From d524b80d9decefb370d8d5353b42c86bf23569fd Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Tue, 13 Feb 2024 12:53:41 +0100
Subject: [PATCH 001/102] Fix minor doc errors (#2940)

Co-authored-by: Taner Topal <taner@flower.dev>
---
 doc/source/.gitignore                            | 1 +
 doc/source/example-walkthrough-pytorch-mnist.rst | 2 +-
 doc/source/how-to-use-built-in-mods.rst          | 2 +-
 doc/source/ref-changelog.md                      | 2 +-
 4 files changed, 4 insertions(+), 3 deletions(-)
 create mode 100644 doc/source/.gitignore

diff --git a/doc/source/.gitignore b/doc/source/.gitignore
new file mode 100644
index 000000000000..e9341a1383b7
--- /dev/null
+++ b/doc/source/.gitignore
@@ -0,0 +1 @@
+ref-api/
diff --git a/doc/source/example-walkthrough-pytorch-mnist.rst b/doc/source/example-walkthrough-pytorch-mnist.rst
index ab311813f5de..0be0af6e1ca6 100644
--- a/doc/source/example-walkthrough-pytorch-mnist.rst
+++ b/doc/source/example-walkthrough-pytorch-mnist.rst
@@ -76,7 +76,7 @@ Inside the server helper script *run-server.sh* you will find the following code
 
 
 We can go a bit deeper and see that :code:`server.py` simply launches a server that will coordinate three rounds of training.
-Flower Servers are very customizable, but for simple workloads, we can start a server using the :ref:`start_server <flwr-server-start_server-apiref>` function and leave all the configuration possibilities at their default values, as seen below.
+Flower Servers are very customizable, but for simple workloads, we can start a server using the `start_server <https://flower.ai/doc/framework/ref-api/flwr.server.start_server.html>`_ function and leave all the configuration possibilities at their default values, as seen below.
 
 .. code-block:: python
 
diff --git a/doc/source/how-to-use-built-in-mods.rst b/doc/source/how-to-use-built-in-mods.rst
index af7102de9d0b..3c13892356ea 100644
--- a/doc/source/how-to-use-built-in-mods.rst
+++ b/doc/source/how-to-use-built-in-mods.rst
@@ -86,4 +86,4 @@ Conclusion
 
 By following this guide, you have learned how to effectively use mods to enhance your ``ClientApp``'s functionality. Remember that the order of mods is crucial and affects how the input and output are processed.
 
-Enjoy building more robust and flexible ``ClientApp``s with mods!
+Enjoy building more robust and flexible ``ClientApp`` s with mods!
diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 78d1e0e491a4..e9282632410d 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -217,7 +217,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Restructure Flower Docs** ([#1824](https://github.com/adap/flower/pull/1824), [#1865](https://github.com/adap/flower/pull/1865), [#1884](https://github.com/adap/flower/pull/1884), [#1887](https://github.com/adap/flower/pull/1887), [#1919](https://github.com/adap/flower/pull/1919), [#1922](https://github.com/adap/flower/pull/1922), [#1920](https://github.com/adap/flower/pull/1920), [#1923](https://github.com/adap/flower/pull/1923), [#1924](https://github.com/adap/flower/pull/1924), [#1962](https://github.com/adap/flower/pull/1962), [#2006](https://github.com/adap/flower/pull/2006), [#2133](https://github.com/adap/flower/pull/2133), [#2203](https://github.com/adap/flower/pull/2203), [#2215](https://github.com/adap/flower/pull/2215), [#2122](https://github.com/adap/flower/pull/2122), [#2223](https://github.com/adap/flower/pull/2223), [#2219](https://github.com/adap/flower/pull/2219), [#2232](https://github.com/adap/flower/pull/2232), [#2233](https://github.com/adap/flower/pull/2233), [#2234](https://github.com/adap/flower/pull/2234), [#2235](https://github.com/adap/flower/pull/2235), [#2237](https://github.com/adap/flower/pull/2237), [#2238](https://github.com/adap/flower/pull/2238), [#2242](https://github.com/adap/flower/pull/2242), [#2231](https://github.com/adap/flower/pull/2231), [#2243](https://github.com/adap/flower/pull/2243), [#2227](https://github.com/adap/flower/pull/2227))
 
-  Much effort went into a completely restructured Flower docs experience. The documentation on [flower.dev/docs](flower.dev/docs) is now divided into Flower Framework, Flower Baselines, Flower Android SDK, Flower iOS SDK, and code example projects.
+  Much effort went into a completely restructured Flower docs experience. The documentation on [flower.dev/docs](https://flower.dev/docs) is now divided into Flower Framework, Flower Baselines, Flower Android SDK, Flower iOS SDK, and code example projects.
 
 - **Introduce Flower Swift SDK** ([#1858](https://github.com/adap/flower/pull/1858), [#1897](https://github.com/adap/flower/pull/1897))
 

From d213534fccfa57c6b963bf8f73ff7141eff73e7e Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Tue, 13 Feb 2024 18:16:33 +0100
Subject: [PATCH 002/102] Add `node_id` to `Metadata` (#2912)

