diff --git a/composer/trainer/mosaic_fsdp.py b/composer/trainer/mosaic_fsdp.py
index 97af3ddaa4..44f42650eb 100644
--- a/composer/trainer/mosaic_fsdp.py
+++ b/composer/trainer/mosaic_fsdp.py
@@ -67,8 +67,11 @@ def patch_pytorch():
         from composer.trainer.mosaic_fsdp_utils import _same_storage
         _flat_param._same_storage = _same_storage
 
+        # Monkeypatch state_dict to get FQNs correctly.
+        # Issue: https://github.com/pytorch/pytorch/pull/124698
         from torch.distributed.checkpoint import state_dict
 
-        from composer.trainer.mosaic_fsdp_utils import set_model_state_dict, set_optimizer_state_dict
+        from composer.trainer.mosaic_fsdp_utils import _get_fqns, set_model_state_dict, set_optimizer_state_dict
         state_dict.set_model_state_dict = set_model_state_dict
         state_dict.set_optimizer_state_dict = set_optimizer_state_dict
+        state_dict._get_fqns = _get_fqns
diff --git a/composer/trainer/mosaic_fsdp_utils.py b/composer/trainer/mosaic_fsdp_utils.py
index 8b90634516..394bbce1bb 100644
--- a/composer/trainer/mosaic_fsdp_utils.py
+++ b/composer/trainer/mosaic_fsdp_utils.py
@@ -566,7 +566,73 @@ def _same_storage(a, b):
             b = b._local_tensor
         return a.untyped_storage().data_ptr() == b.untyped_storage().data_ptr()
 
-    from torch.distributed.checkpoint.state_dict import _unflatten_model_state_dict, _verify_options, _load_model_state_dict, gc_context, _verify_state_dict, _load_optim_state_dict
+    from torch.distributed.checkpoint.state_dict import (_unflatten_model_state_dict, _verify_options,
+                                                         _load_model_state_dict, gc_context,
+                                                         _verify_state_dict, _load_optim_state_dict,
+                                                         FQNS_T)
+
+    @no_type_check
+    def _get_fqns(
+        model: nn.Module,
+        name: str,
+        skip_ddp_prefix: bool = True,
+        skip_compiler_prefix: bool = True,
+    ) -> FQNS_T:
+        """Used to convert the name of a parameter to the FQNs.
+
+        For FSDP without `use_orig_params`, the name of FlatParameter can be mapped to
+        multiple original parameters. As a result, the return type of this function
+        is `Set[str]`.
+
+        Args:
+            module (nn.Module): the root model.
+            name (str): the name
+            skip_ddp_prefix (bool): whether to skip DDP's `module` prefix
+
+        Returns:
+            The canonical FQNs based on the model traversal.
+        """
+        from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import _CHECKPOINT_PREFIX
+        from torch.nn.parallel import DistributedDataParallel as DDP
+        from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+        from torch.distributed.checkpoint.state_dict import FLAT_PARAM
+        from torch.distributed.fsdp._common_utils import FSDP_WRAPPED_MODULE
+
+        # Remove the checkpoint prefix, if it exists.
+        name = name.replace(_CHECKPOINT_PREFIX, '')
+        if '.' not in name:
+            return {name}
+
+        obj_names = name.split('.')
+        fqn_obj_names = []
+        curr_obj = model
+        for i, curr_obj_name in enumerate(obj_names):
+            if isinstance(curr_obj, DDP):
+                assert curr_obj_name == 'module'
+                curr_obj = curr_obj.module
+                if not skip_ddp_prefix:
+                    fqn_obj_names.append(curr_obj_name)
+            elif isinstance(curr_obj, FSDP):
+                if i < len(obj_names) - 1 and obj_names[i + 1] == FLAT_PARAM:
+                    prefix = '.'.join(fqn_obj_names)
+                    flat_param = getattr(curr_obj, FLAT_PARAM)
+                    if prefix:
+                        prefix = f'{prefix}.'
+                    return {f'{prefix}{fqn}' for fqn in flat_param._fqns}
+                curr_obj = getattr(curr_obj, FSDP_WRAPPED_MODULE)
+                if curr_obj_name != FSDP_WRAPPED_MODULE:
+                    fqn_obj_names.append(curr_obj_name)
+                    curr_obj = getattr(curr_obj, curr_obj_name)
+            elif isinstance(curr_obj, torch._dynamo.eval_frame.OptimizedModule):
+                assert curr_obj_name == '_orig_mod'
+                curr_obj = curr_obj._orig_mod
+                if not skip_compiler_prefix:
+                    fqn_obj_names.append(curr_obj_name)
+            else:
+                fqn_obj_names.append(curr_obj_name)
+                curr_obj = getattr(curr_obj, curr_obj_name)
+
+        return {'.'.join(fqn_obj_names).replace(_CHECKPOINT_PREFIX, '')}
 
     def set_model_state_dict(
         model: nn.Module,