diff --git a/datasets/doc/source/how-to-use-with-local-data.rst b/datasets/doc/source/how-to-use-with-local-data.rst
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+Use with local data
+===================
+
+You can partition data from local files and in-memory data by using
+``Flower Datasets`` 's ``Partitioner`` s.
+This guide details how to load and partition your dataset created from:
+
+* local files: CSV, JSON, image, audio data,
+* in-memory data : dictionary, list, pd.DataFrame, np.ndarray.
+
+
+General Overview
+----------------
+FederatedDataset is an abstraction that performs all the steps to prepare the dataset for FL experiments: downloading, preprocessing (including resplitting), and partitioning.
+However, the partitioning happens thanks to the ``partitioners`` of type ``str: Partitioner`` (meaning a dictionary mapping the split name of the dataset to the ``Partitioner``).
+
+You can also use ``Partitioner`` s alone without relying on the FederatedDataset (skipping the data download part). A ``Partitioner`` is also not concerned if the data is downloaded from the Hugging Face Hub or created from another source (e.g., loaded locally). The only crucial point is that the dataset you assign to the ``Partitioner`` has to be of the Dataset type (datasets.Dataset).
+
+The rest of this guide will explain how to create a Dataset from local files and existing (in memory) Python objects.
+
+Local Files
+-----------
+CSV
+^^^
+.. code-block:: python
+
+  from datasets import load_dataset
+  from flwr_datasets.partitioner import ChosenPartitioner
+
+  # Single file
+  data_files = "path-to-my-file.csv"
+
+  # Multitple Files
+  data_files = [ "path-to-my-file-1.csv", "path-to-my-file-2.csv", ...]
+  dataset = load_dataset("csv", data_files=data_files)
+
+  # Divided Dataset
+  data_files = {
+    "train": single_train_file_or_list_of_files,
+    "test": single_test_file_or_list_of_files,
+    "can-have-more-splits": ...
+  }
+  dataset = load_dataset("csv", data_files=data_files)
+
+  partitioner = ChosenPartitioner(...)
+  partitioner.dataset = dataset
+  partition = partitioner.load_partition(partition_id=0)
+
+JSON
+^^^^
+
+.. code-block:: python
+
+  from datasets import load_dataset
+  from flwr_datasets.partitioner import ChosenPartitioner
+
+  # Single file
+  data_files = "path-to-my-file.json"
+
+  # Multitple Files
+  data_files = [ "path-to-my-file-1.json", "path-to-my-file-2.json", ...]
+  dataset = load_dataset("json", data_files=data_files)
+
+  # Divided Dataset
+  data_files = {
+    "train": single_train_file_or_list_of_files,
+     "test": single_test_file_or_list_of_files,
+     "can-have-more-splits": ...
+  }
+  dataset = load_dataset("json", data_files=data_files)
+
+  partitioner = ChosenPartitioner(...)
+  partitioner.dataset = dataset
+  partition = partitioner.load_partition(partition_id=0)
+
+
+Image
+^^^^^
+You can create an image dataset in tow ways:
+
+1) give a path the the directory
+
+.. code-block:: python
+
+  from datasets import load_dataset
+  from flwr_datasets.partitioner import ChosenPartitioner
+
+  # Directly from a directory
+  dataset = load_dataset("imagefolder", data_dir="/path/to/folder")
+  partitioner = ChosenPartitioner(...)
+  partitioner.dataset = dataset
+  partition = partitioner.load_partition(partition_id=0)
+
+2) create a dataset from a CSV/JSON file and cast the path column to Image.
+
+.. code-block:: python
+
+  from datasets import Image
+  from flwr_datasets.partitioner import ChosenPartitioner
+
+  dataset = csv_data_with_path_column.cast_column("path", Image())
+
+  partitioner = ChosenPartitioner(...)
