diff --git a/datasets/doc/source/how-to-use-with-local-data.rst b/datasets/doc/source/how-to-use-with-local-data.rst
new file mode 100644
index 000000000000..276f6d6936ee
--- /dev/null
+++ b/datasets/doc/source/how-to-use-with-local-data.rst
@@ -0,0 +1,257 @@
+Use with Local Data
+===================
+
+You can partition your local files and Python objects in
+``Flower Datasets`` library using any available ``Partitioner``.
+
+This guide details how to create a `Hugging Face <https://huggingface.co/>`_ `Dataset <https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset>`_ which is the required type of input for Partitioners.
+We will cover:
+
+* local files: CSV, JSON, image, audio,
+* in-memory data: dictionary, list, pd.DataFrame, np.ndarray.
+
+
+General Overview
+----------------
+An all-in-one dataset preparation (downloading, preprocessing, partitioning) happens
+using `FederatedDataset <ref-api/flwr_datasets.FederatedDataset.html>`_. However, we
+will use only the `Partitioner` here since we use locally accessible data.
+
+The rest of this guide will explain how to create a
+`Dataset <https://huggingface.co/docs/datasets/en/package_reference/main_classes#datasets.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"
+
+  # Multiple 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 two ways:
+
+1) give a path the directory
+
+The directory needs to be structured in the following way: dataset-name/split/class/name. For example:
+
+.. code-block::
+
+  mnist/train/1/unique_name.png
+  mnist/train/1/unique_name.png
+  mnist/train/2/unique_name.png
+  ...
+  mnist/test/1/unique_name.png
+  mnist/test/1/unique_name.png
+  mnist/test/2/unique_name.png
+
+Then, the path you can give is `./mnist`.
+
+.. 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, load_dataset
+  from flwr_datasets.partitioner import ChosenPartitioner
+
+  dataset = load_dataset(...)
+  dataset = dataset.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 to 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 Audio.
+
+.. code-block:: python
+
+  from datasets import Audio, load_dataset
+  from flwr_datasets.partitioner import ChosenPartitioner
+
+  dataset = load_dataset(...)
+  dataset = dataset.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
+  from flwr_datasets.partitioner import ChosenPartitioner
+  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
+
+  from datasets import Dataset
+  from flwr_datasets.partitioner import ChosenPartitioner
+  
+  my_list = [
+    {"features": 1, "labels": 0},
+    {"features": 2, "labels": 0},
+    {"features": 3, "labels": 1}
+  ]
+  dataset = Dataset.from_list(my_list)
+
+  partitioner = ChosenPartitioner(...)
+  partitioner.dataset = dataset
+  partition = partitioner.load_partition(partition_id=0)
+
+From pd.DataFrame
+^^^^^^^^^^^^^^^^^
+.. code-block:: python
+
+  from datasets import Dataset
+  from flwr_datasets.partitioner import ChosenPartitioner
+  
+  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
+
+  from datasets import Dataset
+  from flwr_datasets.partitioner import ChosenPartitioner
+  
+  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
+
+  from flwr_datasets.partitioner import IidPartitioner
+
+  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
+
+
+More Resources
+--------------
+If you are looking for more details or you have not found the format you are looking for, please visit the `HuggingFace Datasets docs <https://huggingface.co/docs/datasets/index>`_.
+This guide is based on the following ones:
+
+* `General Information <https://huggingface.co/docs/datasets/en/loading>`_
+* `Tabular Data <https://huggingface.co/docs/datasets/en/tabular_load>`_
+* `Image Data <https://huggingface.co/docs/datasets/en/image_load>`_
+* `Audio Data <https://huggingface.co/docs/datasets/en/audio_load>`_
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
diff --git a/datasets/flwr_datasets/federated_dataset.py b/datasets/flwr_datasets/federated_dataset.py
index 55a7e597f6b4..6c41eaa3562f 100644
--- a/datasets/flwr_datasets/federated_dataset.py
+++ b/datasets/flwr_datasets/federated_dataset.py
@@ -59,7 +59,8 @@ class FederatedDataset:
         argument. Defaults to True.
     seed : Optional[int]
         Seed used for dataset shuffling. It has no effect if `shuffle` is False. The
-        seed cannot be set in the later stages.
+        seed cannot be set in the later stages. If `None`, then fresh, unpredictable entropy
+        will be pulled from the OS. Defaults to 42.
 
