Remove presently unused on_conclude_round from Strategy

doc/source/implementing-strategies.rst

@@ -43,11 +43,6 @@ The strategy abstraction defines a few abstract methods that need to be implemen
         @abstractmethod
         def evaluate(self, weights: Weights) -> Optional[Tuple[float, float]]:
 
-        @abstractmethod
-        def on_conclude_round(
-            self, rnd: int, loss: Optional[float], acc: Optional[float]
-        ) -> bool:
-
 Creating a new strategy means implmenting a new :code:`class` derived from the abstract base class :code:`Strategy` which provides implementations for the previously shown abstract methods:
 
 .. code-block:: python
@@ -69,9 +64,6 @@ Creating a new strategy means implmenting a new :code:`class` derived from the a
         def evaluate(self, weights):
             # Your implementation here
 
-        def on_conclude_round(self, rnd, loss, acc):
-            # Your implementation here
-
 The following sections describe each of those methods in detail.
 
 The :code:`on_configure_fit` method
@@ -98,8 +90,3 @@ The :code:`evaluate` method
 ---------------------------
 
 *coming soon*
-
-The :code:`on_conclude_round` method
-------------------------------------
-
-*coming soon*

src/py/flwr/server/server.py

@@ -114,13 +114,6 @@ def fit(self, num_rounds: int) -> History:
                     rnd=current_round, loss=cast(float, loss_fed)
                 )
 
-            # Conclude round
-            loss = res_cen[0] if res_cen is not None else None
-            acc = res_cen[1] if res_cen is not None else None
-            should_continue = self.strategy.on_conclude_round(current_round, loss, acc)
-            if not should_continue:
-                break
-
         # Send shutdown signal to all clients
         all_clients = self._client_manager.all()
         _ = shutdown(clients=[all_clients[k] for k in all_clients.keys()])

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

@@ -176,9 +176,3 @@ def on_aggregate_evaluate(
                 for _, evaluate_res in results
             ]
         )
-
-    def on_conclude_round(
-        self, rnd: int, loss: Optional[float], acc: Optional[float]
-    ) -> bool:
-        """Always continue training."""
-        return True

src/py/flwr/server/strategy/qffedavg.py

@@ -220,9 +220,3 @@ def on_aggregate_evaluate(
                 for client, evaluate_res in results
             ]
         )
-
-    def on_conclude_round(
-        self, rnd: int, loss: Optional[float], acc: Optional[float]
-    ) -> bool:
-        """Always continue training."""
-        return True

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

@@ -135,19 +135,3 @@ def evaluate(self, weights: Weights) -> Optional[Tuple[float, float]]:
         Returns:
             The evaluation result, usually a Tuple containing loss and accuracy.
         """
-
-    @abstractmethod
-    def on_conclude_round(
-        self, rnd: int, loss: Optional[float], acc: Optional[float]
-    ) -> bool:
-        """Conclude federated learning round and decide whether to continue or
-        not.
-
-        Arguments:
-            rnd: int. The current round of federated learning.
-            loss: Optional[float]. The loss of the global model weights.
-            acc: Optional[float]. The accuracy of the global model weights.
-
-        Returns:
-            A boolean indicating whether the server should continue or not.
-        """