Move docs from __init__ to right after class defiition (adap#2692)

Co-authored-by: Charles Beauville <[email protected]>

src/py/flwr/server/strategy/bulyan.py

@@ -38,10 +38,43 @@
 
 
 # flake8: noqa: E501
+# pylint: disable=line-too-long
 class Bulyan(FedAvg):
-    """Bulyan strategy implementation."""
-
-    # pylint: disable=too-many-arguments,too-many-instance-attributes,line-too-long, too-many-locals
+    """Bulyan strategy.
+
+    Implementation based on https://arxiv.org/abs/1802.07927.
+
+    Parameters
+    ----------
+    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
+    """
+
+    # pylint: disable=too-many-arguments,too-many-instance-attributes,too-many-locals
     def __init__(
         self,
         *,
@@ -66,39 +99,6 @@ def __init__(
         first_aggregation_rule: Callable = aggregate_krum,  # type: ignore
         **aggregation_rule_kwargs: Any,
     ) -> None:
-        """Bulyan strategy.
-
-        Implementation based on https://arxiv.org/abs/1802.07927.
-
-        Parameters
-        ----------
-        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
-        """
         super().__init__(
             fraction_fit=fraction_fit,
             fraction_evaluate=fraction_evaluate,

src/py/flwr/server/strategy/fedadagrad.py

@@ -38,13 +38,47 @@
 from .fedopt import FedOpt
 
 
+# pylint: disable=line-too-long
 class FedAdagrad(FedOpt):
     """FedAdagrad strategy - Adaptive Federated Optimization using Adagrad.
 
-    Paper: https://arxiv.org/abs/2003.00295
+    Implementation based on https://arxiv.org/abs/2003.00295v5
+
+    Parameters
+    ----------
+    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.
+    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.
+    fit_metrics_aggregation_fn : Optional[MetricsAggregationFn]
+        Metrics aggregation function, optional.
+    evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn]
+        Metrics aggregation function, optional.
+    accept_failures : bool, optional
+        Whether or not accept rounds containing failures. Defaults to True.
+    initial_parameters : Parameters
+        Initial global model parameters.
+    eta : float, optional
+        Server-side learning rate. Defaults to 1e-1.
+    eta_l : float, optional
+        Client-side learning rate. Defaults to 1e-1.
+    tau : float, optional
+        Controls the algorithm's degree of adaptability. Defaults to 1e-9.
     """
 
-    # pylint: disable=too-many-arguments,too-many-locals,too-many-instance-attributes, line-too-long
+    # pylint: disable=too-many-arguments,too-many-locals,too-many-instance-attributes
     def __init__(
         self,
         *,
@@ -69,43 +103,6 @@ def __init__(
         eta_l: float = 1e-1,
         tau: float = 1e-9,
     ) -> None:
-        """Federated learning strategy using Adagrad on server-side.
-
-        Implementation based on https://arxiv.org/abs/2003.00295v5
-
-        Parameters
-        ----------
-        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.
-        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.
-        fit_metrics_aggregation_fn : Optional[MetricsAggregationFn]
-            Metrics aggregation function, optional.
-        evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn]
-            Metrics aggregation function, optional.
-        accept_failures : bool, optional
-            Whether or not accept rounds containing failures. Defaults to True.
-        initial_parameters : Parameters
-            Initial global model parameters.
-        eta : float, optional
-            Server-side learning rate. Defaults to 1e-1.
-        eta_l : float, optional
-            Client-side learning rate. Defaults to 1e-1.
-        tau : float, optional
-            Controls the algorithm's degree of adaptability. Defaults to 1e-9.
-        """
         super().__init__(
             fraction_fit=fraction_fit,
             fraction_evaluate=fraction_evaluate,

src/py/flwr/server/strategy/fedadam.py

@@ -38,13 +38,51 @@
 from .fedopt import FedOpt
 
 
+# pylint: disable=line-too-long
 class FedAdam(FedOpt):
     """FedAdam - Adaptive Federated Optimization using Adam.
 
