diff --git a/federatedscope/contrib/loss/RMSE.py b/federatedscope/contrib/loss/RMSE.py
new file mode 100644
index 0000000..6ad80ff
--- /dev/null
+++ b/federatedscope/contrib/loss/RMSE.py
@@ -0,0 +1,46 @@
+from federatedscope.register import register_criterion
+
+def call_my_criterion(type, device):
+    try:
+        import torch
+        import torch.nn as nn
+    except ImportError:
+        nn = None
+        criterion = None
+
+    class RMSELoss(nn.Module):
+        def __init__(self):
+            super(RMSELoss, self).__init__()
+            self.mse = nn.MSELoss()
+
+        def forward(self, y_pred, y_true):
+            return torch.sqrt(self.mse(y_pred, y_true))
+
+    class MAPELoss(nn.Module):
+        def __init__(self, epsilon=1e-10):
+            super(MAPELoss, self).__init__()
+            self.epsilon = epsilon
+
+        def forward(self, y_pred, y_true):
+            mask_value = 0.1
+            if mask_value is not None:
+                mask = torch.gt(y_true, mask_value)
+                pred = torch.masked_select(y_pred, mask)
+                true = torch.masked_select(y_true, mask)
+            return torch.mean(torch.abs(torch.div((true - pred), (true + 0.001)))) * 100
+
+
+    if type == 'RMSE':
+        if nn is not None:
+            criterion = RMSELoss().to(device)
+    elif type == 'MAPE':
+        if nn is not None:
+            criterion = MAPELoss().to(device)
+    else:
+        criterion = None
+
+    return criterion
+
+# Register the custom RMSE and MAPE criterion
+register_criterion('RMSE', call_my_criterion)
+register_criterion('MAPE', call_my_criterion)
diff --git a/federatedscope/core/auxiliaries/dataloader_builder.py b/federatedscope/core/auxiliaries/dataloader_builder.py
index 4b95741..5eea675 100644
--- a/federatedscope/core/auxiliaries/dataloader_builder.py
+++ b/federatedscope/core/auxiliaries/dataloader_builder.py
@@ -62,6 +62,10 @@ def get_dataloader(dataset, config, split='train'):
     elif config.dataloader.type == 'mf':
         from federatedscope.mf.dataloader import MFDataLoader
         loader_cls = MFDataLoader
+    elif config.dataloader.type == 'trafficflow':
+    # 待定
+        from torch.utils.data import DataLoader
+        loader_cls = DataLoader
     else:
         raise ValueError(f'data.loader.type {config.data.loader.type} '
                          f'not found!')