Co-authored-by: Heng Pan <134433891+panh99@users.noreply.github.com>
---
 src/py/flwr/client/grpc_client/connection.py   |  1 +
 .../flwr/client/grpc_client/connection_test.py |  2 ++
 .../client/message_handler/message_handler.py  |  8 +++++---
 .../message_handler/message_handler_test.py    |  2 ++
 .../mod/secure_aggregation/secaggplus_mod.py   |  9 ++++++++-
 .../secure_aggregation/secaggplus_mod_test.py  | 18 ++++++++++++++++--
 src/py/flwr/client/mod/utils_test.py           |  4 +++-
 src/py/flwr/common/message.py                  |  3 +++
 src/py/flwr/common/serde.py                    |  2 ++
 src/py/flwr/common/serde_test.py               |  5 +++++
 10 files changed, 47 insertions(+), 7 deletions(-)

diff --git a/src/py/flwr/client/grpc_client/connection.py b/src/py/flwr/client/grpc_client/connection.py
index c7d8494b8fde..aaaf1fcc863c 100644
--- a/src/py/flwr/client/grpc_client/connection.py
+++ b/src/py/flwr/client/grpc_client/connection.py
@@ -173,6 +173,7 @@ def receive() -> Message:
                 task_id=str(uuid.uuid4()),
                 group_id="",
                 ttl="",
+                node_id=0,
                 task_type=task_type,
             ),
             content=recordset,
diff --git a/src/py/flwr/client/grpc_client/connection_test.py b/src/py/flwr/client/grpc_client/connection_test.py
index 7ba39568f0a4..e193c9484fff 100644
--- a/src/py/flwr/client/grpc_client/connection_test.py
+++ b/src/py/flwr/client/grpc_client/connection_test.py
@@ -48,6 +48,7 @@
         run_id=0,
         task_id="",
         group_id="",
+        node_id=0,
         ttl="",
         task_type=TASK_TYPE_GET_PROPERTIES,
     ),
@@ -60,6 +61,7 @@
         run_id=0,
         task_id="",
         group_id="",
+        node_id=0,
         ttl="",
         task_type="reconnect",
     ),
diff --git a/src/py/flwr/client/message_handler/message_handler.py b/src/py/flwr/client/message_handler/message_handler.py
index c45d13d43f94..52dbdec7691b 100644
--- a/src/py/flwr/client/message_handler/message_handler.py
+++ b/src/py/flwr/client/message_handler/message_handler.py
@@ -92,6 +92,7 @@ def handle_control_message(message: Message) -> Tuple[Optional[Message], int]:
                 run_id=0,
                 task_id="",
                 group_id="",
+                node_id=0,
                 ttl="",
                 task_type="reconnect",
             ),
@@ -150,9 +151,10 @@ def handle_legacy_message_from_tasktype(
     # Return Message
     out_message = Message(
         metadata=Metadata(
-            run_id=0,  # Non-user defined
-            task_id="",  # Non-user defined
-            group_id="",  # Non-user defined
+            run_id=0,
+            task_id="",
+            group_id="",
+            node_id=0,
             ttl="",
             task_type=task_type,
         ),
diff --git a/src/py/flwr/client/message_handler/message_handler_test.py b/src/py/flwr/client/message_handler/message_handler_test.py
index 288597b764f4..820de9bbe27c 100644
--- a/src/py/flwr/client/message_handler/message_handler_test.py
+++ b/src/py/flwr/client/message_handler/message_handler_test.py
@@ -124,6 +124,7 @@ def test_client_without_get_properties() -> None:
             run_id=0,
             task_id=str(uuid.uuid4()),
             group_id="",
+            node_id=0,
             ttl="",
             task_type=TASK_TYPE_GET_PROPERTIES,
         ),
@@ -162,6 +163,7 @@ def test_client_with_get_properties() -> None:
             run_id=0,
             task_id=str(uuid.uuid4()),
             group_id="",
+            node_id=0,
             ttl="",
             task_type=TASK_TYPE_GET_PROPERTIES,
         ),
diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
index a6f59b736911..eba5dec658aa 100644
--- a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
@@ -207,7 +207,14 @@ def secaggplus_mod(
 
     # Return message
     return Message(
-        metadata=Metadata(0, "", "", "", TASK_TYPE_FIT),
+        metadata=Metadata(
+            run_id=0,
+            task_id="",
+            group_id="",
+            node_id=0,
+            ttl="",
+            task_type=TASK_TYPE_FIT,
+        ),
         content=RecordSet(configs={RECORD_KEY_CONFIGS: ConfigsRecord(res, False)}),
     )
 
diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
index 2ca9748f8219..d0f461e7a8de 100644
--- a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
@@ -57,7 +57,14 @@ def get_test_handler(
 
     def empty_ffn(_: Message, _2: Context) -> Message:
         return Message(
-            metadata=Metadata(0, "", "", "", TASK_TYPE_FIT),
+            metadata=Metadata(
+                run_id=0,
+                task_id="",
+                group_id="",
+                node_id=0,
+                ttl="",
+                task_type=TASK_TYPE_FIT,
+            ),
             content=RecordSet(),
         )
 
@@ -65,7 +72,14 @@ def empty_ffn(_: Message, _2: Context) -> Message:
 
     def func(configs: Dict[str, ConfigsRecordValues]) -> Dict[str, ConfigsRecordValues]:
         in_msg = Message(
-            metadata=Metadata(0, "", "", "", TASK_TYPE_FIT),
+            metadata=Metadata(
+                run_id=0,
+                task_id="",
+                group_id="",
+                node_id=0,
+                ttl="",
+                task_type=TASK_TYPE_FIT,
+            ),
             content=RecordSet(configs={RECORD_KEY_CONFIGS: ConfigsRecord(configs)}),
         )
         out_msg = app(in_msg, ctxt)
diff --git a/src/py/flwr/client/mod/utils_test.py b/src/py/flwr/client/mod/utils_test.py
index 00c0ef850dc0..07b38a250d6c 100644
--- a/src/py/flwr/client/mod/utils_test.py
+++ b/src/py/flwr/client/mod/utils_test.py
@@ -74,7 +74,9 @@ def app(message: Message, context: Context) -> Message:
 def _get_dummy_flower_message() -> Message:
     return Message(
         content=RecordSet(),
-        metadata=Metadata(run_id=0, task_id="", group_id="", ttl="", task_type="mock"),
+        metadata=Metadata(
+            run_id=0, task_id="", group_id="", node_id=0, ttl="", task_type="mock"
+        ),
     )
 
