diff --git a/opacus/grad_sample/conv.py b/opacus/grad_sample/conv.py
index e272e4c5..b29c47ad 100644
--- a/opacus/grad_sample/conv.py
+++ b/opacus/grad_sample/conv.py
@@ -49,8 +49,15 @@ def compute_conv_grad_sample(
             ret[layer.bias] = torch.zeros_like(layer.bias).unsqueeze(0)
         return ret
 
+    # FSDPWrapper adds a prefix 'FSDP' to layer type, e.g. FSDPConv2d.
+    # Therefore the layer type can not be directly determined by type(layer).
+    layer_type = (
+        layer.__class__.__bases__[1]
+        if isinstance(layer, torch.distributed.fsdp.FSDPModule)
+        else type(layer)
+    )
     # get activations and backprops in shape depending on the Conv layer
-    if type(layer) is nn.Conv2d:
+    if layer_type is nn.Conv2d:
         activations = unfold2d(
             activations,
             kernel_size=layer.kernel_size,
@@ -58,7 +65,7 @@ def compute_conv_grad_sample(
             stride=layer.stride,
             dilation=layer.dilation,
         )
-    elif type(layer) is nn.Conv1d:
+    elif layer_type is nn.Conv1d:
         activations = activations.unsqueeze(-2)  # add the H dimension
         # set arguments to tuples with appropriate second element
         if layer.padding == "same":
@@ -76,7 +83,7 @@ def compute_conv_grad_sample(
             stride=(1, layer.stride[0]),
             dilation=(1, layer.dilation[0]),
         )
-    elif type(layer) is nn.Conv3d:
+    elif layer_type is nn.Conv3d:
         activations = unfold3d(
             activations,
             kernel_size=layer.kernel_size,