diff --git a/python/dgl/distributed/dist_dataloader.py b/python/dgl/distributed/dist_dataloader.py
index 117379b2b4a3..833fb937a43c 100644
--- a/python/dgl/distributed/dist_dataloader.py
+++ b/python/dgl/distributed/dist_dataloader.py
@@ -311,7 +311,7 @@ def collate(self, items):
         raise NotImplementedError
 
     @staticmethod
-    def add_edge_attribute_to_graph(g, data_name):
+    def add_edge_attribute_to_graph(g, data_name, gb_padding):
         """Add data into the graph as an edge attribute.
 
         For some cases such as prob/mask-based sampling on GraphBolt partitions,
@@ -327,9 +327,11 @@ def add_edge_attribute_to_graph(g, data_name):
             The graph.
         data_name : str
             The name of data that's stored in DistGraph.ndata/edata.
+        gb_padding : int, optional
+            The padding value for GraphBolt partitions' new edge_attributes.
         """
         if g._use_graphbolt and data_name:
-            g.add_edge_attribute(data_name)
+            g.add_edge_attribute(data_name, gb_padding)
 
 
 class NodeCollator(Collator):
@@ -344,6 +346,11 @@ class NodeCollator(Collator):
         The node set to compute outputs.
     graph_sampler : dgl.dataloading.BlockSampler
         The neighborhood sampler.
+    gb_padding : int, optional
+        The padding value for GraphBolt partitions' new edge_attributes if the attributes in DistGraph are None.
+        e.g. prob/mask-based sampling.
+        Only when the mask of one edge is set as 1, an edge will be sampled in dgl.graphbolt.FusedCSCSamplingGraph.sample_neighbors.
+        The argument will be used in add_edge_attribute_to_graph to add new edge_attributes in graphbolt.
 
     Examples
     --------
@@ -366,7 +373,7 @@ class NodeCollator(Collator):
     :doc:`Minibatch Training Tutorials <tutorials/large/L0_neighbor_sampling_overview>`.
     """
 
-    def __init__(self, g, nids, graph_sampler):
+    def __init__(self, g, nids, graph_sampler, gb_padding=1):
         self.g = g
         if not isinstance(nids, Mapping):
             assert (
@@ -380,7 +387,7 @@ def __init__(self, g, nids, graph_sampler):
         # Add prob/mask into graphbolt partition's edge attributes if needed.
         if hasattr(self.graph_sampler, "prob"):
             Collator.add_edge_attribute_to_graph(
-                self.g, self.graph_sampler.prob
+                self.g, self.graph_sampler.prob, gb_padding
             )
 
     @property
@@ -508,8 +515,11 @@ class EdgeCollator(Collator):
 
         A set of builtin negative samplers are provided in
         :ref:`the negative sampling module <api-dataloading-negative-sampling>`.
-
-    Examples
+    gb_padding : int, optional
+        The padding value for GraphBolt partitions' new edge_attributes if the attributes in DistGraph are None.
+        e.g. prob/mask-based sampling.
+        Only when the mask of one edge is set as 1, an edge will be sampled in dgl.graphbolt.FusedCSCSamplingGraph.sample_neighbors.
+        The argument will be used in add_edge_attribute_to_graph to add new edge_attributes in graphbolt.
     --------
     The following example shows how to train a 3-layer GNN for edge classification on a
     set of edges ``train_eid`` on a homogeneous undirected graph. Each node takes
@@ -612,6 +622,7 @@ def __init__(
         reverse_eids=None,
         reverse_etypes=None,
         negative_sampler=None,
+        gb_padding=1,
     ):
         self.g = g
         if not isinstance(eids, Mapping):
@@ -642,7 +653,7 @@ def __init__(
         # Add prob/mask into graphbolt partition's edge attributes if needed.
         if hasattr(self.graph_sampler, "prob"):
             Collator.add_edge_attribute_to_graph(
-                self.g, self.graph_sampler.prob
+                self.g, self.graph_sampler.prob, gb_padding
             )
 
     @property
diff --git a/python/dgl/distributed/dist_graph.py b/python/dgl/distributed/dist_graph.py
index d4e31ce02770..149d5d3fd878 100644
--- a/python/dgl/distributed/dist_graph.py
+++ b/python/dgl/distributed/dist_graph.py
@@ -143,15 +143,16 @@ def _copy_data_from_shared_mem(name, shape):
 class AddEdgeAttributeFromKVRequest(rpc.Request):
     """Add edge attribute from kvstore to local GraphBolt partition."""
 
