diff --git a/python/fate/ml/glm/hetero/sshe/sshe_linr.py b/python/fate/ml/glm/hetero/sshe/sshe_linr.py
index 11193d541b..d8cdf8753c 100644
--- a/python/fate/ml/glm/hetero/sshe/sshe_linr.py
+++ b/python/fate/ml/glm/hetero/sshe/sshe_linr.py
@@ -168,6 +168,7 @@ def fit_single_model(self, ctx: Context, train_data: DataFrame, valid_data: Data
             initialize_func = lambda x: self.w
         if self.init_param.get("fit_intercept"):
             train_data["intercept"] = 1.0
+        train_data_n = train_data.shape[0]
         layer = SSHELinearRegressionLayer(
             ctx,
             in_features_a=ctx.mpc.option_call(lambda: train_data.shape[1], dst=rank_a),
@@ -210,12 +211,13 @@ def fit_single_model(self, ctx: Context, train_data: DataFrame, valid_data: Data
                 loss = loss_fn(z, y)
                 if i % self.reveal_loss_freq == 0:
                     if epoch_loss is None:
-                        epoch_loss = loss.get(dst=rank_b)
+                        epoch_loss = loss.get(dst=rank_b) * h.shape[0]
                     else:
-                        epoch_loss += loss.get(dst=rank_b)
+                        epoch_loss += loss.get(dst=rank_b) * h.shape[0]
                 loss.backward()
                 optimizer.step()
             if epoch_loss is not None and ctx.is_on_guest:
+                epoch_loss = epoch_loss / train_data_n
                 epoch_ctx.metrics.log_loss("linr_loss", epoch_loss.tolist())
             # if self.reveal_every_epoch:
             #    wa_p = wa.get_plain_text(dst=rank_a)
diff --git a/python/fate/ml/glm/hetero/sshe/sshe_lr.py b/python/fate/ml/glm/hetero/sshe/sshe_lr.py
index 0de7e172e6..bd362e8838 100644
--- a/python/fate/ml/glm/hetero/sshe/sshe_lr.py
+++ b/python/fate/ml/glm/hetero/sshe/sshe_lr.py
@@ -272,6 +272,7 @@ def fit_single_model(self, ctx: Context, train_data: DataFrame, valid_data: Data
             initialize_func = lambda x: self.w
         if self.init_param.get("fit_intercept"):
             train_data["intercept"] = 1.0
+        train_data_n = train_data.shape[0]
         layer = SSHELogisticRegressionLayer(
             ctx,
             in_features_a=ctx.mpc.option_call(lambda: train_data.shape[1], dst=rank_a),
@@ -314,12 +315,13 @@ def fit_single_model(self, ctx: Context, train_data: DataFrame, valid_data: Data
                 loss = loss_fn(z, y)
                 if i % self.reveal_loss_freq == 0:
                     if epoch_loss is None:
-                        epoch_loss = loss.get(dst=rank_b)
+                        epoch_loss = loss.get(dst=rank_b) * h.shape[0]
                     else:
-                        epoch_loss += loss.get(dst=rank_b)
+                        epoch_loss += loss.get(dst=rank_b) * h.shape[0]
                 loss.backward()
                 optimizer.step()
             if epoch_loss is not None and ctx.is_on_guest:
+                epoch_loss = epoch_loss / train_data_n
                 epoch_ctx.metrics.log_loss("lr_loss", epoch_loss.tolist())
             # if self.reveal_every_epoch:
             #    wa_p = wa.get_plain_text(dst=rank_a)