 
diff --git a/src/py/flwr/common/message.py b/src/py/flwr/common/message.py
index 1a971749966e..ada4a617b60d 100644
--- a/src/py/flwr/common/message.py
+++ b/src/py/flwr/common/message.py
@@ -33,6 +33,8 @@ class Metadata:
     group_id : str
         An identifier for grouping tasks. In some settings
         this is used as the FL round.
+    node_id : int
+        An identifier for the node running a task.
     ttl : str
         Time-to-live for this task.
     task_type : str
@@ -43,6 +45,7 @@ class Metadata:
     run_id: int
     task_id: str
     group_id: str
+    node_id: int
     ttl: str
     task_type: str
 
diff --git a/src/py/flwr/common/serde.py b/src/py/flwr/common/serde.py
index 3b8e4c3a1c2c..5a8c5c753136 100644
--- a/src/py/flwr/common/serde.py
+++ b/src/py/flwr/common/serde.py
@@ -563,6 +563,7 @@ def message_from_taskins(taskins: TaskIns) -> Message:
         run_id=taskins.run_id,
         task_id=taskins.task_id,
         group_id=taskins.group_id,
+        node_id=taskins.task.consumer.node_id,
         ttl=taskins.task.ttl,
         task_type=taskins.task.task_type,
     )
@@ -592,6 +593,7 @@ def message_from_taskres(taskres: TaskRes) -> Message:
         run_id=taskres.run_id,
         task_id=taskres.task_id,
         group_id=taskres.group_id,
+        node_id=taskres.task.consumer.node_id,
         ttl=taskres.task.ttl,
         task_type=taskres.task.task_type,
     )
diff --git a/src/py/flwr/common/serde_test.py b/src/py/flwr/common/serde_test.py
index c30f24d3700c..1c36f2171149 100644
--- a/src/py/flwr/common/serde_test.py
+++ b/src/py/flwr/common/serde_test.py
@@ -219,6 +219,7 @@ def metadata(self) -> Metadata:
             run_id=self.rng.randint(0, 1 << 30),
             task_id=self.get_str(64),
             group_id=self.get_str(30),
+            node_id=self.rng.randint(0, 1 << 63),
             ttl=self.get_str(10),
             task_type=self.get_str(10),
         )
@@ -309,6 +310,7 @@ def test_message_to_and_from_taskins() -> None:
             run_id=0,
             task_id="",
             group_id="",
+            node_id=metadata.node_id,
             ttl=metadata.ttl,
             task_type=metadata.task_type,
         ),
@@ -320,6 +322,7 @@ def test_message_to_and_from_taskins() -> None:
     taskins.run_id = metadata.run_id
     taskins.task_id = metadata.task_id
     taskins.group_id = metadata.group_id
+    taskins.task.consumer.node_id = metadata.node_id
     deserialized = message_from_taskins(taskins)
 
     # Assert
@@ -337,6 +340,7 @@ def test_message_to_and_from_taskres() -> None:
             run_id=0,
             task_id="",
             group_id="",
+            node_id=metadata.node_id,
             ttl=metadata.ttl,
             task_type=metadata.task_type,
         ),
@@ -348,6 +352,7 @@ def test_message_to_and_from_taskres() -> None:
     taskres.run_id = metadata.run_id
     taskres.task_id = metadata.task_id
     taskres.group_id = metadata.group_id
+    taskres.task.consumer.node_id = metadata.node_id
     deserialized = message_from_taskres(taskres)
 
     # Assert

From d1c0481e254a3d8d7948bd8cbe2ea3bf1a5748cc Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Tue, 13 Feb 2024 19:39:42 +0100
Subject: [PATCH 003/102] Update mt-pytorch example (#2933)

---
 examples/mt-pytorch/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/mt-pytorch/README.md b/examples/mt-pytorch/README.md
index 721a26ed814d..0f676044ee90 100644
--- a/examples/mt-pytorch/README.md
+++ b/examples/mt-pytorch/README.md
@@ -36,17 +36,17 @@ flower-superlink --insecure
 In a new terminal window, start the first long-running Flower client:
 
 ```bash
-flower-client client:app --insecure
+flower-client-app client:app --insecure
 ```
 
 In yet another new terminal window, start the second long-running Flower client:
 
 ```bash
-flower-client client:app --insecure
+flower-client-app client:app --insecure
 ```
 
 ## Start the driver
 
 ```bash
-python driver.py
+python start_driver.py
 ```

From db9a527573f2f8beb034e421d76ae702bf1cadc5 Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.dev>
Date: Wed, 14 Feb 2024 12:46:51 +0100
Subject: [PATCH 004/102] Create ServerApp (#2931)

---
 doc/source/how-to-use-built-in-mods.rst |  2 +-
 src/py/flwr/client/clientapp.py         |  4 +-
 src/py/flwr/server/__init__.py          |  2 +
 src/py/flwr/server/serverapp.py         | 95 +++++++++++++++++++++++++
 src/py/flwr/server/typing.py            | 23 ++++++
 5 files changed, 123 insertions(+), 3 deletions(-)
 create mode 100644 src/py/flwr/server/serverapp.py
 create mode 100644 src/py/flwr/server/typing.py

diff --git a/doc/source/how-to-use-built-in-mods.rst b/doc/source/how-to-use-built-in-mods.rst
index 3c13892356ea..341139175074 100644
--- a/doc/source/how-to-use-built-in-mods.rst
+++ b/doc/source/how-to-use-built-in-mods.rst
@@ -86,4 +86,4 @@ Conclusion
 
 By following this guide, you have learned how to effectively use mods to enhance your ``ClientApp``'s functionality. Remember that the order of mods is crucial and affects how the input and output are processed.
 
