diff --git a/openfl/component/collaborator/collaborator.py b/openfl/component/collaborator/collaborator.py
index 7a2f0aee9d..a8d6024719 100644
--- a/openfl/component/collaborator/collaborator.py
+++ b/openfl/component/collaborator/collaborator.py
@@ -152,6 +152,7 @@ def __init__(
         self.callbacks = callbacks_module.CallbackList(
             callbacks,
             add_memory_profiler=log_memory_usage,
+            add_metric_writer=write_logs,
             origin=self.collaborator_name,
         )
 
@@ -184,12 +185,14 @@ def run(self):
             self.callbacks.on_round_begin(round_num)
 
             # Run tasks
+            logs = {}
             for task in tasks:
-                self.do_task(task, round_num)
+                metrics = self.do_task(task, round_num)
+                logs.update(metrics)
 
             # Round end
             self.tensor_db.clean_up(self.db_store_rounds)
-            self.callbacks.on_round_end(round_num)
+            self.callbacks.on_round_end(round_num, logs)
 
         # Experiment end
         self.callbacks.on_experiment_end()
@@ -235,12 +238,15 @@ def get_tasks(self):
 
         return tasks, round_number, sleep_time, time_to_quit
 
-    def do_task(self, task, round_number):
+    def do_task(self, task, round_number) -> dict:
         """Perform the specified task.
 
         Args:
             task (list_of_str): List of tasks.
             round_number (int): Actual round number.
+
+        Returns:
+            A dictionary of reportable metrics of the current collaborator for the task.
         """
         # map this task to an actual function name and kwargs
         if hasattr(self.task_runner, "TASK_REGISTRY"):
@@ -329,7 +335,8 @@ def do_task(self, task, round_number):
 
         # send the results for this tasks; delta and compression will occur in
         # this function
-        self.send_task_results(global_output_tensor_dict, round_number, task_name)
+        metrics = self.send_task_results(global_output_tensor_dict, round_number, task_name)
+        return metrics
 
     def get_numpy_dict_for_tensorkeys(self, tensor_keys):
         """Get tensor dictionary for specified tensorkey set.
@@ -462,13 +469,16 @@ def get_aggregated_tensor_from_aggregator(self, tensor_key, require_lossless=Fal
 
         return nparray
 
-    def send_task_results(self, tensor_dict, round_number, task_name):
+    def send_task_results(self, tensor_dict, round_number, task_name) -> dict:
         """Send task results to the aggregator.
 
         Args:
             tensor_dict (dict): Tensor dictionary.
             round_number (int):  Actual round number.
             task_name (string): Task name.
+
+        Returns:
+            A dictionary of reportable metrics of the current collaborator for the task.
         """
         named_tensors = [self.nparray_to_named_tensor(k, v) for k, v in tensor_dict.items()]
 
@@ -485,15 +495,19 @@ def send_task_results(self, tensor_dict, round_number, task_name):
 
         logger.debug("%s data size = %s", task_name, data_size)
 
+        metrics = {}
         for tensor in tensor_dict:
             tensor_name, origin, fl_round, report, tags = tensor
 
             if report:
-                logger.info(
-                    f"Round {round_number}, collaborator {self.collaborator_name} "
-                    f"is sending metric for task {task_name}:"
-                    f" {tensor_name}\t{tensor_dict[tensor]:f}"
+                # This tensor must be a scalar metric
+                assert (
+                    tensor_dict[tensor].ndim == 0
+                ), "Expected `report` tensor to be a scalar, instead got one with shape {}".format(
+                    tensor_dict[tensor].shape
                 )
+                value = float(tensor_dict[tensor])
+                metrics.update({f"{self.collaborator_name}/{task_name}/{tensor_name}": value})
 
         self.client.send_local_task_results(
             self.collaborator_name,
@@ -503,6 +517,8 @@ def send_task_results(self, tensor_dict, round_number, task_name):
             named_tensors,
         )
 
+        return metrics
+
     def nparray_to_named_tensor(self, tensor_key, nparray):
         """Construct the NamedTensor Protobuf.