+  partitioner.dataset = dataset
+  partition = partitioner.load_partition(partition_id=0)
+
+
+Audio
+^^^^^
+Analogously to the image datasets there are two methods here:
+
+1) give a path the the directory
+
+.. code-block:: python
+
+  from datasets import load_dataset
+  from flwr_datasets.partitioner import ChosenPartitioner
+
+  dataset = load_dataset("audiofolder", data_dir="/path/to/folder")
+
+  partitioner = ChosenPartitioner(...)
+  partitioner.dataset = dataset
+  partition = partitioner.load_partition(partition_id=0)
+
+2) create a dataset from a CSV/JSON file and cast the path column to Image.
+
+.. code-block:: python
+
+  from datasets import Audio
+  from flwr_datasets.partitioner import ChosenPartitioner
+
+  dataset = csv_data_with_path_column.cast_column("path", Audio())
+
+  partitioner = ChosenPartitioner(...)
+  partitioner.dataset = dataset
+  partition = partitioner.load_partition(partition_id=0)
+
+In-Memory
+---------
+
+From dictionary
+^^^^^^^^^^^^^^^
+.. code-block:: python
+
+  from datasets import Dataset
+  data = {"features": [1, 2, 3], "labels": [0, 0, 1]}
+  dataset = Dataset.from_dict(data)
+
+  partitioner = ChosenPartitioner(...)
+  partitioner.dataset = dataset
+  partition = partitioner.load_partition(partition_id=0)
+
+From list
+^^^^^^^^^
+.. code-block:: python
+
+  data = [
+    {"features": 1, "labels": 0},
+    {"features": 2, "labels": 0},
+    {"features": 3, "labels": 1}
+  ]
+  dataset = Dataset.from_dict(my_dict)
+
+  partitioner = ChosenPartitioner(...)
+  partitioner.dataset = dataset
+  partition = partitioner.load_partition(partition_id=0)
+
+From pd.DataFrame
+^^^^^^^^^^^^^^^^^
+.. code-block:: python
+
+  data = {"features": [1, 2, 3], "labels": [0, 0, 1]}
+  df = pd.DataFrame(data)
+  dataset = Dataset.from_pandas(df)
+
+  partitioner = ChosenPartitioner(...)
+  partitioner.dataset = dataset
+  partition = partitioner.load_partition(partition_id=0)
+
+From np.ndarray
+^^^^^^^^^^^^^^^
+The np.ndarray will be first transformed to pd.DataFrame
+
+.. code-block:: python
+
+  data = np.array([[1, 2, 3], [0, 0, 1]).T
+  # You can add the column names by passing columns=["features", "labels"]
+  df = pd.DataFrame(data)
+  dataset = Dataset.from_pandas(df)
+
+  partitioner = ChosenPartitioner(...)
+  partitioner.dataset = dataset
+  partition = partitioner.load_partition(partition_id=0)
+
+Partitioner Details
+-------------------
+Partitioning is triggered automatically during the first ``load_partition`` call.
+You do not need to call any “do_partitioning” method.
+
+Partitioner abstraction is designed to allow for a single dataset assignment.
+
+.. code-block:: python
+
+  partitioner.dataset = your_dataset
+
+If you need to do the same partitioning on a different dataset, create a new Partitioner
+for that, e.g.:
+
+.. code-block:: python
+
+  iid_partitioner_for_mnist = IidPartitioner(num_partitions=10)
+  iid_partitioner_for_mnist.dataset = mnist_dataset
+
+  iid_partitioner_for_cifar = IidPartitioner(num_partitions=10)
+  iid_partitioner_for_cifar.dataset = cifar_dataset
diff --git a/datasets/doc/source/index.rst b/datasets/doc/source/index.rst
index fd226b308bd5..2144c527f8cd 100644
--- a/datasets/doc/source/index.rst
+++ b/datasets/doc/source/index.rst
@@ -31,6 +31,7 @@ Problem-oriented how-to guides show step-by-step how to achieve a specific goal.
    how-to-use-with-pytorch
    how-to-use-with-tensorflow
    how-to-use-with-numpy
+   how-to-use-with-local-data
    how-to-disable-enable-progress-bar
 
 References