-Enjoy building more robust and flexible ``ClientApp`` s with mods!
+Enjoy building a more robust and flexible ``ClientApp`` with mods!
diff --git a/src/py/flwr/client/clientapp.py b/src/py/flwr/client/clientapp.py
index 51c912890c7e..2d8e9e652dba 100644
--- a/src/py/flwr/client/clientapp.py
+++ b/src/py/flwr/client/clientapp.py
@@ -72,12 +72,12 @@ def ffn(
         self._call = make_ffn(ffn, mods if mods is not None else [])
 
     def __call__(self, message: Message, context: Context) -> Message:
-        """."""
+        """Execute `ClientApp`."""
         return self._call(message, context)
 
 
 class LoadClientAppError(Exception):
-    """."""
+    """Error when trying to load `ClientApp`."""
 
 
 def load_client_app(module_attribute_str: str) -> ClientApp:
diff --git a/src/py/flwr/server/__init__.py b/src/py/flwr/server/__init__.py
index 84c24b4bc2c1..1472bcaf0893 100644
--- a/src/py/flwr/server/__init__.py
+++ b/src/py/flwr/server/__init__.py
@@ -26,6 +26,7 @@
 from .client_manager import SimpleClientManager as SimpleClientManager
 from .history import History as History
 from .server import Server as Server
+from .serverapp import ServerApp as ServerApp
 
 __all__ = [
     "ClientManager",
@@ -36,6 +37,7 @@
     "run_server_app",
     "run_superlink",
     "Server",
+    "ServerApp",
     "ServerConfig",
     "SimpleClientManager",
     "start_server",
diff --git a/src/py/flwr/server/serverapp.py b/src/py/flwr/server/serverapp.py
new file mode 100644
index 000000000000..1a89093621e1
--- /dev/null
+++ b/src/py/flwr/server/serverapp.py
@@ -0,0 +1,95 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Flower ServerApp."""
+
+
+import importlib
+from typing import Optional, cast
+
+from flwr.common.context import Context
+from flwr.server.driver.driver import Driver
+from flwr.server.strategy import Strategy
+
+from .app import ServerConfig
+from .client_manager import ClientManager
+from .server import Server
+
+
+class ServerApp:
+    """Flower ServerApp."""
+
+    def __init__(
+        self,
+        server: Optional[Server] = None,
+        config: Optional[ServerConfig] = None,
+        strategy: Optional[Strategy] = None,
+        client_manager: Optional[ClientManager] = None,
+    ) -> None:
+        self.server = server
+        self.config = config
+        self.strategy = strategy
+        self.client_manager = client_manager
+
+    def __call__(self, driver: Driver, context: Context) -> None:
+        """Execute `ServerApp`."""
+
+
+class LoadServerAppError(Exception):
+    """Error when trying to load `ServerApp`."""
+
+
+def load_server_app(module_attribute_str: str) -> ServerApp:
+    """Load the `ServerApp` object specified in a module attribute string.
+
+    The module/attribute string should have the form <module>:<attribute>. Valid
+    examples include `server:app` and `project.package.module:wrapper.app`. It
+    must refer to a module on the PYTHONPATH, the module needs to have the specified
+    attribute, and the attribute must be of type `ServerApp`.
+    """
+    module_str, _, attributes_str = module_attribute_str.partition(":")
+    if not module_str:
+        raise LoadServerAppError(
+            f"Missing module in {module_attribute_str}",
+        ) from None
+    if not attributes_str:
+        raise LoadServerAppError(
+            f"Missing attribute in {module_attribute_str}",
+        ) from None
+
+    # Load module
+    try:
+        module = importlib.import_module(module_str)
+    except ModuleNotFoundError:
+        raise LoadServerAppError(
+            f"Unable to load module {module_str}",
+        ) from None
+
+    # Recursively load attribute
+    attribute = module
+    try:
+        for attribute_str in attributes_str.split("."):
+            attribute = getattr(attribute, attribute_str)
+    except AttributeError:
+        raise LoadServerAppError(
+            f"Unable to load attribute {attributes_str} from module {module_str}",
+        ) from None
+
+    # Check type
+    if not isinstance(attribute, ServerApp):
+        raise LoadServerAppError(
+            f"Attribute {attributes_str} is not of type {ServerApp}",
+        ) from None
+
+    return cast(ServerApp, attribute)
diff --git a/src/py/flwr/server/typing.py b/src/py/flwr/server/typing.py
new file mode 100644
index 000000000000..728121c2eddf
--- /dev/null
+++ b/src/py/flwr/server/typing.py
@@ -0,0 +1,23 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Custom types for Flower servers."""
+
+
+from typing import Callable
+
+from flwr.common.context import Context
+from flwr.server.driver import Driver
+
+ServerAppCallable = Callable[[Driver, Context], None]

From 87ad890da6d9a5f947c6b21e46678f5b10c87100 Mon Sep 17 00:00:00 2001
From: Robert Steiner <robert@flower.dev>
Date: Wed, 14 Feb 2024 13:29:34 +0100
Subject: [PATCH 005/102] Update artifact bucket ID (#2945)

