diff --git a/python/dgl/graphbolt/feature_fetcher.py b/python/dgl/graphbolt/feature_fetcher.py
index 75dab4d54cd2..cf9d5f4104c2 100644
--- a/python/dgl/graphbolt/feature_fetcher.py
+++ b/python/dgl/graphbolt/feature_fetcher.py
@@ -8,6 +8,7 @@
 from torch.utils.data import functional_datapipe
 
 from .base import etype_tuple_to_str
+from .impl.cooperative_conv import CooperativeConvFunction
 
 from .minibatch_transformer import MiniBatchTransformer
 
@@ -73,6 +74,16 @@ class FeatureFetcher(MiniBatchTransformer):
         If True, the feature fetcher will overlap the UVA feature fetcher
         operations with the rest of operations by using an alternative CUDA
         stream or utilizing asynchronous operations. Default is True.
+    cooperative: bool, optional
+        Boolean indicating whether Cooperative Minibatching, which was initially
+        proposed in
+        `Deep Graph Library PR#4337<https://github.com/dmlc/dgl/pull/4337>`__
+        and was later first fully described in
+        `Cooperative Minibatching in Graph Neural Networks
+        <https://arxiv.org/abs/2310.12403>`__. Cooperation between the GPUs
+        eliminates duplicate work performed across the GPUs due to the
+        overlapping sampled k-hop neighborhoods of seed nodes when performing
+        GNN minibatching.
     """
 
     def __init__(
@@ -82,6 +93,7 @@ def __init__(
         node_feature_keys=None,
         edge_feature_keys=None,
         overlap_fetch=True,
+        cooperative=False,
     ):
         datapipe = datapipe.mark_feature_fetcher_start()
         self.feature_store = feature_store
@@ -113,9 +125,12 @@ def __init__(
             datapipe = datapipe.transform(
                 partial(self._execute_stage, i)
             ).buffer(1)
-        super().__init__(
-            datapipe, self._identity if max_val == 0 else self._final_stage
-        )
+        if max_val > 0:
+            datapipe = datapipe.transform(self._final_stage)
+        if cooperative:
+            datapipe = datapipe.transform(self._cooperative_exchange)
+            datapipe = datapipe.buffer()
+        super().__init__(datapipe)
         # A positive value indicates that the overlap optimization is enabled.
         self.max_num_stages = max_val
 
@@ -145,6 +160,26 @@ def _final_stage(data):
                 features[key] = value.wait()
         return data
 
+    def _cooperative_exchange(self, data):
+        subgraph = data.sampled_subgraphs[0]
+        is_heterogeneous = isinstance(
+            self.node_feature_keys, Dict
+        ) or isinstance(self.edge_feature_keys, Dict)
+        if is_heterogeneous:
+            node_features = {key: {} for key, _ in data.node_features.keys()}
+            for (key, ntype), feature in data.node_features.items():
+                node_features[key][ntype] = feature
+            for key, feature in node_features.items():
+                new_feature = CooperativeConvFunction.apply(subgraph, feature)
+                for ntype, tensor in new_feature.items():
+                    data.node_features[(key, ntype)] = tensor
+        else:
+            for key in data.node_features:
+                feature = data.node_features[key]
+                new_feature = CooperativeConvFunction.apply(subgraph, feature)
+                data.node_features[key] = new_feature
+        return data
+
     def _read(self, data):
         """
         Fill in the node/edge features field in data.
diff --git a/python/dgl/graphbolt/impl/cooperative_conv.py b/python/dgl/graphbolt/impl/cooperative_conv.py
index 28f11bc8b317..cb3d39d4d980 100644
--- a/python/dgl/graphbolt/impl/cooperative_conv.py
+++ b/python/dgl/graphbolt/impl/cooperative_conv.py
@@ -57,7 +57,7 @@ def forward(
     def backward(
         ctx, grad_output: Union[torch.Tensor, Dict[str, torch.Tensor]]
     ):
-        """Implements the forward pass."""
+        """Implements the backward pass."""
         (
             counts_sent,
             counts_received,
diff --git a/tests/python/pytorch/graphbolt/test_dataloader.py b/tests/python/pytorch/graphbolt/test_dataloader.py
index 5d5d44fd1eb7..ee8f2b0cb9f5 100644
--- a/tests/python/pytorch/graphbolt/test_dataloader.py
+++ b/tests/python/pytorch/graphbolt/test_dataloader.py
@@ -146,6 +146,7 @@ def test_gpu_sampling_DataLoader(
                 ["a", "b", "c"],
                 ["d"],
                 overlap_fetch=overlap_feature_fetch and i == 0,
+                cooperative=asynchronous and cooperative and i == 0,
             )
         dataloaders.append(dgl.graphbolt.DataLoader(datapipe))
     dataloader, dataloader2 = dataloaders
@@ -159,6 +160,8 @@ def test_gpu_sampling_DataLoader(
         bufferer_cnt += 2 * num_layers + 1  # _preprocess stage has 1.
         if cooperative:
             bufferer_cnt += 3 * num_layers
+            if enable_feature_fetch:
+                bufferer_cnt += 1  # feature fetch has 1.
     if cooperative:
         # _preprocess stage and each sampling layer.
         bufferer_cnt += 3