-    def __init__(self, name, kv_names):
+    def __init__(self, name, kv_names, padding):
         self._name = name
         self._kv_names = kv_names
+        self._padding = padding
 
     def __getstate__(self):
-        return self._name, self._kv_names
+        return self._name, self._kv_names, self._padding
 
     def __setstate__(self, state):
-        self._name, self._kv_names = state
+        self._name, self._kv_names, self._padding = state
 
     def process_request(self, server_state):
         # For now, this is only used to add prob/mask data to the graph.
@@ -169,7 +170,13 @@ def process_request(self, server_state):
             gpb = server_state.partition_book
             # Initialize the edge attribute.
             num_edges = g.total_num_edges
-            attr_data = torch.zeros(num_edges, dtype=data_type)
+
+            # Padding is used to fill missing edge attributes (e.g., 'prob' or 'mask') for certain edge types.
+            # In DGLGraph, some edges may lack these attributes or have them set to None, but DGL will still sample these edges.
+            # In contrast, GraphBolt samples edges based on specific attributes (e.g., 'mask' == 1) and will skip edges with missing attributes.
+            # To ensure consistent sampling behavior in GraphBolt, we pad missing attributes with default values (e.g., 'mask' = 1),
+            # allowing all edges to be sampled, even if their attributes were missing or None in DGLGraph.
+            attr_data = torch.full((num_edges,), self._padding, dtype=data_type)
             # Map data from kvstore to the local partition for inner edges only.
             num_inner_edges = gpb.metadata()[gpb.partid]["num_edges"]
             homo_eids = g.edge_attributes[EID][:num_inner_edges]
@@ -1620,13 +1627,15 @@ def _get_edata_names(self, etype=None):
                 edata_names.append(name)
         return edata_names
 
-    def add_edge_attribute(self, name):
+    def add_edge_attribute(self, name, padding):
         """Add an edge attribute into GraphBolt partition from edge data.
 
         Parameters
         ----------
         name : str
             The name of the edge attribute.
+        padding : int, optional
+            The padding value for the new edge attribute.
         """
         # Sanity checks.
         if not self._use_graphbolt:
@@ -1643,7 +1652,7 @@ def add_edge_attribute(self, name):
         ]
         rpc.send_request(
             self._client._main_server_id,
-            AddEdgeAttributeFromKVRequest(name, kv_names),
+            AddEdgeAttributeFromKVRequest(name, kv_names, padding),
         )
         # Wait for the response.
         assert rpc.recv_response()._name == name
diff --git a/tests/distributed/test_distributed_sampling.py b/tests/distributed/test_distributed_sampling.py
index 4ca8f7b130ac..556e091fbbe3 100644
--- a/tests/distributed/test_distributed_sampling.py
+++ b/tests/distributed/test_distributed_sampling.py
@@ -7,8 +7,9 @@
 import unittest
 from pathlib import Path
 
-import backend as F
 import dgl
+
+import dgl.backend as F
 import numpy as np
 import pytest
 import torch
@@ -1858,6 +1859,81 @@ def test_local_sampling_heterograph(num_parts, use_graphbolt, prob_or_mask):
                 )
 
 
+def check_hetero_dist_edge_dataloader_gb(
+    tmpdir, num_server, use_graphbolt=True
+):
+    generate_ip_config("rpc_ip_config.txt", num_server, num_server)
+
+    g = create_random_hetero()
+    eids = torch.randperm(g.num_edges("r23"))[:10]
+    mask = torch.zeros(g.num_edges("r23"), dtype=torch.bool)
+    mask[eids] = True
+
+    num_parts = num_server
+
+    orig_nid_map, orig_eid_map = partition_graph(
+        g,
+        "test_sampling",
+        num_parts,
+        tmpdir,
+        num_hops=1,
+        part_method="metis",
+        return_mapping=True,
+        use_graphbolt=use_graphbolt,
+        store_eids=True,
+    )
+
+    part_config = tmpdir / "test_sampling.json"
+
+    pserver_list = []
+    ctx = mp.get_context("spawn")
+    for i in range(num_server):
+        p = ctx.Process(
+            target=start_server,
+            args=(
+                i,
+                tmpdir,
+                num_server > 1,
+                "test_sampling",
+                ["csc", "coo"],
+                True,
+            ),
+        )
+        p.start()
+        time.sleep(1)
+        pserver_list.append(p)
+
+    dgl.distributed.initialize("rpc_ip_config.txt", use_graphbolt=True)
+    dist_graph = DistGraph("test_sampling", part_config=part_config)
+
+    os.environ["DGL_DIST_DEBUG"] = "1"
+
+    edges = {("n2", "r23", "n3"): eids}
+    sampler = dgl.dataloading.MultiLayerNeighborSampler([10, 10], mask="mask")
+    loader = dgl.dataloading.DistEdgeDataLoader(
+        dist_graph, edges, sampler, batch_size=64
+    )
+    dgl.distributed.exit_client()
+    for p in pserver_list:
+        p.join()
+        assert p.exitcode == 0
+
+    block = next(iter(loader))[2][0]
+    assert block.num_src_nodes("n1") > 0
+    assert block.num_edges("r12") > 0
+    assert block.num_edges("r13") > 0
+    assert block.num_edges("r23") > 0
+
+
+def test_hetero_dist_edge_dataloader_gb(
+    num_server=1,
+):
+    reset_envs()
+    os.environ["DGL_DIST_MODE"] = "distributed"
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        check_hetero_dist_edge_dataloader_gb(Path(tmpdirname), num_server)
+
+
 if __name__ == "__main__":
     import tempfile