diff --git a/python/pyspark/sql/pandas/serializers.py b/python/pyspark/sql/pandas/serializers.py
index 667af40c36bc..b17daa386d45 100644
--- a/python/pyspark/sql/pandas/serializers.py
+++ b/python/pyspark/sql/pandas/serializers.py
@@ -1134,7 +1134,8 @@ def __repr__(self):
         return "GroupArrowUDFSerializer"
 
 
-# Serializer for SQL_GROUPED_AGG_ARROW_UDF and SQL_WINDOW_AGG_ARROW_UDF
+# Serializer for SQL_GROUPED_AGG_ARROW_UDF, SQL_WINDOW_AGG_ARROW_UDF,
+# and SQL_GROUPED_AGG_ARROW_ITER_UDF
 class ArrowStreamAggArrowUDFSerializer(ArrowStreamArrowUDFSerializer):
     def __init__(
         self,
@@ -1156,59 +1157,8 @@ def __init__(
 
     def load_stream(self, stream):
         """
-        Flatten the struct into Arrow's record batches.
-        """
-        import pyarrow as pa
-
-        dataframes_in_group = None
-
-        while dataframes_in_group is None or dataframes_in_group > 0:
-            dataframes_in_group = read_int(stream)
-
-            if dataframes_in_group == 1:
-                batches = ArrowStreamSerializer.load_stream(self, stream)
-                if hasattr(pa, "concat_batches"):
-                    yield pa.concat_batches(batches)
-                else:
-                    # pyarrow.concat_batches not supported in old versions
-                    yield pa.RecordBatch.from_struct_array(
-                        pa.concat_arrays([b.to_struct_array() for b in batches])
-                    )
-
-            elif dataframes_in_group != 0:
-                raise PySparkValueError(
-                    errorClass="INVALID_NUMBER_OF_DATAFRAMES_IN_GROUP",
-                    messageParameters={"dataframes_in_group": str(dataframes_in_group)},
-                )
-
-    def __repr__(self):
-        return "ArrowStreamAggArrowUDFSerializer"
-
-
-# Serializer for SQL_GROUPED_AGG_ARROW_ITER_UDF
-class ArrowStreamAggArrowIterUDFSerializer(ArrowStreamArrowUDFSerializer):
-    def __init__(
-        self,
-        timezone,
-        safecheck,
-        assign_cols_by_name,
-        arrow_cast,
-    ):
-        super().__init__(
-            timezone=timezone,
-            safecheck=safecheck,
-            assign_cols_by_name=False,
-            arrow_cast=True,
-        )
-        self._timezone = timezone
-        self._safecheck = safecheck
-        self._assign_cols_by_name = assign_cols_by_name
-        self._arrow_cast = arrow_cast
-
-    def load_stream(self, stream):
-        """
-        Yield an iterator that produces one list of column arrays per batch.
-        Each group yields Iterator[List[pa.Array]], allowing UDF to process batches one by one
+        Yield an iterator that produces one tuple of column arrays per batch.
+        Each group yields Iterator[Tuple[pa.Array, ...]], allowing UDF to process batches one by one
         without consuming all batches upfront.
         """
         dataframes_in_group = None
@@ -1234,7 +1184,7 @@ def load_stream(self, stream):
                 )
 
     def __repr__(self):
-        return "ArrowStreamAggArrowIterUDFSerializer"
+        return "ArrowStreamAggArrowUDFSerializer"
 
 
 # Serializer for SQL_GROUPED_AGG_PANDAS_UDF and SQL_WINDOW_AGG_PANDAS_UDF
diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index 50e71fb6da9d..7f83f8e2cd6b 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -66,7 +66,6 @@
     ArrowStreamArrowUDFSerializer,
     ArrowStreamAggPandasUDFSerializer,
     ArrowStreamAggArrowUDFSerializer,
-    ArrowStreamAggArrowIterUDFSerializer,
     ArrowBatchUDFSerializer,
     ArrowStreamUDTFSerializer,
     ArrowStreamArrowUDTFSerializer,
@@ -2737,12 +2736,9 @@ def read_udfs(pickleSer, infile, eval_type):
             or eval_type == PythonEvalType.SQL_GROUPED_MAP_ARROW_ITER_UDF
         ):
             ser = GroupArrowUDFSerializer(runner_conf.assign_cols_by_name)
-        elif eval_type == PythonEvalType.SQL_GROUPED_AGG_ARROW_ITER_UDF:
-            ser = ArrowStreamAggArrowIterUDFSerializer(
-                runner_conf.timezone, True, runner_conf.assign_cols_by_name, True
-            )
         elif eval_type in (
             PythonEvalType.SQL_GROUPED_AGG_ARROW_UDF,
+            PythonEvalType.SQL_GROUPED_AGG_ARROW_ITER_UDF,
             PythonEvalType.SQL_WINDOW_AGG_ARROW_UDF,
         ):
             ser = ArrowStreamAggArrowUDFSerializer(
@@ -3265,6 +3261,44 @@ def mapper(a):
                 batch_iter = (tuple(batch_columns[o] for o in arg_offsets) for batch_columns in a)
             return f(batch_iter)
 
+    elif eval_type in (
+        PythonEvalType.SQL_GROUPED_AGG_ARROW_UDF,
+        PythonEvalType.SQL_WINDOW_AGG_ARROW_UDF,
+    ):
+        import pyarrow as pa
+
+        # For SQL_GROUPED_AGG_ARROW_UDF and SQL_WINDOW_AGG_ARROW_UDF,
+        # convert iterator of batch columns to a concatenated RecordBatch
+        def mapper(a):
+            # a is Iterator[Tuple[pa.Array, ...]] - convert to RecordBatch
+            batches = []
+            for batch_columns in a:
+                # batch_columns is Tuple[pa.Array, ...] - convert to RecordBatch
+                batch = pa.RecordBatch.from_arrays(
+                    batch_columns, names=["_%d" % i for i in range(len(batch_columns))]
+                )
+                batches.append(batch)
+
+            # Concatenate all batches into one
+            if hasattr(pa, "concat_batches"):
+                concatenated_batch = pa.concat_batches(batches)
+            else:
+                # pyarrow.concat_batches not supported in old versions
+                concatenated_batch = pa.RecordBatch.from_struct_array(
+                    pa.concat_arrays([b.to_struct_array() for b in batches])
+                )
+
+            # Extract series using offsets (concatenated_batch.columns[o] gives pa.Array)
+            result = tuple(
+                f(*[concatenated_batch.columns[o] for o in arg_offsets]) for arg_offsets, f in udfs
+            )
+            # In the special case of a single UDF this will return a single result rather
+            # than a tuple of results; this is the format that the JVM side expects.
+            if len(result) == 1:
+                return result[0]
+            else:
+                return result
+
     else:
 
         def mapper(a):