diff --git a/doc/source/tutorial-quickstart-scikitlearn.rst b/doc/source/tutorial-quickstart-scikitlearn.rst
index d1d47dc37f19..93322842cc70 100644
--- a/doc/source/tutorial-quickstart-scikitlearn.rst
+++ b/doc/source/tutorial-quickstart-scikitlearn.rst
@@ -45,41 +45,51 @@ However, before setting up the client and server, we will define all functionali
 * :code:`get_model_parameters()`
     * Returns the parameters of a :code:`sklearn` LogisticRegression model
 * :code:`set_model_params()`
-    * Sets the parameters of a :code:`sklean` LogisticRegression model
+    * Sets the parameters of a :code:`sklearn` LogisticRegression model
 * :code:`set_initial_params()`
     * Initializes the model parameters that the Flower server will ask for
-* :code:`load_mnist()`
-    * Loads the MNIST dataset using OpenML
-* :code:`shuffle()`
-    * Shuffles data and its label
-* :code:`partition()`
-    * Splits datasets into a number of partitions
 
 Please check out :code:`utils.py` `here <https://github.com/adap/flower/blob/main/examples/sklearn-logreg-mnist/utils.py>`_ for more details.
 The pre-defined functions are used in the :code:`client.py` and imported. The :code:`client.py` also requires to import several packages such as Flower and scikit-learn:
 
 .. code-block:: python
 
+  import argparse
   import warnings
-  import flwr as fl
-  import numpy as np
-
+  
   from sklearn.linear_model import LogisticRegression
   from sklearn.metrics import log_loss
-
+  
+  import flwr as fl
   import utils
+  from flwr_datasets import FederatedDataset
 
-
-We load the MNIST dataset from `OpenML <https://www.openml.org/search?type=data&sort=runs&id=554>`_, a popular image classification dataset of handwritten digits for machine learning. The utility :code:`utils.load_mnist()` downloads the training and test data. The training set is split afterwards into 10 partitions with :code:`utils.partition()`.
+Prior to local training, we need to load the MNIST dataset, a popular image classification dataset of handwritten digits for machine learning, and partition the dataset for FL. This can be conveniently achieved using `Flower Datasets <https://flower.ai/docs/datasets>`_.
+The :code:`FederatedDataset.load_partition()` method loads the partitioned training set for each partition ID defined in the :code:`--partition-id` argument.
 
 .. code-block:: python
 
     if __name__ == "__main__":
-
-        (X_train, y_train), (X_test, y_test) = utils.load_mnist()
-
-        partition_id = np.random.choice(10)
-        (X_train, y_train) = utils.partition(X_train, y_train, 10)[partition_id]
+        N_CLIENTS = 10
+    
+        parser = argparse.ArgumentParser(description="Flower")
+        parser.add_argument(
+            "--partition-id",
+            type=int,
+            choices=range(0, N_CLIENTS),
+            required=True,
+            help="Specifies the artificial data partition",
+        )
+        args = parser.parse_args()
+        partition_id = args.partition_id
+    
+        fds = FederatedDataset(dataset="mnist", partitioners={"train": N_CLIENTS})
+    
+        dataset = fds.load_partition(partition_id, "train").with_format("numpy")
+        X, y = dataset["image"].reshape((len(dataset), -1)), dataset["label"]
+        
+        X_train, X_test = X[: int(0.8 * len(X))], X[int(0.8 * len(X)) :]
+        y_train, y_test = y[: int(0.8 * len(y))], y[int(0.8 * len(y)) :]
 
 
 Next, the logistic regression model is defined and initialized with :code:`utils.set_initial_params()`.
@@ -168,10 +178,13 @@ First, we import again all required libraries such as Flower and scikit-learn.
     from flwr.common import NDArrays, Scalar
     from sklearn.metrics import log_loss
     from sklearn.linear_model import LogisticRegression
-    from typing import Dict, Optional
+    from typing import Dict
+    
+    from flwr_datasets import FederatedDataset
 
 The number of federated learning rounds is set in :code:`fit_round()` and the evaluation is defined in :code:`get_evaluate_fn()`.
 The evaluation function is called after each federated learning round and gives you information about loss and accuracy.
+Note that we also make use of Flower Datasets here to load the test split of the MNIST dataset for server-side evaluation.
 
 .. code-block:: python
 
@@ -183,7 +196,9 @@ The evaluation function is called after each federated learning round and gives
     def get_evaluate_fn(model: LogisticRegression):
         """Return an evaluation function for server-side evaluation."""
 
-        _, (X_test, y_test) = utils.load_mnist()
+        fds = FederatedDataset(dataset="mnist", partitioners={"train": 10})
+        dataset = fds.load_split("test").with_format("numpy")
+        X_test, y_test = dataset["image"].reshape((len(dataset), -1)), dataset["label"]
 
         def evaluate(
             server_round: int, parameters: NDArrays, config: Dict[str, Scalar]
@@ -199,7 +214,7 @@ The :code:`main` contains the server-side parameter initialization :code:`utils.
 
 .. code-block:: python
 
-    # Start Flower server for five rounds of federated learning
+    # Start Flower server for three rounds of federated learning
     if __name__ == "__main__":
         model = LogisticRegression()
         utils.set_initial_params(model)