     Examples
     --------
diff --git a/examples/embedded-devices/Dockerfile b/examples/embedded-devices/Dockerfile
index a85c05c4bb7a..48602c89970a 100644
--- a/examples/embedded-devices/Dockerfile
+++ b/examples/embedded-devices/Dockerfile
@@ -8,7 +8,7 @@ RUN pip3 install --upgrade pip
 
 # Install flower
 RUN pip3 install flwr>=1.0
-RUN pip3 install flwr-datsets>=0.2
+RUN pip3 install flwr-datsets>=0.0.2
 RUN pip3 install tqdm==4.65.0
 
 WORKDIR /client
diff --git a/src/py/flwr/cli/example.py b/src/py/flwr/cli/example.py
index 625ca8729640..4790e72d48bf 100644
--- a/src/py/flwr/cli/example.py
+++ b/src/py/flwr/cli/example.py
@@ -39,7 +39,9 @@ def example() -> None:
     with urllib.request.urlopen(examples_directory_url) as res:
         data = json.load(res)
         example_names = [
-            item["path"] for item in data["tree"] if item["path"] not in [".gitignore"]
+            item["path"]
+            for item in data["tree"]
+            if item["path"] not in [".gitignore", "doc"]
         ]
 
     example_name = prompt_options(
diff --git a/src/py/flwr/cli/new/new.py b/src/py/flwr/cli/new/new.py
index 7eb47e3e3548..0c429ce34cf2 100644
--- a/src/py/flwr/cli/new/new.py
+++ b/src/py/flwr/cli/new/new.py
@@ -22,7 +22,12 @@
 import typer
 from typing_extensions import Annotated
 
-from ..utils import prompt_options, prompt_text
+from ..utils import (
+    is_valid_project_name,
+    prompt_options,
+    prompt_text,
+    sanitize_project_name,
+)
 
 
 class MlFramework(str, Enum):
@@ -81,6 +86,16 @@ def new(
     ] = None,
 ) -> None:
     """Create new Flower project."""
+    if project_name is None:
+        project_name = prompt_text("Please provide project name")
+    if not is_valid_project_name(project_name):
+        project_name = prompt_text(
+            "Please provide a name that only contains "
+            "characters in {'_', 'a-zA-Z', '0-9'}",
+            predicate=is_valid_project_name,
+            default=sanitize_project_name(project_name),
+        )
+
     print(
         typer.style(
             f"🔨 Creating Flower project {project_name}...",
@@ -89,9 +104,6 @@ def new(
         )
     )
 
-    if project_name is None:
-        project_name = prompt_text("Please provide project name")
-
     if framework is not None:
         framework_str = str(framework.value)
     else:
@@ -116,7 +128,6 @@ def new(
     # List of files to render
     files = {
         "README.md": {"template": "app/README.md.tpl"},
-        "requirements.txt": {"template": f"app/requirements.{framework_str}.txt.tpl"},
         "flower.toml": {"template": "app/flower.toml.tpl"},
         "pyproject.toml": {"template": f"app/pyproject.{framework_str}.toml.tpl"},
         f"{pnl}/__init__.py": {"template": "app/code/__init__.py.tpl"},
diff --git a/src/py/flwr/cli/new/new_test.py b/src/py/flwr/cli/new/new_test.py
index cedcb09b7755..11620d234191 100644
--- a/src/py/flwr/cli/new/new_test.py
+++ b/src/py/flwr/cli/new/new_test.py
@@ -66,7 +66,6 @@ def test_new(tmp_path: str) -> None:
     project_name = "FedGPT"
     framework = MlFramework.PYTORCH
     expected_files_top_level = {
-        "requirements.txt",
         "fedgpt",
         "README.md",
         "flower.toml",
diff --git a/src/py/flwr/cli/new/templates/app/pyproject.numpy.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.numpy.toml.tpl
index 15d8211a1a25..9701c62af6f0 100644
--- a/src/py/flwr/cli/new/templates/app/pyproject.numpy.toml.tpl
+++ b/src/py/flwr/cli/new/templates/app/pyproject.numpy.toml.tpl
@@ -1,19 +1,19 @@
 [build-system]
-requires = ["poetry-core>=1.4.0"]
-build-backend = "poetry.core.masonry.api"
+requires = ["hatchling"]
+build-backend = "hatchling.build"
 