---
 .github/workflows/e2e.yml                     |  7 ++++---
 .github/workflows/framework-draft-release.yml | 21 +++++++++++--------
 .github/workflows/framework-release.yml       | 13 +++++++-----
 3 files changed, 24 insertions(+), 17 deletions(-)

diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml
index 065e79fff9ab..c89896dd9d6b 100644
--- a/.github/workflows/e2e.yml
+++ b/.github/workflows/e2e.yml
@@ -14,6 +14,7 @@ concurrency:
 
 env:
   FLWR_TELEMETRY_ENABLED: 0
+  ARTIFACT_BUCKET: artifact.flower.ai
 
 jobs:
   wheel:
@@ -43,7 +44,7 @@ jobs:
           echo "SHORT_SHA=$sha_short" >> "$GITHUB_OUTPUT"
           [ -z "${{ github.head_ref }}" ] && dir="${{ github.ref_name }}" || dir="pr/${{ github.head_ref }}"
           echo "DIR=$dir" >> "$GITHUB_OUTPUT"
-          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./ s3://artifact.flower.dev/py/$dir/$sha_short --recursive
+          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./ s3://${{ env.ARTIFACT_BUCKET }}/py/$dir/$sha_short --recursive
     outputs:
       whl_path: ${{ steps.upload.outputs.WHL_PATH }}
       short_sha: ${{ steps.upload.outputs.SHORT_SHA }}
@@ -123,7 +124,7 @@ jobs:
       - name: Install Flower wheel from artifact store
         if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork && github.actor != 'dependabot[bot]' }}
         run: |
-          python -m pip install https://artifact.flower.dev/py/${{ needs.wheel.outputs.dir }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
+          python -m pip install https://${{ env.ARTIFACT_BUCKET }}/py/${{ needs.wheel.outputs.dir }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Download dataset
         if: ${{ matrix.dataset }}
         run: python -c "${{ matrix.dataset }}"
@@ -164,7 +165,7 @@ jobs:
       - name: Install Flower wheel from artifact store
         if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork && github.actor != 'dependabot[bot]' }}
         run: |
-          python -m pip install https://artifact.flower.dev/py/${{ needs.wheel.outputs.dir }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
+          python -m pip install https://${{ env.ARTIFACT_BUCKET }}/py/${{ needs.wheel.outputs.dir }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Cache Datasets
         uses: actions/cache@v3
         with:
diff --git a/.github/workflows/framework-draft-release.yml b/.github/workflows/framework-draft-release.yml
index 959d17249765..91a89953cf96 100644
--- a/.github/workflows/framework-draft-release.yml
+++ b/.github/workflows/framework-draft-release.yml
@@ -5,6 +5,9 @@ on:
     tags:
       - "v*.*.*"
 
+env:
+  ARTIFACT_BUCKET: artifact.flower.ai
+
 jobs:
   publish:
     if: ${{ github.repository == 'adap/flower' }}
@@ -26,16 +29,16 @@ jobs:
         run: |
           tag_name=$(echo "${GITHUB_REF_NAME}" | cut -c2-)
           echo "TAG_NAME=$tag_name" >> "$GITHUB_ENV"
-          
+
           wheel_name="flwr-${tag_name}-py3-none-any.whl"
           echo "WHEEL_NAME=$wheel_name" >> "$GITHUB_ENV"
-          
+
           tar_name="flwr-${tag_name}.tar.gz"
           echo "TAR_NAME=$tar_name" >> "$GITHUB_ENV"
 
-          wheel_url="https://artifact.flower.dev/py/main/${GITHUB_SHA::7}/${wheel_name}"
-          tar_url="https://artifact.flower.dev/py/main/${GITHUB_SHA::7}/${tar_name}"
-          
+          wheel_url="https://${{ env.ARTIFACT_BUCKET }}/py/main/${GITHUB_SHA::7}/${wheel_name}"
+          tar_url="https://${{ env.ARTIFACT_BUCKET }}/py/main/${GITHUB_SHA::7}/${tar_name}"
+
           curl $wheel_url --output $wheel_name
           curl $tar_url --output $tar_name
       - name: Upload wheel
@@ -44,14 +47,14 @@ jobs:
           AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
           AWS_SECRET_ACCESS_KEY: ${{ secrets. AWS_SECRET_ACCESS_KEY }}
         run: |
-          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./${{ env.WHEEL_NAME }} s3://artifact.flower.dev/py/release/v${{ env.TAG_NAME }}/${{ env.WHEEL_NAME }}
-          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./${{ env.TAR_NAME }} s3://artifact.flower.dev/py/release/v${{ env.TAG_NAME }}/${{ env.TAR_NAME }}
-          
+          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./${{ env.WHEEL_NAME }} s3://${{ env.ARTIFACT_BUCKET }}/py/release/v${{ env.TAG_NAME }}/${{ env.WHEEL_NAME }}
+          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./${{ env.TAR_NAME }} s3://${{ env.ARTIFACT_BUCKET }}/py/release/v${{ env.TAG_NAME }}/${{ env.TAR_NAME }}
+
       - name: Generate body
         run: |
           ./dev/get-latest-changelog.sh > body.md
           cat body.md
-          
+
       - name: Release
         uses: softprops/action-gh-release@v1
         with:
diff --git a/.github/workflows/framework-release.yml b/.github/workflows/framework-release.yml
index f052d3a4a928..04b68fd38af9 100644
--- a/.github/workflows/framework-release.yml
+++ b/.github/workflows/framework-release.yml
@@ -3,11 +3,14 @@ name: Publish `flwr` release on PyPI
 on:
   release:
     types: [released]
-    
+
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.event.pull_request.number || github.ref }}
   cancel-in-progress: true
-  
+
+env:
+  ARTIFACT_BUCKET: artifact.flower.ai
+
 jobs:
   publish:
     if: ${{ github.repository == 'adap/flower' }}
@@ -28,11 +31,11 @@ jobs:
       run: |
         TAG_NAME=$(echo "${GITHUB_REF_NAME}" | cut -c2-)
 
