diff --git a/ignite/contrib/metrics/roc_auc.py b/ignite/contrib/metrics/roc_auc.py
index 79238de4d4f2..51c2c3779de1 100644
--- a/ignite/contrib/metrics/roc_auc.py
+++ b/ignite/contrib/metrics/roc_auc.py
@@ -1,7 +1,9 @@
-from typing import Any, Callable, Tuple, Union
+from typing import Any, Callable, cast, Tuple, Union
 
 import torch
 
+from ignite import distributed as idist
+from ignite.exceptions import NotComputableError
 from ignite.metrics import EpochMetric
 
 
@@ -103,6 +105,8 @@ class RocCurve(EpochMetric):
             <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#
             sklearn.metrics.roc_curve>`_ is run on the first batch of data to ensure there are
             no issues. User will be warned in case there are any issues computing the function.
+        device: optional device specification for internal storage.
+
     Note:
         RocCurve expects y to be comprised of 0's and 1's. y_pred must either be probability estimates or confidence
         values. To apply an activation to y_pred, use output_transform as shown below:
@@ -137,9 +141,17 @@ def sigmoid_output_transform(output):
             FPR [0.0, 0.333, 0.333, 1.0]
             TPR [0.0, 0.0, 1.0, 1.0]
             Thresholds [2.0, 1.0, 0.711, 0.047]
+
+    ..  versionchanged:: 0.4.11
+        added `device` argument
     """
 
-    def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None:
+    def __init__(
+        self,
+        output_transform: Callable = lambda x: x,
+        check_compute_fn: bool = False,
+        device: Union[str, torch.device] = torch.device("cpu"),
+    ) -> None:
 
         try:
             from sklearn.metrics import roc_curve  # noqa: F401
@@ -147,5 +159,38 @@ def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: b
             raise ModuleNotFoundError("This contrib module requires scikit-learn to be installed.")
 
         super(RocCurve, self).__init__(
-            roc_auc_curve_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn
+            roc_auc_curve_compute_fn,
+            output_transform=output_transform,
+            check_compute_fn=check_compute_fn,
+            device=device,
         )
+
+    def compute(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        if len(self._predictions) < 1 or len(self._targets) < 1:
+            raise NotComputableError("RocCurve must have at least one example before it can be computed.")
+
+        _prediction_tensor = torch.cat(self._predictions, dim=0)
+        _target_tensor = torch.cat(self._targets, dim=0)
+
+        ws = idist.get_world_size()
+        if ws > 1:
+            # All gather across all processes
+            _prediction_tensor = cast(torch.Tensor, idist.all_gather(_prediction_tensor))
+            _target_tensor = cast(torch.Tensor, idist.all_gather(_target_tensor))
+
+        if idist.get_rank() == 0:
+            # Run compute_fn on zero rank only
+            fpr, tpr, thresholds = self.compute_fn(_prediction_tensor, _target_tensor)
+            fpr = torch.tensor(fpr)
+            tpr = torch.tensor(tpr)
+            thresholds = torch.tensor(thresholds)
+        else:
+            fpr, tpr, thresholds = None, None, None
+
+        if ws > 1:
+            # broadcast result to all processes
+            fpr = idist.broadcast(fpr, src=0, safe_mode=True)
+            tpr = idist.broadcast(tpr, src=0, safe_mode=True)
+            thresholds = idist.broadcast(thresholds, src=0, safe_mode=True)
+
+        return fpr, tpr, thresholds
diff --git a/tests/ignite/contrib/metrics/test_roc_curve.py b/tests/ignite/contrib/metrics/test_roc_curve.py
index c51293c40bb2..77ccb3a9d8b8 100644
--- a/tests/ignite/contrib/metrics/test_roc_curve.py
+++ b/tests/ignite/contrib/metrics/test_roc_curve.py
@@ -6,11 +6,22 @@
 import torch
 from sklearn.metrics import roc_curve
 
+from ignite import distributed as idist
 from ignite.contrib.metrics.roc_auc import RocCurve
 from ignite.engine import Engine
+from ignite.exceptions import NotComputableError
 from ignite.metrics.epoch_metric import EpochMetricWarning
 
 
+def test_wrong_setup():
+    def compute_fn(y_preds, y_targets):
+        return 0.0
+
+    with pytest.raises(NotComputableError, match="RocCurve must have at least one example before it can be computed"):
+        metric = RocCurve(compute_fn)
+        metric.compute()
+
+
 @pytest.fixture()
 def mock_no_sklearn():
     with patch.dict("sys.modules", {"sklearn.metrics": None}):
@@ -121,3 +132,37 @@ def test_check_compute_fn():
 
     em = RocCurve(check_compute_fn=False)
     em.update(output)
+
+
+def test_distrib_integration(distributed):
+    rank = idist.get_rank()
+    torch.manual_seed(41 + rank)
+    n_batches, batch_size = 5, 10
+    y = torch.randint(0, 2, size=(n_batches * batch_size,))
+    y_pred = torch.rand((n_batches * batch_size,))
+
+    def update(engine, i):
+        return (
+            y_pred[i * batch_size : (i + 1) * batch_size],
+            y[i * batch_size : (i + 1) * batch_size],
+        )
+
+    engine = Engine(update)
+
+    device = "cpu" if idist.device().type == "xla" else idist.device()
+    metric = RocCurve(device=device)
+    metric.attach(engine, "roc_curve")
+
+    data = list(range(n_batches))
+
+    engine.run(data=data, max_epochs=1)
+
+    fpr, tpr, thresholds = engine.state.metrics["roc_curve"]
+
+    y = idist.all_gather(y)
+    y_pred = idist.all_gather(y_pred)
+    sk_fpr, sk_tpr, sk_thresholds = roc_curve(y, y_pred)
+
+    assert np.array_equal(fpr, sk_fpr)
+    assert np.array_equal(tpr, sk_tpr)
+    np.testing.assert_array_almost_equal(thresholds, sk_thresholds)