Rename models and add readme file

federatedscope/core/configs/cfg_fl_setting.py

@@ -82,7 +82,8 @@ def extend_fl_setting_cfg(cfg):
     # ---------------------------------------------------------------------- #
     cfg.vertical = CN()
     cfg.vertical.use = False
-    cfg.vertical.mode = 'order_based'  # ['order_based', 'label_based']
+    cfg.vertical.mode = 'feature_gathering'
+    # ['feature_gathering', 'label_scattering']
     cfg.vertical.dims = [5, 10]  # TODO: we need to explain dims
     cfg.vertical.encryption = 'paillier'
     cfg.vertical.key_size = 3072

federatedscope/vertical_fl/tree_based_models/baseline/README.md

@@ -0,0 +1,20 @@
+Here we give some hand-on examples, and roughly show their characteristics.
+
+For more details, please see the specific configuration in each file.
+
+| Yaml                                                | Dataset        | Model _type           | Algo     | Protect_method | Eval_protection |
+| --------------------------------------------------- | -------------- | --------------------- | -------- | -------------- | --------------- |
+| `gbdt_feature_gathering_on_abalone.yaml`            | Abalone (reg.) | `'feature_gathering'` | `'gbdt'` | None           | None            |
+| `gbdt_feature_gathering_on_adult.yaml`              | Adult (clas.)  | `'feature_gathering'` | `'gbdt'` | None           | None            |
+| `gbdt_label_scattering_on_adult.yaml`               | Adult (clas.)  | `'label_scattering'`  | `'gbdt'` | `'he'`         | None            |
+| `rf_feature_gathering_on_abalone.yaml`              | Abalone (reg.) | `'feature_gathering'` | `'rf'`   | None           | None            |
+| `rf_feature_gathering_on_adult.yaml`                | Adult (clas.)  | `'feature_gathering'` | `'rf'`   | None           | None            |
+| `rf_label_scattering_on_adult.yaml`                 | Adult (clas.)  | `'label_scattering'`  | `'rf'`   | `'he'`         | None            |
+| `xgb_feature_gathering_on_abalone.yaml`             | Abalone (reg.) | `'feature_gathering'` | `'xgb'`  | None           | None            |
+| `xgb_feature_gathering_on_adult.yaml`               | Adult (clas.)  | `'feature_gathering'` | `'xgb'`  | None           | None            |
+| `xgb_feature_gathering_dp_on_abalone.yaml`          | Abalone (reg.) | `'feature_gathering'` | `'xgb'`  | None           | None            |
+| `xgb_feature_gathering_dp_on_adult.yaml`            | Adult (clas.)  | `'feature_gathering'` | `'xgb'`  | `'dp'`         | None            |
+| `xgb_feature_gathering_op_boost_on_adult.yaml`      | Adult (clas.)  | `'feature_gathering'` | `'xgb'`  | `'op_boost'`   | None            |
+| `xgb_label_scattering_on_abalone.yaml`              | Abalone (reg.) | `'label_scattering'`  | `'xgb'`  | `'he'`         | None            |
+| `xgb_label_scattering_on_adult.yaml`                | Adult (clas.)  | `'label_scattering'`  | `'xgb'`  | `'he'`         | None            |
+| `xgb_feature_gathering_dp_on_adult_by_he_eval.yaml` | Adult (clas.)  | `'feature_gathering'` | `'xgb'`  | `'he'`         | `'he'`          |

federatedscope/vertical_fl/tree_based_models/baseline/rf_base_on_abalone.yaml → federatedscope/vertical_fl/tree_based_models/baseline/rf_feature_gathering_on_abalone.yaml

@@ -25,7 +25,7 @@ vertical:
   use: True
   dims: [4, 8]
   algo: 'rf'
-  data_size_for_debug: 1500
+  data_size_for_debug: 2000
   feature_subsample_ratio: 0.5
 eval:
   freq: 3

federatedscope/vertical_fl/tree_based_models/baseline/rf_base_on_adult.yaml → federatedscope/vertical_fl/tree_based_models/baseline/rf_feature_gathering_on_adult.yaml

