DPFedAvgAdaptive#
- class DPFedAvgAdaptive(strategy: Strategy, num_sampled_clients: int, init_clip_norm: float = 0.1, noise_multiplier: float = 1.0, server_side_noising: bool = True, clip_norm_lr: float = 0.2, clip_norm_target_quantile: float = 0.5, clip_count_stddev: float | None = None)[source]#
Bases:
DPFedAvgFixed
Wrapper for configuring a Strategy for DP with Adaptive Clipping.
Warning
This class is deprecated and will be removed in a future release.
Methods
aggregate_evaluate
(server_round, results, ...)Aggregate evaluation losses using the given strategy.
aggregate_fit
(server_round, results, failures)Aggregate training results as in DPFedAvgFixed and update clip norms.
configure_evaluate
(server_round, parameters, ...)Configure the next round of evaluation using the specified strategy.
configure_fit
(server_round, parameters, ...)Configure the next round of training.
evaluate
(server_round, parameters)Evaluate model parameters using an evaluation function from the strategy.
initialize_parameters
(client_manager)Initialize global model parameters using given strategy.
- 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 the given strategy.
- 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 training results as in DPFedAvgFixed and update clip norms.
- configure_evaluate(server_round: int, parameters: Parameters, client_manager: ClientManager) List[Tuple[ClientProxy, EvaluateIns]] #
Configure the next round of evaluation using the specified strategy.
- Parameters:
server_round (int) – The current round of federated learning.
parameters (Parameters) – The current (global) model parameters.
client_manager (ClientManager) – The client manager which holds all currently connected clients.
- Returns:
evaluate_configuration – A list of tuples. Each tuple in the list identifies a ClientProxy and the EvaluateIns for this particular ClientProxy. If a particular ClientProxy is not included in this list, it means that this ClientProxy will not participate in the next round of federated evaluation.
- Return type:
List[Tuple[ClientProxy, EvaluateIns]]
- 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 #
Evaluate model parameters using an evaluation function from the strategy.
- initialize_parameters(client_manager: ClientManager) Parameters | None #
Initialize global model parameters using given strategy.