-[tool.poetry]
+[project]
 name = "$project_name"
 version = "1.0.0"
 description = ""
-license = "Apache-2.0"
 authors = [
-    "The Flower Authors <hello@flower.ai>",
+    { name = "The Flower Authors", email = "hello@flower.ai" },
+]
+license = {text = "Apache License (2.0)"}
+dependencies = [
+    "flwr[simulation]>=1.8.0,<2.0",
+    "numpy>=1.21.0",
 ]
-readme = "README.md"
 
-[tool.poetry.dependencies]
-python = "^3.9"
-# Mandatory dependencies
-numpy = "^1.21.0"
-flwr = { version = "^1.8.0", extras = ["simulation"] }
+[tool.hatch.build.targets.wheel]
+packages = ["."]
diff --git a/src/py/flwr/cli/new/templates/app/pyproject.pytorch.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.pytorch.toml.tpl
index 46a5508fe2ac..0661c7b730c1 100644
--- a/src/py/flwr/cli/new/templates/app/pyproject.pytorch.toml.tpl
+++ b/src/py/flwr/cli/new/templates/app/pyproject.pytorch.toml.tpl
@@ -1,21 +1,21 @@
 [build-system]
-requires = ["poetry-core>=1.4.0"]
-build-backend = "poetry.core.masonry.api"
+requires = ["hatchling"]
+build-backend = "hatchling.build"
 
-[tool.poetry]
+[project]
 name = "$project_name"
 version = "1.0.0"
 description = ""
-license = "Apache-2.0"
 authors = [
-    "The Flower Authors <hello@flower.ai>",
+    { name = "The Flower Authors", email = "hello@flower.ai" },
+]
+license = {text = "Apache License (2.0)"}
+dependencies = [
+    "flwr[simulation]>=1.8.0,<2.0",
+    "flwr-datasets[vision]>=0.0.2,<1.0.0",
+    "torch==2.2.1",
+    "torchvision==0.17.1",
 ]
-readme = "README.md"
 
-[tool.poetry.dependencies]
-python = "^3.9"
-# Mandatory dependencies
-flwr = { version = "^1.8.0", extras = ["simulation"] }
-flwr-datasets = { version = "0.0.2", extras = ["vision"] }
-torch = "2.2.1"
-torchvision = "0.17.1"
+[tool.hatch.build.targets.wheel]
+packages = ["."]
diff --git a/src/py/flwr/cli/new/templates/app/pyproject.tensorflow.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.tensorflow.toml.tpl
index f7383a78b7d5..5a017eb6ed74 100644
--- a/src/py/flwr/cli/new/templates/app/pyproject.tensorflow.toml.tpl
+++ b/src/py/flwr/cli/new/templates/app/pyproject.tensorflow.toml.tpl
@@ -1,21 +1,20 @@
 [build-system]
-requires = ["poetry-core>=1.4.0"]
-build-backend = "poetry.core.masonry.api"
+requires = ["hatchling"]
+build-backend = "hatchling.build"
 
