diff --git a/fastdeploy/worker/worker_process.py b/fastdeploy/worker/worker_process.py
index 668229183..e5b184b3c 100644
--- a/fastdeploy/worker/worker_process.py
+++ b/fastdeploy/worker/worker_process.py
@@ -417,17 +417,11 @@ class PaddleDisWorkerProc:
         while True:
             # run eplb
             self._run_eplb(tp_rank)
-            if tp_rank == 0:
+
+            if self.fd_config.load_config.dynamic_load_weight:
                 if self.model_weights_status.value[0] != ModelWeightsStatus.NORMAL:
                     self.model_weights_signal[0] = int(self.model_weights_status.value[0])
-                if self.fd_config.load_config.dynamic_load_weight and self.parallel_config.enable_expert_parallel:
-                    self.model_weights_signal[0] = self._broadcast_model_weights_signal(
-                        src=0, group=self.parallel_config.ep_group
-                    )
-            if self.fd_config.load_config.dynamic_load_weight and tp_size > 1:
-                self.model_weights_signal[0] = self._broadcast_model_weights_signal(
-                    src=0, group=self.parallel_config.tp_group
-                )
+                    self.model_weights_signal[0] = self._broadcast_model_weights_signal(src=0, group=None)
 
             req_dicts = None
             self.worker_healthy_live_signal.value[tp_rank % self.max_chips_per_node] = int(time.time())
@@ -447,10 +441,7 @@ class PaddleDisWorkerProc:
             self._tp_barrier_wait() if tp_size > 1 else None
 
             if self.fd_config.load_config.dynamic_load_weight:
-                if self.parallel_config.enable_expert_parallel:
-                    paddle.distributed.barrier(self.parallel_config.ep_group)
-                else:
-                    paddle.distributed.barrier(self.parallel_config.tp_group)
+                paddle.distributed.barrier()
                 if self.model_weights_signal[0] != ModelWeightsStatus.NORMAL:
                     logger.info(
                         f"Rank: {self.local_rank} to update or clear parameters, signal is {self.model_weights_signal[0]}, [-1:clear, 1:update]"