diff --git a/keras_core/layers/layer.py b/keras_core/layers/layer.py
index 776bbe7ce..ff2a3c897 100644
--- a/keras_core/layers/layer.py
+++ b/keras_core/layers/layer.py
@@ -33,6 +33,7 @@ from keras_core.backend.common import global_state
 from keras_core.layers import input_spec
 from keras_core.metrics.metric import Metric
 from keras_core.operations.operation import Operation
+from keras_core.utils import python_utils
 from keras_core.utils import summary_utils
 from keras_core.utils import traceback_utils
 from keras_core.utils import tracking
@@ -1121,6 +1122,15 @@ class Layer(BackendLayer, Operation):
                     # It's a C type.
                     pass
 
+    @python_utils.default
+    def get_config(self):
+        base_config = super().get_config()
+        config = {
+            "trainable": self.trainable,
+            "dtype": self.dtype_policy.name,
+        }
+        return {**base_config, **config}
+
 
 def is_backend_tensor_or_symbolic(x):
     return backend.is_tensor(x) or isinstance(x, backend.KerasTensor)
diff --git a/keras_core/layers/regularization/dropout.py b/keras_core/layers/regularization/dropout.py
index 6862b83b5..88e8fc8d0 100644
--- a/keras_core/layers/regularization/dropout.py
+++ b/keras_core/layers/regularization/dropout.py
@@ -38,10 +38,8 @@ class Dropout(Layer):
             training mode (adding dropout) or in inference mode (doing nothing).
     """
 
-    def __init__(
-        self, rate, noise_shape=None, seed=None, name=None, dtype=None
-    ):
-        super().__init__(name=name, dtype=dtype)
+    def __init__(self, rate, noise_shape=None, seed=None, **kwargs):
+        super().__init__(**kwargs)
         if not 0 <= rate <= 1:
             raise ValueError(
                 f"Invalid value received for argument "
diff --git a/keras_core/layers/regularization/gaussian_dropout.py b/keras_core/layers/regularization/gaussian_dropout.py
index bee22320e..66d682517 100644
--- a/keras_core/layers/regularization/gaussian_dropout.py
+++ b/keras_core/layers/regularization/gaussian_dropout.py
@@ -24,8 +24,8 @@ class GaussianDropout(layers.Layer):
             training mode (adding dropout) or in inference mode (doing nothing).
     """
 
-    def __init__(self, rate, seed=None, name=None, dtype=None):
-        super().__init__(name=name, dtype=dtype)
+    def __init__(self, rate, seed=None, **kwargs):
+        super().__init__(**kwargs)
         if not 0 <= rate <= 1:
             raise ValueError(
                 f"Invalid value received for argument "
diff --git a/keras_core/layers/regularization/gaussian_noise.py b/keras_core/layers/regularization/gaussian_noise.py
index 66466cd0e..35fca69c5 100644
--- a/keras_core/layers/regularization/gaussian_noise.py
+++ b/keras_core/layers/regularization/gaussian_noise.py
@@ -25,8 +25,8 @@ class GaussianNoise(layers.Layer):
             training mode (adding noise) or in inference mode (doing nothing).
     """
 
-    def __init__(self, stddev, seed=None, name=None, dtype=None):
-        super().__init__(name=name, dtype=dtype)
+    def __init__(self, stddev, seed=None, **kwargs):
+        super().__init__(**kwargs)
         if not 0 <= stddev <= 1:
             raise ValueError(
                 f"Invalid value received for argument "
diff --git a/keras_core/saving/serialization_lib_test.py b/keras_core/saving/serialization_lib_test.py
index 02fe105b9..fc45caf93 100644
--- a/keras_core/saving/serialization_lib_test.py
+++ b/keras_core/saving/serialization_lib_test.py
@@ -87,8 +87,17 @@ class SerializationLibTest(testing.TestCase):
             self.assertEqual(serialized, reserialized)
 
     def test_builtin_layers(self):
-        serialized, _, reserialized = self.roundtrip(keras_core.layers.Dense(3))
+        layer = keras_core.layers.Dense(
+            3,
+            name="foo",
+            trainable=False,
+            dtype="float16",
+        )
+        serialized, restored, reserialized = self.roundtrip(layer)
         self.assertEqual(serialized, reserialized)
+        self.assertEqual(layer.name, restored.name)
+        self.assertEqual(layer.trainable, restored.trainable)
+        self.assertEqual(layer.compute_dtype, restored.compute_dtype)
 
     def test_tensors_and_shapes(self):
         x = ops.random.normal((2, 2), dtype="float64")