@@ -25,7 +25,7 @@ vertical:
   use: True
   dims: [7, 14]
   algo: 'rf'
-  data_size_for_debug: 1500
+  data_size_for_debug: 2000
   feature_subsample_ratio: 0.8
 eval:
   freq: 3

federatedscope/vertical_fl/tree_based_models/baseline/rf_label_base_on_adult.yaml → federatedscope/vertical_fl/tree_based_models/baseline/rf_label_scattering_on_adult.yaml

@@ -29,7 +29,7 @@ vertical:
   use: True
   dims: [ 7, 14 ]
   algo: 'rf'
-  mode: 'label_based'
+  mode: 'label_scattering'
   data_size_for_debug: 2000
   feature_subsample_ratio: 0.4
   protect_object: 'grad_and_hess'

federatedscope/vertical_fl/tree_based_models/baseline/xgb_label_base_on_abalone.yaml → federatedscope/vertical_fl/tree_based_models/baseline/xgb_label_scattering_on_abalone.yaml

@@ -27,7 +27,7 @@ train:
     eta: 0.19
 vertical:
   use: True
-  mode: 'label_based'
+  mode: 'label_scattering'
   dims: [6, 10]
   algo: 'xgb'
   data_size_for_debug: 0

federatedscope/vertical_fl/tree_based_models/baseline/xgb_label_based_on_adult.yaml → federatedscope/vertical_fl/tree_based_models/baseline/xgb_label_scattering_on_adult.yaml

@@ -27,7 +27,7 @@ train:
     eta: 0.5
 vertical:
   use: True
-  mode: 'label_based'
+  mode: 'label_scattering'
   dims: [7, 14]
   algo: 'xgb'
   data_size_for_debug: 2000

federatedscope/vertical_fl/tree_based_models/model/Tree.py

@@ -177,11 +177,14 @@ def cal_gain(self, split_idx, y, indicator):
         else:
             raise ValueError(f'Task type error: {self.task_type}')
 
-    def cal_gain_for_rf_label_base(self, node_num, split_idx, y, indicator):
+    def cal_gain_for_rf_label_scattering(self, node_num, split_idx, y,
+                                         indicator):
+
         y_left_children_label_sum = np.sum(y[:split_idx])
         y_right_children_label_sum = np.sum(y[split_idx:])
         left_children_num = np.sum(indicator[:split_idx])
         right_children_num = np.sum(indicator[split_idx:])
+
         if self.task_type == 'classification':
             if np.sum(indicator) == np.sum(y) or np.sum(y) == 0:
                 return 0

federatedscope/vertical_fl/tree_based_models/trainer/label_protected_trainer.py

@@ -307,7 +307,7 @@ def get_best_gain_from_msg(self, msg, tree_num=None, node_num=None):
                                 continue
 
                             gain = self.model[
-                                tree_num].cal_gain_for_rf_label_base(
+                                tree_num].cal_gain_for_rf_label_scattering(
                                     node_num, value_idx, label, indicator)
                             if gain < best_gain:
                                 client_has_max_gain = client_id

federatedscope/vertical_fl/tree_based_models/trainer/trainer.py

@@ -94,7 +94,7 @@ def train(self, training_info=None, tree_num=0, node_num=None):
         # Start to build a tree
         if node_num is None:
             if training_info is not None and \
-                    self.cfg.vertical.mode == 'order_based':
+                    self.cfg.vertical.mode == 'feature_gathering':
                 self.merged_feature_order, self.extra_info = \
                     self._parse_training_info(training_info)
             return self._compute_for_root(tree_num=tree_num)
@@ -274,7 +274,7 @@ def _compute_for_node(self, tree_num, node_num):
             return self._compute_for_node(tree_num, node_num + 1)
         # Calculate best gain
         else:
-            if self.cfg.vertical.mode == 'order_based':
+            if self.cfg.vertical.mode == 'feature_gathering':
                 improved_flag, split_ref, _ = self._get_best_gain(
                     tree_num, node_num)
                 if improved_flag:
@@ -290,7 +290,7 @@ def _compute_for_node(self, tree_num, node_num):
                 else:
                     self._set_weight_and_status(tree_num, node_num)
                     return self._compute_for_node(tree_num, node_num + 1)
-            elif self.cfg.vertical.mode == 'label_based':
+            elif self.cfg.vertical.mode == 'label_scattering':
                 results = (self.model[tree_num][node_num].grad,
                            self.model[tree_num][node_num].hess,
                            self.model[tree_num][node_num].indicator, tree_num,

