diff --git a/python-package/xgboost/spark/core.py b/python-package/xgboost/spark/core.py
index c38fcbffd48a..bd461d49b5cd 100644
--- a/python-package/xgboost/spark/core.py
+++ b/python-package/xgboost/spark/core.py
@@ -2,7 +2,6 @@
 """Xgboost pyspark integration submodule for core code."""
 # pylint: disable=fixme, too-many-ancestors, protected-access, no-member, invalid-name
 # pylint: disable=too-few-public-methods
-import cloudpickle
 import numpy as np
 import pandas as pd
 from scipy.special import expit, softmax  # pylint: disable=no-name-in-module
@@ -410,12 +409,13 @@ def _pyspark_model_cls(cls):
     def _create_pyspark_model(self, xgb_model):
         return self._pyspark_model_cls()(xgb_model)
 
-    def _convert_to_sklearn_model(self, booster):
+    def _convert_to_sklearn_model(self, booster: bytearray, config: str):
         xgb_sklearn_params = self._gen_xgb_params_dict(
             gen_xgb_sklearn_estimator_param=True
         )
         sklearn_model = self._xgb_cls()(**xgb_sklearn_params)
-        sklearn_model._Booster = booster
+        sklearn_model.load_model(booster)
+        sklearn_model._Booster.load_config(config)
         return sklearn_model
 
     def _query_plan_contains_valid_repartition(self, dataset):
@@ -629,16 +629,27 @@ def _train_booster(pandas_df_iter):
             context.barrier()
 
             if context.partitionId() == 0:
-                yield pd.DataFrame(data={"booster_bytes": [cloudpickle.dumps(booster)]})
+                yield pd.DataFrame(
+                    data={
+                        "config": [booster.save_config()],
+                        "booster": [booster.save_raw("json").decode("utf-8")]
+                    }
+                )
+
+        def _run_job():
+            ret = (
+                dataset.mapInPandas(_train_booster, schema="config string, booster string")
+                .rdd.barrier()
+                .mapPartitions(lambda x: x)
+                .collect()[0]
+            )
+            return ret[0], ret[1]
+
+        (config, booster) = _run_job()
 
-        result_ser_booster = (
-            dataset.mapInPandas(_train_booster, schema="booster_bytes binary")
-            .rdd.barrier()
-            .mapPartitions(lambda x: x)
-            .collect()[0][0]
-        )
         result_xgb_model = self._convert_to_sklearn_model(
-            cloudpickle.loads(result_ser_booster)
+            bytearray(booster, "utf-8"),
+            config
         )
         return self._copyValues(self._create_pyspark_model(result_xgb_model))
 
diff --git a/tests/python/test_spark/test_spark_local.py b/tests/python/test_spark/test_spark_local.py
index f57e9b874d8a..3b04a002a1e2 100644
--- a/tests/python/test_spark/test_spark_local.py
+++ b/tests/python/test_spark/test_spark_local.py
@@ -904,7 +904,8 @@ def test_convert_to_sklearn_model(self):
 
         # Check that regardless of what booster, _convert_to_model converts to the correct class type
         sklearn_classifier = classifier._convert_to_sklearn_model(
-            clf_model.get_booster()
+            clf_model.get_booster().save_raw("json"),
+            clf_model.get_booster().save_config()
         )
         assert isinstance(sklearn_classifier, XGBClassifier)
         assert sklearn_classifier.n_estimators == 200
@@ -912,7 +913,10 @@ def test_convert_to_sklearn_model(self):
         assert sklearn_classifier.max_depth == 3
         assert sklearn_classifier.get_params()["sketch_eps"] == 0.5
 
-        sklearn_regressor = regressor._convert_to_sklearn_model(reg_model.get_booster())
+        sklearn_regressor = regressor._convert_to_sklearn_model(
+            reg_model.get_booster().save_raw("json"),
+            reg_model.get_booster().save_config()
+        )
         assert isinstance(sklearn_regressor, XGBRegressor)
         assert sklearn_regressor.n_estimators == 200
         assert sklearn_regressor.missing == 2.0