-    Paper: https://arxiv.org/abs/2003.00295
+    Implementation based on https://arxiv.org/abs/2003.00295v5
+
+    Parameters
+    ----------
+    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.
+    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
+        Initial global model parameters.
+    fit_metrics_aggregation_fn : Optional[MetricsAggregationFn]
+        Metrics aggregation function, optional.
+    evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn]
+        Metrics aggregation function, optional.
+    eta : float, optional
+        Server-side learning rate. Defaults to 1e-1.
+    eta_l : float, optional
+        Client-side learning rate. Defaults to 1e-1.
+    beta_1 : float, optional
+        Momentum parameter. Defaults to 0.9.
+    beta_2 : float, optional
+        Second moment parameter. Defaults to 0.99.
+    tau : float, optional
+        Controls the algorithm's degree of adaptability. Defaults to 1e-9.
     """
 
-    # pylint: disable=too-many-arguments,too-many-instance-attributes,too-many-locals, line-too-long
+    # pylint: disable=too-many-arguments,too-many-instance-attributes,too-many-locals
     def __init__(
         self,
         *,
@@ -71,47 +109,6 @@ def __init__(
         beta_2: float = 0.99,
         tau: float = 1e-9,
     ) -> None:
-        """Federated learning strategy using Adagrad on server-side.
-
-        Implementation based on https://arxiv.org/abs/2003.00295v5
-
-        Parameters
-        ----------
-        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.
-        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
-            Initial global model parameters.
-        fit_metrics_aggregation_fn : Optional[MetricsAggregationFn]
-            Metrics aggregation function, optional.
-        evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn]
-            Metrics aggregation function, optional.
-        eta : float, optional
-            Server-side learning rate. Defaults to 1e-1.
-        eta_l : float, optional
-            Client-side learning rate. Defaults to 1e-1.
-        beta_1 : float, optional
-            Momentum parameter. Defaults to 0.9.
-        beta_2 : float, optional
-            Second moment parameter. Defaults to 0.99.
-        tau : float, optional
-            Controls the algorithm's degree of adaptability. Defaults to 1e-9.
-        """
         super().__init__(
             fraction_fit=fraction_fit,
             fraction_evaluate=fraction_evaluate,

src/py/flwr/server/strategy/fedavg.py

@@ -48,8 +48,43 @@
 """
 
 
+# pylint: disable=line-too-long
 class FedAvg(Strategy):
-    """Configurable FedAvg strategy implementation."""
+    """Federated Averaging strategy.
+
+    Implementation based on https://arxiv.org/abs/1602.05629
+
+    Parameters
+    ----------
+    fraction_fit : float, optional
+        Fraction of clients used during training. In case `min_fit_clients`
+        is larger than `fraction_fit * available_clients`, `min_fit_clients`
+        will still be sampled. Defaults to 1.0.
+    fraction_evaluate : float, optional
+        Fraction of clients used during validation. In case `min_evaluate_clients`
+        is larger than `fraction_evaluate * available_clients`,
+        `min_evaluate_clients` will still be sampled. 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.
+    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.
+    fit_metrics_aggregation_fn : Optional[MetricsAggregationFn]
+        Metrics aggregation function, optional.
+    evaluate_metrics_aggregation_fn : Optional[MetricsAggregationFn]
+        Metrics aggregation function, optional.
+    """
 
     # pylint: disable=too-many-arguments,too-many-instance-attributes, line-too-long
     def __init__(
@@ -73,41 +108,6 @@ def __init__(
         fit_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
         evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
     ) -> None:
-        """Federated Averaging strategy.
-
-        Implementation based on https://arxiv.org/abs/1602.05629
-
-        Parameters
-        ----------
-        fraction_fit : float, optional
-            Fraction of clients used during training. In case `min_fit_clients`
-            is larger than `fraction_fit * available_clients`, `min_fit_clients`
-            will still be sampled. Defaults to 1.0.
-        fraction_evaluate : float, optional
-            Fraction of clients used during validation. In case `min_evaluate_clients`
-            is larger than `fraction_evaluate * available_clients`,
-            `min_evaluate_clients` will still be sampled. 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.
-        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.
-        fit_metrics_aggregation_fn : Optional[MetricsAggregationFn]
-            Metrics aggregation function, optional.
-        evaluate_metrics_aggregation_fn : Optional[MetricsAggregationFn]
-            Metrics aggregation function, optional.
-        """
         super().__init__()
 
         if (