-        wheel_name="flwr-${TAG_NAME}-py3-none-any.whl"        
+        wheel_name="flwr-${TAG_NAME}-py3-none-any.whl"
         tar_name="flwr-${TAG_NAME}.tar.gz"
 
-        wheel_url="https://artifact.flower.dev/py/release/v${TAG_NAME}/${wheel_name}"
-        tar_url="https://artifact.flower.dev/py/release/v${TAG_NAME}/${tar_name}"
+        wheel_url="https://${{ env.ARTIFACT_BUCKET }}/py/release/v${TAG_NAME}/${wheel_name}"
+        tar_url="https://${{ env.ARTIFACT_BUCKET }}/py/release/v${TAG_NAME}/${tar_name}"
 
         mkdir -p dist
 

From cbbb813a7e46c165788e5c405a90dbfb6565730b Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.dev>
Date: Wed, 14 Feb 2024 14:53:21 +0100
Subject: [PATCH 006/102] Load ServerApp (#2934)

---
 src/py/flwr/server/__init__.py      |  2 +-
 src/py/flwr/server/app.py           | 44 +++++++++++++++--------------
 src/py/flwr/server/driver/app.py    |  3 +-
 src/py/flwr/server/server_config.py | 31 ++++++++++++++++++++
 src/py/flwr/server/serverapp.py     |  2 +-
 src/py/flwr/simulation/app.py       |  3 +-
 6 files changed, 60 insertions(+), 25 deletions(-)
 create mode 100644 src/py/flwr/server/server_config.py

diff --git a/src/py/flwr/server/__init__.py b/src/py/flwr/server/__init__.py
index 1472bcaf0893..b0f95f903811 100644
--- a/src/py/flwr/server/__init__.py
+++ b/src/py/flwr/server/__init__.py
@@ -16,7 +16,6 @@
 
 
 from . import driver, strategy
-from .app import ServerConfig as ServerConfig
 from .app import run_driver_api as run_driver_api
 from .app import run_fleet_api as run_fleet_api
 from .app import run_server_app as run_server_app
@@ -26,6 +25,7 @@
 from .client_manager import SimpleClientManager as SimpleClientManager
 from .history import History as History
 from .server import Server as Server
+from .server_config import ServerConfig as ServerConfig
 from .serverapp import ServerApp as ServerApp
 
 __all__ = [
diff --git a/src/py/flwr/server/app.py b/src/py/flwr/server/app.py
index 6e2f1f7dc88d..66adcbdb6b85 100644
--- a/src/py/flwr/server/app.py
+++ b/src/py/flwr/server/app.py
@@ -19,7 +19,6 @@
 import importlib.util
 import sys
 import threading
-from dataclasses import dataclass
 from logging import DEBUG, ERROR, INFO, WARN
 from os.path import isfile
 from pathlib import Path
@@ -43,17 +42,20 @@
 from flwr.proto.fleet_pb2_grpc import (  # pylint: disable=E0611
     add_FleetServicer_to_server,
 )
-from flwr.server.client_manager import ClientManager, SimpleClientManager
-from flwr.server.history import History
-from flwr.server.server import Server
-from flwr.server.strategy import FedAvg, Strategy
-from flwr.server.superlink.driver.driver_servicer import DriverServicer
-from flwr.server.superlink.fleet.grpc_bidi.grpc_server import (
+
+from .client_manager import ClientManager, SimpleClientManager
+from .history import History
+from .server import Server
+from .server_config import ServerConfig
+from .serverapp import ServerApp, load_server_app
+from .strategy import FedAvg, Strategy
+from .superlink.driver.driver_servicer import DriverServicer
+from .superlink.fleet.grpc_bidi.grpc_server import (
     generic_create_grpc_server,
     start_grpc_server,
 )
-from flwr.server.superlink.fleet.grpc_rere.fleet_servicer import FleetServicer
-from flwr.server.superlink.state import StateFactory
+from .superlink.fleet.grpc_rere.fleet_servicer import FleetServicer
+from .superlink.state import StateFactory
 
 ADDRESS_DRIVER_API = "0.0.0.0:9091"
 ADDRESS_FLEET_API_GRPC_RERE = "0.0.0.0:9092"
@@ -63,18 +65,6 @@
 DATABASE = ":flwr-in-memory-state:"
 
 
-@dataclass
-class ServerConfig:
-    """Flower server config.
-
-    All attributes have default values which allows users to configure just the ones
-    they care about.
-    """
-
-    num_rounds: int = 1
-    round_timeout: Optional[float] = None
-
-
 def run_server_app() -> None:
     """Run Flower server app."""
     event(EventType.RUN_SERVER_APP_ENTER)
@@ -126,6 +116,18 @@ def run_server_app() -> None:
 
     log(WARN, "Not implemented: run_server_app")
 
