diff --git a/federatedscope/core/configs/cfg_evaluation.py b/federatedscope/core/configs/cfg_evaluation.py
index 09b9cdd48..df1a1ed97 100644
--- a/federatedscope/core/configs/cfg_evaluation.py
+++ b/federatedscope/core/configs/cfg_evaluation.py
@@ -19,7 +19,6 @@ def extend_evaluation_cfg(cfg):
 
     # Monitoring, e.g., 'dissim' for B-local dissimilarity
     cfg.eval.monitoring = []
-
     cfg.eval.count_flops = True
 
     # ---------------------------------------------------------------------- #
diff --git a/federatedscope/core/data/base_data.py b/federatedscope/core/data/base_data.py
index 499cd344a..72421eac4 100644
--- a/federatedscope/core/data/base_data.py
+++ b/federatedscope/core/data/base_data.py
@@ -1,4 +1,7 @@
 import logging
+
+from scipy.sparse.csc import csc_matrix
+
 from federatedscope.core.data.utils import merge_data
 from federatedscope.core.auxiliaries.dataloader_builder import get_dataloader
 
@@ -145,13 +148,16 @@ class ClientData(dict):
         test: test dataset, which will be converted to ``Dataloader``
 
     Note:
-        Key ``data`` in ``ClientData`` is the raw dataset.
+        Key ``{split}_data`` in ``ClientData`` is the raw dataset.
+        Key ``{split}`` in ``ClientData`` is the dataloader.
     """
+    SPLIT_NAMES = ['train', 'val', 'test']
+
     def __init__(self, client_cfg, train=None, val=None, test=None, **kwargs):
         self.client_cfg = None
-        self.train = train
-        self.val = val
-        self.test = test
+        self.train_data = train
+        self.val_data = val
+        self.test_data = test
         self.setup(client_cfg)
         if kwargs is not None:
             for key in kwargs:
@@ -168,18 +174,26 @@ def setup(self, new_client_cfg=None):
         Returns:
             Bool: Status for indicating whether the client_cfg is updated
         """
-        # if `batch_size` or `shuffle` change, reinstantiate DataLoader
+        # if `batch_size` or `shuffle` change, re-instantiate DataLoader
         if self.client_cfg is not None:
             if dict(self.client_cfg.dataloader) == dict(
                     new_client_cfg.dataloader):
                 return False
 
         self.client_cfg = new_client_cfg
-        if self.train is not None:
-            self['train'] = get_dataloader(self.train, self.client_cfg,
-                                           'train')
-        if self.val is not None:
-            self['val'] = get_dataloader(self.val, self.client_cfg, 'val')
-        if self.test is not None:
-            self['test'] = get_dataloader(self.test, self.client_cfg, 'test')
+
+        for split_data, split_name in zip(
+            [self.train_data, self.val_data, self.test_data],
+                self.SPLIT_NAMES):
+            if split_data is not None:
+                # csc_matrix does not have ``__len__`` attributes
+                if isinstance(split_data, csc_matrix):
+                    self[split_name] = get_dataloader(split_data,
+                                                      self.client_cfg,
+                                                      split_name)
+                elif len(split_data) > 0:
+                    self[split_name] = get_dataloader(split_data,
+                                                      self.client_cfg,
+                                                      split_name)
+
         return True