diff --git a/datasets/flwr_datasets/partitioner/pathological_partitioner.py b/datasets/flwr_datasets/partitioner/pathological_partitioner.py
index 350383f344e..d114ccbda02 100644
--- a/datasets/flwr_datasets/partitioner/pathological_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/pathological_partitioner.py
@@ -225,7 +225,7 @@ def _determine_partition_id_to_unique_labels(self) -> None:
         if self._class_assignment_mode == "first-deterministic":
             # if self._first_class_deterministic_assignment:
             for partition_id in range(self._num_partitions):
-                label = partition_id % num_unique_classes
+                label = self._unique_labels[partition_id % num_unique_classes]
                 self._partition_id_to_unique_labels[partition_id].append(label)
 
                 while (
diff --git a/datasets/flwr_datasets/partitioner/pathological_partitioner_test.py b/datasets/flwr_datasets/partitioner/pathological_partitioner_test.py
index 18707a56bd9..5a3b13bb143 100644
--- a/datasets/flwr_datasets/partitioner/pathological_partitioner_test.py
+++ b/datasets/flwr_datasets/partitioner/pathological_partitioner_test.py
@@ -18,7 +18,7 @@
 import unittest
 
 import numpy as np
-from parameterized import parameterized
+from parameterized import parameterized, parameterized_class
 
 import datasets
 from datasets import Dataset
@@ -26,7 +26,10 @@
 
 
 def _dummy_dataset_setup(
-    num_samples: int, partition_by: str, num_unique_classes: int
+    num_samples: int,
+    partition_by: str,
+    num_unique_classes: int,
+    string_partition_by: bool = False,
 ) -> Dataset:
     """Create a dummy dataset for testing."""
     data = {
@@ -35,6 +38,8 @@ def _dummy_dataset_setup(
         )[:num_samples],
         "features": np.random.randn(num_samples),
     }
+    if string_partition_by:
+        data[partition_by] = data[partition_by].astype(str)
     return Dataset.from_dict(data)
 
 
@@ -51,6 +56,7 @@ def _dummy_heterogeneous_dataset_setup(
     return Dataset.from_dict(data)
 
 
+@parameterized_class(("string_partition_by",), [(False,), (True,)])
 class TestClassConstrainedPartitioner(unittest.TestCase):
     """Unit tests for PathologicalPartitioner."""
 
@@ -94,7 +100,8 @@ def test_first_class_deterministic_assignment(self) -> None:
         Test if all the classes are used (which has to be the case, given num_partitions
         >= than the number of unique classes).
         """
-        dataset = _dummy_dataset_setup(100, "labels", 10)
+        partition_by = "labels"
+        dataset = _dummy_dataset_setup(100, partition_by, 10)
         partitioner = PathologicalPartitioner(
             num_partitions=10,
             partition_by="labels",
@@ -103,7 +110,12 @@ def test_first_class_deterministic_assignment(self) -> None:
         )
         partitioner.dataset = dataset
         partitioner.load_partition(0)
-        expected_classes = set(range(10))
+        expected_classes = set(
+            range(10)
+            # pylint: disable=unsubscriptable-object
+            if isinstance(dataset[partition_by][0], int)
+            else [str(i) for i in range(10)]
+        )
         actual_classes = set()
         for pid in range(10):
             partition = partitioner.load_partition(pid)
@@ -141,6 +153,9 @@ def test_deterministic_class_assignment(
                     for i in range(num_classes_per_partition)
                 ]
             )
+            # pylint: disable=unsubscriptable-object
+            if isinstance(dataset["labels"][0], str):
+                expected_labels = [str(label) for label in expected_labels]
             actual_labels = sorted(np.unique(partition["labels"]))
             self.assertTrue(
                 np.array_equal(expected_labels, actual_labels),
@@ -166,6 +181,9 @@ def test_too_many_partitions_for_a_class(
             "labels": np.array([num_unique_classes - 1] * (num_samples // 2)),
             "features": np.random.randn(num_samples // 2),
         }
+        # pylint: disable=unsubscriptable-object
+        if isinstance(dataset_1["labels"][0], str):
+            data["labels"] = data["labels"].astype(str)
         dataset_2 = Dataset.from_dict(data)
         dataset = datasets.concatenate_datasets([dataset_1, dataset_2])