diff --git a/federatedscope/vertical_fl/model/Tree.py b/federatedscope/vertical_fl/model/Tree.py
index 9f9a18789..bf8ccc65c 100644
--- a/federatedscope/vertical_fl/model/Tree.py
+++ b/federatedscope/vertical_fl/model/Tree.py
@@ -41,6 +41,14 @@ def split_childern(self, data, feature_value):
     def set_status(self, node_num, status='off'):
         self.tree[node_num].status = status
 
+    def check_empty_child(self, node_num, split_idx, order):
+        indicator = self.tree[node_num].indicator[order]
+        if np.sum(indicator[:split_idx]) == 0 or np.sum(
+                indicator[split_idx:]) == 0:
+            return True
+
+        return False
+
 
 class XGBTree(Tree):
     def __init__(self, max_depth, lambda_, gamma):
diff --git a/federatedscope/vertical_fl/trainer/label_protected_trainer.py b/federatedscope/vertical_fl/trainer/label_protected_trainer.py
index 2ba968ea2..569c5bfc1 100644
--- a/federatedscope/vertical_fl/trainer/label_protected_trainer.py
+++ b/federatedscope/vertical_fl/trainer/label_protected_trainer.py
@@ -95,8 +95,8 @@ def get_feature_value(self, feature_idx, value_idx):
         def get_abs_value_idx(self, feature_idx, value_idx):
             if self.extra_info is not None and self.extra_info.get(
                     'split_position', None) is not None:
-                return self.extra_info['split_position'][feature_idx][
-                    value_idx]
+                return self.extra_info['split_position'][feature_idx][value_idx
+                                                                      - 1]
             else:
                 return value_idx
 
diff --git a/federatedscope/vertical_fl/trainer/random_forest_trainer.py b/federatedscope/vertical_fl/trainer/random_forest_trainer.py
index be60a12d5..558b34d65 100644
--- a/federatedscope/vertical_fl/trainer/random_forest_trainer.py
+++ b/federatedscope/vertical_fl/trainer/random_forest_trainer.py
@@ -57,14 +57,6 @@ def _get_best_gain(self, tree_num, node_num, grad=None, hess=None):
         if split_position is None:
             # The left/right sub-tree cannot be empty
             split_position = activate_idx[:, 1:]
-        else:
-            active_split_position = list()
-            for idx, each_split_position in enumerate(split_position):
-                active_split_position.append([
-                    x for x in each_split_position
-                    if x in activate_idx[idx, 1:]
-                ])
-            split_position = active_split_position
 
         for feature_idx in range(feature_num):
             if len(split_position[feature_idx]) == 0:
@@ -72,7 +64,11 @@ def _get_best_gain(self, tree_num, node_num, grad=None, hess=None):
             ordered_indicator, ordered_label = \
                 self._get_ordered_indicator_and_label(
                     tree_num, node_num, feature_idx)
+            order = self.merged_feature_order[feature_idx]
             for value_idx in split_position[feature_idx]:
+                if self.model[tree_num].check_empty_child(
+                        node_num, value_idx, order):
+                    continue
                 gain = self.model[tree_num].cal_gain(value_idx, ordered_label,
                                                      ordered_indicator)
                 if gain < best_gain:
diff --git a/federatedscope/vertical_fl/trainer/trainer.py b/federatedscope/vertical_fl/trainer/trainer.py
index 5bc263999..8eaa7a620 100644
--- a/federatedscope/vertical_fl/trainer/trainer.py
+++ b/federatedscope/vertical_fl/trainer/trainer.py
@@ -194,19 +194,15 @@ def _get_best_gain(self, tree_num, node_num, grad=None, hess=None):
         if split_position is None:
             # The left/right sub-tree cannot be empty
             split_position = activate_idx[:, 1:]
-        else:
-            active_split_position = list()
-            for idx, each_split_position in enumerate(split_position):
-                active_split_position.append([
-                    x for x in each_split_position
-                    if x in activate_idx[idx, 1:]
-                ])
-            split_position = active_split_position
 
         for feature_idx in range(feature_num):
             ordered_g, ordered_h = self._get_ordered_gh(
                 tree_num, node_num, feature_idx, grad, hess)
+            order = self.merged_feature_order[feature_idx]
             for value_idx in split_position[feature_idx]:
+                if self.model[tree_num].check_empty_child(
+                        node_num, value_idx, order):
+                    continue
                 gain = self.model[tree_num].cal_gain(ordered_g, ordered_h,
                                                      value_idx, node_num)