+    server_app_dir = args.dir
+    if server_app_dir is not None:
+        sys.path.insert(0, server_app_dir)
+
+    def _load() -> ServerApp:
+        server_app: ServerApp = load_server_app(getattr(args, "server-app"))
+        return server_app
+
+    server_app = _load()
+
+    log(DEBUG, "server_app: `%s`", server_app)
+
     event(EventType.RUN_SERVER_APP_LEAVE)
 
 
diff --git a/src/py/flwr/server/driver/app.py b/src/py/flwr/server/driver/app.py
index ae47c58f4e9c..b47454b7b4b6 100644
--- a/src/py/flwr/server/driver/app.py
+++ b/src/py/flwr/server/driver/app.py
@@ -26,10 +26,11 @@
 from flwr.common.address import parse_address
 from flwr.common.logger import log
 from flwr.proto import driver_pb2  # pylint: disable=E0611
-from flwr.server.app import ServerConfig, init_defaults, run_fl
+from flwr.server.app import init_defaults, run_fl
 from flwr.server.client_manager import ClientManager
 from flwr.server.history import History
 from flwr.server.server import Server
+from flwr.server.server_config import ServerConfig
 from flwr.server.strategy import Strategy
 
 from .driver_client_proxy import DriverClientProxy
diff --git a/src/py/flwr/server/server_config.py b/src/py/flwr/server/server_config.py
new file mode 100644
index 000000000000..823f832da6f8
--- /dev/null
+++ b/src/py/flwr/server/server_config.py
@@ -0,0 +1,31 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Flower ServerConfig."""
+
+
+from dataclasses import dataclass
+from typing import Optional
+
+
+@dataclass
+class ServerConfig:
+    """Flower server config.
+
+    All attributes have default values which allows users to configure just the ones
+    they care about.
+    """
+
+    num_rounds: int = 1
+    round_timeout: Optional[float] = None
diff --git a/src/py/flwr/server/serverapp.py b/src/py/flwr/server/serverapp.py
index 1a89093621e1..1ffa087719dc 100644
--- a/src/py/flwr/server/serverapp.py
+++ b/src/py/flwr/server/serverapp.py
@@ -22,9 +22,9 @@
 from flwr.server.driver.driver import Driver
 from flwr.server.strategy import Strategy
 
-from .app import ServerConfig
 from .client_manager import ClientManager
 from .server import Server
+from .server_config import ServerConfig
 
 
 class ServerApp:
diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py
index 6a18a258ac60..b159042588c9 100644
--- a/src/py/flwr/simulation/app.py
+++ b/src/py/flwr/simulation/app.py
@@ -29,9 +29,10 @@
 from flwr.common import EventType, event
 from flwr.common.logger import log
 from flwr.server import Server
-from flwr.server.app import ServerConfig, init_defaults, run_fl
+from flwr.server.app import init_defaults, run_fl
 from flwr.server.client_manager import ClientManager
 from flwr.server.history import History
+from flwr.server.server_config import ServerConfig
 from flwr.server.strategy import Strategy
 from flwr.simulation.ray_transport.ray_actor import (
     DefaultActor,

From 314492cc64eff9689d98c2d22eba348d801c4d28 Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Wed, 14 Feb 2024 14:23:59 +0000
Subject: [PATCH 007/102] Rename `task_*` to `message_*` (#2944)

---
 src/py/flwr/client/clientapp.py               |  4 +-
 src/py/flwr/client/grpc_client/connection.py  | 40 +++++++++----------
 .../client/grpc_client/connection_test.py     | 12 +++---
 .../client/message_handler/message_handler.py | 32 +++++++--------
 .../message_handler/message_handler_test.py   | 20 +++++-----
 .../mod/secure_aggregation/secaggplus_mod.py  |  8 ++--
 .../secure_aggregation/secaggplus_mod_test.py | 10 ++---
 src/py/flwr/client/mod/utils_test.py          |  2 +-
 src/py/flwr/common/constant.py                |  8 ++--
 src/py/flwr/common/message.py                 | 20 +++++-----
 src/py/flwr/common/serde.py                   | 12 +++---
 src/py/flwr/common/serde_test.py              | 16 ++++----
 .../flwr/server/driver/driver_client_proxy.py | 16 ++++----
 .../server/driver/driver_client_proxy_test.py | 18 ++++-----
 14 files changed, 109 insertions(+), 109 deletions(-)

diff --git a/src/py/flwr/client/clientapp.py b/src/py/flwr/client/clientapp.py
index 2d8e9e652dba..cfc59c9298ed 100644
--- a/src/py/flwr/client/clientapp.py
+++ b/src/py/flwr/client/clientapp.py
@@ -19,7 +19,7 @@
 from typing import List, Optional, cast
 
 from flwr.client.message_handler.message_handler import (
-    handle_legacy_message_from_tasktype,
+    handle_legacy_message_from_msgtype,
 )
 from flwr.client.mod.utils import make_ffn
 from flwr.client.typing import ClientFn, Mod
@@ -63,7 +63,7 @@ def ffn(
             message: Message,
             context: Context,
         ) -> Message:  # pylint: disable=invalid-name
-            out_message = handle_legacy_message_from_tasktype(
+            out_message = handle_legacy_message_from_msgtype(
                 client_fn=client_fn, message=message, context=context
             )
             return out_message
diff --git a/src/py/flwr/client/grpc_client/connection.py b/src/py/flwr/client/grpc_client/connection.py
index aaaf1fcc863c..e6d21963fcbf 100644
--- a/src/py/flwr/client/grpc_client/connection.py
+++ b/src/py/flwr/client/grpc_client/connection.py
@@ -27,10 +27,10 @@
 from flwr.common import serde
 from flwr.common.configsrecord import ConfigsRecord
 from flwr.common.constant import (
-    TASK_TYPE_EVALUATE,
-    TASK_TYPE_FIT,
-    TASK_TYPE_GET_PARAMETERS,
-    TASK_TYPE_GET_PROPERTIES,
+    MESSAGE_TYPE_EVALUATE,
+    MESSAGE_TYPE_FIT,
+    MESSAGE_TYPE_GET_PARAMETERS,
+    MESSAGE_TYPE_GET_PROPERTIES,
 )
 from flwr.common.grpc import create_channel
 from flwr.common.logger import log
@@ -133,33 +133,33 @@ def receive() -> Message:
 