-[tool.poetry]
+[project]
 name = "$project_name"
 version = "1.0.0"
 description = ""
-license = "Apache-2.0"
 authors = [
-    "The Flower Authors <hello@flower.ai>",
+    { name = "The Flower Authors", email = "hello@flower.ai" },
+]
+license = {text = "Apache License (2.0)"}
+dependencies = [
+    "flwr[simulation]>=1.8.0,<2.0",
+    "flwr-datasets[vision]>=0.0.2,<1.0.0",
+    "tensorflow>=2.11.1",
 ]
-readme = "README.md"
 
-[tool.poetry.dependencies]
-python = ">=3.9,<3.11"
-# Mandatory dependencies
-flwr = { version = "^1.8.0", extras = ["simulation"] }
-flwr-datasets = { version = "^0.0.2", extras = ["vision"] }
-tensorflow-cpu = { version = ">=2.9.1,<2.11.1 || >2.11.1", markers = "platform_machine == \"x86_64\"" }
-tensorflow-macos = { version = ">=2.9.1,<2.11.1 || >2.11.1", markers = "sys_platform == \"darwin\" and platform_machine == \"arm64\"" }
+[tool.hatch.build.targets.wheel]
+packages = ["."]
diff --git a/src/py/flwr/cli/new/templates/app/requirements.numpy.txt.tpl b/src/py/flwr/cli/new/templates/app/requirements.numpy.txt.tpl
deleted file mode 100644
index 4b460798e96f..000000000000
--- a/src/py/flwr/cli/new/templates/app/requirements.numpy.txt.tpl
+++ /dev/null
@@ -1,2 +0,0 @@
-flwr>=1.8, <2.0
-numpy>=1.21.0
diff --git a/src/py/flwr/cli/new/templates/app/requirements.pytorch.txt.tpl b/src/py/flwr/cli/new/templates/app/requirements.pytorch.txt.tpl
deleted file mode 100644
index f20b9d71e339..000000000000
--- a/src/py/flwr/cli/new/templates/app/requirements.pytorch.txt.tpl
+++ /dev/null
@@ -1,4 +0,0 @@
-flwr[simulation]>=1.8.0
-flwr-datasets[vision]==0.0.2
-torch==2.2.1
-torchvision==0.17.1
diff --git a/src/py/flwr/cli/new/templates/app/requirements.tensorflow.txt.tpl b/src/py/flwr/cli/new/templates/app/requirements.tensorflow.txt.tpl
deleted file mode 100644
index b6fb49a4bbcb..000000000000
--- a/src/py/flwr/cli/new/templates/app/requirements.tensorflow.txt.tpl
+++ /dev/null
@@ -1,4 +0,0 @@
-flwr>=1.8, <2.0
-flwr-datasets[vision]>=0.0.2, <1.0.0
-tensorflow-macos>=2.9.1, !=2.11.1 ; sys_platform == "darwin" and platform_machine == "arm64"
-tensorflow-cpu>=2.9.1, !=2.11.1 ; platform_machine == "x86_64"
diff --git a/src/py/flwr/cli/utils.py b/src/py/flwr/cli/utils.py
index 4e86f0c3b8c8..7a36c3eb7b84 100644
--- a/src/py/flwr/cli/utils.py
+++ b/src/py/flwr/cli/utils.py
@@ -14,18 +14,23 @@
 # ==============================================================================
 """Flower command line interface utils."""
 
-from typing import List, cast
+from typing import Callable, List, Optional, cast
 
 import typer
 
 
-def prompt_text(text: str) -> str:
+def prompt_text(
+    text: str,
+    predicate: Callable[[str], bool] = lambda _: True,
+    default: Optional[str] = None,
+) -> str:
     """Ask user to enter text input."""
     while True:
         result = typer.prompt(
-            typer.style(f"\n💬 {text}", fg=typer.colors.MAGENTA, bold=True)
+            typer.style(f"\n💬 {text}", fg=typer.colors.MAGENTA, bold=True),
+            default=default,
         )
-        if len(result) > 0:
+        if predicate(result) and len(result) > 0:
             break
         print(typer.style("❌ Invalid entry", fg=typer.colors.RED, bold=True))
 
