diff --git a/examples/getting_started/pt/swarm_script_executor_cifar10.py b/examples/getting_started/pt/swarm_script_executor_cifar10.py
index 4a04f10f40..adf59091c5 100644
--- a/examples/getting_started/pt/swarm_script_executor_cifar10.py
+++ b/examples/getting_started/pt/swarm_script_executor_cifar10.py
@@ -47,19 +47,22 @@
         executor = ScriptExecutor(task_script_path=train_script)
         job.to(executor, f"site-{i}", gpu=0, tasks=["train", "validate", "submit_model"])
 
-        client_controller = SwarmClientController()
-        job.to(client_controller, f"site-{i}", tasks=["swarm_*"])
-
-        client_controller = CrossSiteEvalClientController()
-        job.to(client_controller, f"site-{i}", tasks=["cse_*"])
-
         # In swarm learning, each client acts also as an aggregator
         aggregator = InTimeAccumulateWeightedAggregator(expected_data_kind=DataKind.WEIGHTS)
-        job.to(aggregator, f"site-{i}")
 
         # In swarm learning, each client uses a model persistor and shareable_generator
-        job.to(PTFileModelPersistor(model=Net()), f"site-{i}")
-        job.to(SimpleModelShareableGenerator(), f"site-{i}")
+        persistor = PTFileModelPersistor(model=Net())
+        shareable_generator = SimpleModelShareableGenerator()
+
+        client_controller = SwarmClientController(
+            aggregator_id=job.as_id(aggregator),
+            persistor_id=job.as_id(persistor),
+            shareable_generator_id=job.as_id(shareable_generator),
+        )
+        job.to(client_controller, f"site-{i}", tasks=["swarm_*"])
+
+        client_controller = CrossSiteEvalClientController()
+        job.to(client_controller, f"site-{i}", tasks=["cse_*"])
 
     # job.export_job("/tmp/nvflare/jobs/job_config")
     job.simulator_run("/tmp/nvflare/jobs/workdir")