federatedscope/vertical_fl/tree_based_models/worker/TreeClient.py

@@ -88,14 +88,15 @@ def callback_func_for_data_sample(self, message: Message):
             index=batch_index)
         self.feature_order = feature_order_info['feature_order']
 
-        if self._cfg.vertical.mode == 'order_based':
+        if self._cfg.vertical.mode == 'feature_gathering':
             training_info = feature_order_info
-        elif self._cfg.vertical.mode == 'label_based':
+        elif self._cfg.vertical.mode == 'label_scattering':
             training_info = 'dummy_info'
         else:
-            raise TypeError(f'The expected types of vertical.mode include '
-                            f'["label_based", "order_based"], but got '
-                            f'{self._cfg.vertical.mode}.')
+            raise TypeError(
+                f'The expected types of vertical.mode include '
+                f'["label_scattering", "feature_gathering"], but got '
+                f'{self._cfg.vertical.mode}.')
 
         self.comm_manager.send(
             Message(msg_type='training_info',
@@ -122,7 +123,7 @@ def start_a_new_training_round(self,
         self.msg_buffer['train'].clear()
         self.feature_order = feature_order_info['feature_order']
         self.msg_buffer['train'][self.ID] = feature_order_info \
-            if self._cfg.vertical.mode == 'order_based' else 'dummy_info'
+            if self._cfg.vertical.mode == 'feature_gathering' else 'dummy_info'
         self.state = tree_num
         receiver = [
             each for each in list(self.comm_manager.neighbors.keys())

tests/test_tree_based_model_for_vfl.py

@@ -157,7 +157,7 @@ def set_config_for_rf_base(self, cfg):
 
         return backup_cfg
 
-    def set_config_for_rf_label_base(self, cfg):
+    def set_config_for_rf_label_scattering(self, cfg):
         backup_cfg = cfg.clone()
 
         import torch
@@ -270,7 +270,7 @@ def set_config_for_xgb_dp_too_large_noise(self, cfg):
 
         return backup_cfg
 
-    def set_config_for_label_based_xgb(self, cfg):
+    def set_config_for_label_scattering_xgb(self, cfg):
         backup_cfg = cfg.clone()
 
         import torch
@@ -298,7 +298,7 @@ def set_config_for_label_based_xgb(self, cfg):
         cfg.vertical.use = True
         cfg.vertical.dims = [7, 14]
         cfg.vertical.algo = 'xgb'
-        cfg.vertical.mode = 'label_based'
+        cfg.vertical.mode = 'label_scattering'
         cfg.vertical.protect_object = 'grad_and_hess'
         cfg.vertical.protect_method = 'he'
         cfg.vertical.data_size_for_debug = 2000
@@ -473,7 +473,7 @@ def test_RF_Base(self):
 
     def test_RF_lable_Base(self):
         init_cfg = global_cfg.clone()
-        backup_cfg = self.set_config_for_rf_label_base(init_cfg)
+        backup_cfg = self.set_config_for_rf_label_scattering(init_cfg)
         setup_seed(init_cfg.seed)
         update_logger(init_cfg, True)
 
@@ -533,9 +533,9 @@ def test_XGB_use_dp_too_large_noise(self):
         print(test_results)
         self.assertLess(test_results['server_global_eval']['test_acc'], 0.76)
 
-    def test_label_based_XGB(self):
+    def test_label_scattering_XGB(self):
         init_cfg = global_cfg.clone()
-        backup_cfg = self.set_config_for_label_based_xgb(init_cfg)
+        backup_cfg = self.set_config_for_label_scattering_xgb(init_cfg)
         setup_seed(init_cfg.seed)
         update_logger(init_cfg, True)