@@ -65,3 +70,54 @@ def prompt_options(text: str, options: List[str]) -> str:
 
     result = options[int(index)]
     return result
+
+
+def is_valid_project_name(name: str) -> bool:
+    """Check if the given string is a valid Python module name.
+
+    A valid module name must start with a letter or an underscore, and can only contain
+    letters, digits, and underscores.
+    """
+    if not name:
+        return False
+
+    # Check if the first character is a letter or underscore
+    if not (name[0].isalpha() or name[0] == "_"):
+        return False
+
+    # Check if the rest of the characters are valid (letter, digit, or underscore)
+    for char in name[1:]:
+        if not (char.isalnum() or char == "_"):
+            return False
+
+    return True
+
+
+def sanitize_project_name(name: str) -> str:
+    """Sanitize the given string to make it a valid Python module name.
+
+    This version replaces hyphens with underscores, removes any characters not allowed
+    in Python module names, makes the string lowercase, and ensures it starts with a
+    valid character.
+    """
+    # Replace '-' with '_'
+    name_with_underscores = name.replace("-", "_").replace(" ", "_")
+
+    # Allowed characters in a module name: letters, digits, underscore
+    allowed_chars = set(
+        "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_"
+    )
+
+    # Make the string lowercase
+    sanitized_name = name_with_underscores.lower()
+
+    # Remove any characters not allowed in Python module names
+    sanitized_name = "".join(c for c in sanitized_name if c in allowed_chars)
+
+    # Ensure the first character is a letter or underscore
+    if sanitized_name and (
+        sanitized_name[0].isdigit() or sanitized_name[0] not in allowed_chars
+    ):
+        sanitized_name = "_" + sanitized_name
+
+    return sanitized_name
diff --git a/src/py/flwr/client/app.py b/src/py/flwr/client/app.py
index 1720405ab867..4fa9c80c6cdf 100644
--- a/src/py/flwr/client/app.py
+++ b/src/py/flwr/client/app.py
@@ -456,12 +456,13 @@ def _load_client_app() -> ClientApp:
                     continue
 
                 log(INFO, "")
-                log(
-                    INFO,
-                    "[RUN %s, ROUND %s]",
-                    message.metadata.run_id,
-                    message.metadata.group_id,
-                )
+                if len(message.metadata.group_id) > 0:
+                    log(
+                        INFO,
+                        "[RUN %s, ROUND %s]",
+                        message.metadata.run_id,
+                        message.metadata.group_id,
+                    )
                 log(
                     INFO,
                     "Received: %s message %s",
diff --git a/src/py/flwr/client/heartbeat.py b/src/py/flwr/client/heartbeat.py
index 0cc979ddfd13..b68e6163cc01 100644
--- a/src/py/flwr/client/heartbeat.py
+++ b/src/py/flwr/client/heartbeat.py
@@ -66,7 +66,9 @@ def start_ping_loop(
     asynchronous ping operations. The loop can be terminated through the provided stop
     event.
     """
-    thread = threading.Thread(target=_ping_loop, args=(ping_fn, stop_event))
+    thread = threading.Thread(
+        target=_ping_loop, args=(ping_fn, stop_event), daemon=True
+    )
     thread.start()
 
     return thread
diff --git a/src/py/flwr/common/retry_invoker.py b/src/py/flwr/common/retry_invoker.py
index 7cec319e7906..d12124b89840 100644
--- a/src/py/flwr/common/retry_invoker.py
+++ b/src/py/flwr/common/retry_invoker.py
@@ -261,6 +261,7 @@ def try_call_event_handler(
             try:
                 ret = target(*args, **kwargs)
             except self.recoverable_exceptions as err:
+                state.exception = err
                 # Check if giveup event should be triggered
                 max_tries_exceeded = try_cnt == self.max_tries
                 max_time_exceeded = (
diff --git a/src/py/flwr/server/compat/app_utils.py b/src/py/flwr/server/compat/app_utils.py
index 696ec1132c4a..709be3d96a33 100644
--- a/src/py/flwr/server/compat/app_utils.py
+++ b/src/py/flwr/server/compat/app_utils.py
@@ -16,7 +16,6 @@
 
