diff --git a/python/fate/ml/glm/homo/lr/client.py b/python/fate/ml/glm/homo/lr/client.py
index e5578d9a06..d5eb16aafe 100644
--- a/python/fate/ml/glm/homo/lr/client.py
+++ b/python/fate/ml/glm/homo/lr/client.py
@@ -399,20 +399,17 @@ def predict(self, ctx: Context, predict_data: DataFrame) -> DataFrame:
         if self.model is None:
             raise ValueError("model is not initialized")
         self.predict_set = self._make_dataset(predict_data)
-        if self.trainer is None:
-            batch_size = len(self.predict_set) if self.batch_size is None else self.batch_size
-            train_arg = TrainingArguments(num_train_epochs=self.max_iter, per_device_eval_batch_size=batch_size)
-            trainer = FedAVGClient(
-                ctx,
-                train_set=self.predict_set,
-                model=self.model,
-                training_args=train_arg,
-                fed_args=FedAVGArguments(),
-                data_collator=default_data_collator,
-            )
-            trainer.set_local_mode()
-        else:
-            trainer = self.trainer
+        batch_size = len(self.predict_set) if self.batch_size is None else self.batch_size
+        train_arg = TrainingArguments(num_train_epochs=self.max_iter, per_device_eval_batch_size=batch_size)
+        trainer = FedAVGClient(
+            ctx,
+            train_set=self.predict_set,
+            model=self.model,
+            training_args=train_arg,
+            fed_args=FedAVGArguments(),
+            data_collator=default_data_collator,
+        )
+        trainer.set_local_mode()
         predict_rs = trainer.predict(self.predict_set)
         predict_out_df = self._make_output_df(ctx, predict_rs, self.predict_set, self.threshold)
         return predict_out_df