Added the current-round info the fl_ctx for BaseModelController

nvflare/app_common/workflows/base_model_controller.py

@@ -100,8 +100,8 @@ def _build_shareable(self, data: FLModel = None) -> Shareable:
 
     def broadcast_model(
         self,
+        data,
         task_name: str = AppConstants.TASK_TRAIN,
-        data: FLModel = None,
         targets: Union[List[Client], List[str], None] = None,
         min_responses: int = None,
         timeout: int = 0,
@@ -112,8 +112,8 @@ def broadcast_model(
         """Send a task with data to a list of targets.
 
         Args:
+            data: FLModel to be sent to clients. It must be a FLModel object. It will raise an exception if None.
             task_name (str, optional): name of the task. Defaults to "train".
-            data (FLModel, optional): FLModel to be sent to clients. If no data is given, send empty FLModel.
             targets (List[str], optional): the list of target client names or None (all clients). Defaults to None.
             min_responses (int, optional): the minimum number of responses expected. If None, must receive responses from
               all clients that the task has been sent to. Defaults to None.
@@ -128,9 +128,9 @@ def broadcast_model(
         """
 
         if not isinstance(task_name, str):
-            raise TypeError("task_name must be a string but got {}".format(type(task_name)))
-        if data and not isinstance(data, FLModel):
-            raise TypeError("data must be a FLModel or None but got {}".format(type(data)))
+            raise TypeError(f"task_name must be a string but got {type(task_name)}")
+        if not isinstance(data, FLModel):
+            raise TypeError(f"data must be a FLModel but got {type(data)}")
         if min_responses is None:
             min_responses = 0  # this is internally used by controller's broadcast to represent all targets
         check_non_negative_int("min_responses", min_responses)
@@ -139,9 +139,7 @@ def broadcast_model(
         if not blocking and not isinstance(callback, Callable):
             raise TypeError("callback must be defined if blocking is False, but got {}".format(type(callback)))
 
-        if not data:
-            self.warning("data is None. Sending empty FLModel.")
-            data = FLModel(params_type=ParamsType.FULL, params={})
+        self.set_fl_context(data)
 
         task = self._prepare_task(data=data, task_name=task_name, timeout=timeout, callback=callback)
 
@@ -224,9 +222,6 @@ def _process_result(self, client_task: ClientTask, fl_ctx: FLContext) -> None:
         result_model.meta["props"] = client_task.task.props[AppConstants.META_DATA]
         result_model.meta["client_name"] = client_name
 
-        if result_model.current_round is not None:
-            self.fl_ctx.set_prop(AppConstants.CURRENT_ROUND, result_model.current_round, private=True, sticky=True)
-
         self.event(AppEventType.BEFORE_CONTRIBUTION_ACCEPT)
         self._accept_train_result(client_name=client_name, result=result, fl_ctx=fl_ctx)
         self.event(AppEventType.AFTER_CONTRIBUTION_ACCEPT)
@@ -379,6 +374,13 @@ def sample_clients(self, num_clients: int = None) -> List[str]:
 
         return clients
 
+    def set_fl_context(self, data: FLModel):
+        """Set up the fl_ctx information based on the passed in FLModel data."""
+        if data and data.current_round is not None:
+            self.fl_ctx.set_prop(AppConstants.CURRENT_ROUND, data.current_round, private=True, sticky=True)
+        else:
+            self.debug("The FLModel data does not contain the current_round information.")
+
     def get_component(self, component_id: str):
         return self.engine.get_component(component_id)
 

research/fed-bpt/src/global_es.py

@@ -95,7 +95,7 @@ def run(self) -> None:
 
             clients = self.sample_clients(self.num_clients)
 
-            global_model = FLModel(params={"global_es": global_es})
+            global_model = FLModel(params={"global_es": global_es}, current_round=self.current_round)
             results = self.send_model_and_wait(targets=clients, data=global_model)
 
             # get solutions from clients