 
 import threading
-import time
 from typing import Dict, Tuple
 
 from ..client_manager import ClientManager
@@ -60,6 +59,7 @@ def start_update_client_manager_thread(
             client_manager,
             f_stop,
         ),
+        daemon=True,
     )
     thread.start()
 
@@ -99,4 +99,5 @@ def _update_client_manager(
                 raise RuntimeError("Could not register node.")
 
         # Sleep for 3 seconds
-        time.sleep(3)
+        if not f_stop.is_set():
+            f_stop.wait(3)
diff --git a/src/py/flwr/server/compat/app_utils_test.py b/src/py/flwr/server/compat/app_utils_test.py
index 7e47e6eaaf32..023d65b0dc72 100644
--- a/src/py/flwr/server/compat/app_utils_test.py
+++ b/src/py/flwr/server/compat/app_utils_test.py
@@ -17,6 +17,8 @@
 
 import time
 import unittest
+from threading import Event
+from typing import Optional
 from unittest.mock import Mock, patch
 
 from ..client_manager import SimpleClientManager
@@ -29,9 +31,6 @@ class TestUtils(unittest.TestCase):
     def test_start_update_client_manager_thread(self) -> None:
         """Test start_update_client_manager_thread function."""
         # Prepare
-        sleep = time.sleep
-        sleep_patch = patch("time.sleep", lambda x: sleep(x / 100))
-        sleep_patch.start()
         expected_node_ids = list(range(100))
         updated_expected_node_ids = list(range(80, 120))
         driver = Mock()
@@ -39,20 +38,30 @@ def test_start_update_client_manager_thread(self) -> None:
         driver.run_id = 123
         driver.get_node_ids.return_value = expected_node_ids
         client_manager = SimpleClientManager()
+        original_wait = Event.wait
+
+        def custom_wait(self: Event, timeout: Optional[float] = None) -> None:
+            if timeout is not None:
+                timeout /= 100
+            original_wait(self, timeout)
 
         # Execute
-        thread, f_stop = start_update_client_manager_thread(driver, client_manager)
-        # Wait until all nodes are registered via `client_manager.sample()`
-        client_manager.sample(len(expected_node_ids))
-        # Retrieve all nodes in `client_manager`
-        node_ids = {proxy.node_id for proxy in client_manager.all().values()}
-        # Update the GetNodesResponse and wait until the `client_manager` is updated
-        driver.get_node_ids.return_value = updated_expected_node_ids
-        sleep(0.1)
-        # Retrieve all nodes in `client_manager`
-        updated_node_ids = {proxy.node_id for proxy in client_manager.all().values()}
-        # Stop the thread
-        f_stop.set()
+        # Patching Event.wait with our custom function
+        with patch.object(Event, "wait", new=custom_wait):
+            thread, f_stop = start_update_client_manager_thread(driver, client_manager)
+            # Wait until all nodes are registered via `client_manager.sample()`
+            client_manager.sample(len(expected_node_ids))
+            # Retrieve all nodes in `client_manager`
+            node_ids = {proxy.node_id for proxy in client_manager.all().values()}
+            # Update the GetNodesResponse and wait until the `client_manager` is updated
+            driver.get_node_ids.return_value = updated_expected_node_ids
+            time.sleep(0.1)
+            # Retrieve all nodes in `client_manager`
+            updated_node_ids = {
+                proxy.node_id for proxy in client_manager.all().values()
+            }
+            # Stop the thread
+            f_stop.set()
 
         # Assert
         assert node_ids == set(expected_node_ids)