eval_losses=False)[source]¶
Bases:
BasicFedAvg- __init__(*, fraction_fit=1.0, fraction_evaluate=1.0, min_fit_clients=2, min_evaluate_clients=2, min_available_clients=2, evaluate_fn=None, on_fit_config_fn=None, on_evaluate_config_fn=None, accept_failures=True, initial_parameters, fit_metrics_aggregation_fn=None, evaluate_metrics_aggregation_fn=None, eta=0.1, eta_l=0.1, beta_1=0.9, beta_2=0.99, tau=1e-09, weighted_aggregation=False, weighted_eval_losses=False)[source]¶
Flash: Concept Drift Adaptation in Federated Learning.
Implementation based on https://proceedings.mlr.press/v202/panchal23a/panchal23a.pdf
- Parameters:
fraction_fit (
float) – float, optional Fraction of clients used during training. Defaults to 1.0.fraction_evaluate (
float) – float, optional Fraction of clients used during validation. Defaults to 1.0.min_fit_clients (
int) – int, optional Minimum number of clients used during training. Defaults to 2.min_evaluate_clients (
int) – int, optional Minimum number of clients used during validation. Defaults to 2.min_available_clients (
int) – int, optional Minimum number of total clients in the system. Defaults to 2.evaluate_fn (
Optional[Callable[[int,List[ndarray[Any,dtype[Any]]],dict[str,Union[bool,bytes,float,int,str]]],tuple[float,dict[str,Union[bool,bytes,float,int,str]]] |None]]) –- Callable[[int, NDArrays, dict[str, Scalar] | None,
tuple[float, dict[str, Scalar]]]] | None
Optional function used for validation. Defaults to None.
on_fit_config_fn (
Optional[Callable[[int],dict[str,Union[bool,bytes,float,int,str]]]]) – Callable[[int], dict[str, Scalar]], optional Function used to configure training. Defaults to None.on_evaluate_config_fn (
Optional[Callable[[int],dict[str,Union[bool,bytes,float,int,str]]]]) – Callable[[int], dict[str, Scalar]], optional Function used to configure validation. Defaults to None.accept_failures (
bool) – bool, optional Whether or not accept rounds containing failures. Defaults to True.initial_parameters (
Parameters) – Parameters Initial global model parameters.fit_metrics_aggregation_fn (
Optional[Callable[[List[Tuple[int,Dict[str,Union[bool,bytes,float,int,str]]]]],Dict[str,Union[bool,bytes,float,int,str]]]]) – MetricsAggregationFn | None Metrics aggregation function, optional.evaluate_metrics_aggregation_fn (
Optional[Callable[[List[Tuple[int,Dict[str,Union[bool,bytes,float,int,str]]]]],Dict[str,Union[bool,bytes,float,int,str]]]]) – MetricsAggregationFn | None Metrics aggregation function, optional.eta (
float) – float, optional Server-side learning rate. Defaults to 1e-1.eta_l (
float) – float, optional Client-side learning rate. Defaults to 1e-1.beta_1 (
float) – float, optional Momentum parameter. Defaults to 0.9.beta_2 (
float) – float, optional Second moment parameter. Defaults to 0.99.tau (
float) – float, optional Controls the algorithm’s degree of adaptability. Defaults to 1e-9.weighted_aggregation (bool, optional) – Determines whether parameter aggregation is a linearly weighted average or a uniform average. Flash default is a uniform average by the number of clients. Defaults to False.
weighted_eval_losses (bool, optional) – Determines whether losses during evaluation are linearly weighted averages or a uniform average. Flash default is a uniform average of the losses by dividing the total loss by the number of clients. Defaults to False.