         # ServerMessage proto --> *Ins --> RecordSet
         field = proto.WhichOneof("msg")
-        task_type = ""
+        message_type = ""
         if field == "get_properties_ins":
             recordset = compat.getpropertiesins_to_recordset(
                 serde.get_properties_ins_from_proto(proto.get_properties_ins)
             )
-            task_type = TASK_TYPE_GET_PROPERTIES
+            message_type = MESSAGE_TYPE_GET_PROPERTIES
         elif field == "get_parameters_ins":
             recordset = compat.getparametersins_to_recordset(
                 serde.get_parameters_ins_from_proto(proto.get_parameters_ins)
             )
-            task_type = TASK_TYPE_GET_PARAMETERS
+            message_type = MESSAGE_TYPE_GET_PARAMETERS
         elif field == "fit_ins":
             recordset = compat.fitins_to_recordset(
                 serde.fit_ins_from_proto(proto.fit_ins), False
             )
-            task_type = TASK_TYPE_FIT
+            message_type = MESSAGE_TYPE_FIT
         elif field == "evaluate_ins":
             recordset = compat.evaluateins_to_recordset(
                 serde.evaluate_ins_from_proto(proto.evaluate_ins), False
             )
-            task_type = TASK_TYPE_EVALUATE
+            message_type = MESSAGE_TYPE_EVALUATE
         elif field == "reconnect_ins":
             recordset = RecordSet()
             recordset.set_configs(
                 "config", ConfigsRecord({"seconds": proto.reconnect_ins.seconds})
             )
-            task_type = "reconnect"
+            message_type = "reconnect"
         else:
             raise ValueError(
                 "Unsupported instruction in ServerMessage, "
@@ -170,44 +170,44 @@ def receive() -> Message:
         return Message(
             metadata=Metadata(
                 run_id=0,
-                task_id=str(uuid.uuid4()),
+                message_id=str(uuid.uuid4()),
                 group_id="",
                 ttl="",
                 node_id=0,
-                task_type=task_type,
+                message_type=message_type,
             ),
             content=recordset,
         )
 
     def send(message: Message) -> None:
-        # Retrieve RecordSet and task_type
+        # Retrieve RecordSet and message_type
         recordset = message.content
-        task_type = message.metadata.task_type
+        message_type = message.metadata.message_type
 
         # RecordSet --> *Res --> *Res proto -> ClientMessage proto
-        if task_type == TASK_TYPE_GET_PROPERTIES:
+        if message_type == MESSAGE_TYPE_GET_PROPERTIES:
             getpropres = compat.recordset_to_getpropertiesres(recordset)
             msg_proto = ClientMessage(
                 get_properties_res=serde.get_properties_res_to_proto(getpropres)
             )
-        elif task_type == TASK_TYPE_GET_PARAMETERS:
+        elif message_type == MESSAGE_TYPE_GET_PARAMETERS:
             getparamres = compat.recordset_to_getparametersres(recordset, False)
             msg_proto = ClientMessage(
                 get_parameters_res=serde.get_parameters_res_to_proto(getparamres)
             )
-        elif task_type == TASK_TYPE_FIT:
+        elif message_type == MESSAGE_TYPE_FIT:
             fitres = compat.recordset_to_fitres(recordset, False)
             msg_proto = ClientMessage(fit_res=serde.fit_res_to_proto(fitres))
-        elif task_type == TASK_TYPE_EVALUATE:
+        elif message_type == MESSAGE_TYPE_EVALUATE:
             evalres = compat.recordset_to_evaluateres(recordset)
             msg_proto = ClientMessage(evaluate_res=serde.evaluate_res_to_proto(evalres))
-        elif task_type == "reconnect":
+        elif message_type == "reconnect":
             reason = cast(Reason.ValueType, recordset.get_configs("config")["reason"])
             msg_proto = ClientMessage(
                 disconnect_res=ClientMessage.DisconnectRes(reason=reason)
             )
         else:
-            raise ValueError(f"Invalid task type: {task_type}")
+            raise ValueError(f"Invalid task type: {message_type}")
 
         # Send ClientMessage proto
         return queue.put(msg_proto, block=False)
diff --git a/src/py/flwr/client/grpc_client/connection_test.py b/src/py/flwr/client/grpc_client/connection_test.py
index e193c9484fff..127e27356f64 100644
--- a/src/py/flwr/client/grpc_client/connection_test.py
+++ b/src/py/flwr/client/grpc_client/connection_test.py
@@ -25,7 +25,7 @@
 
 from flwr.common import recordset_compat as compat
 from flwr.common.configsrecord import ConfigsRecord
-from flwr.common.constant import TASK_TYPE_GET_PROPERTIES
+from flwr.common.constant import MESSAGE_TYPE_GET_PROPERTIES
 from flwr.common.message import Message, Metadata
 from flwr.common.recordset import RecordSet
 from flwr.common.typing import Code, GetPropertiesRes, Status
@@ -46,11 +46,11 @@
 MESSAGE_GET_PROPERTIES = Message(
     metadata=Metadata(
         run_id=0,
-        task_id="",
+        message_id="",
         group_id="",
         node_id=0,
         ttl="",
-        task_type=TASK_TYPE_GET_PROPERTIES,
+        message_type=MESSAGE_TYPE_GET_PROPERTIES,
     ),
     content=compat.getpropertiesres_to_recordset(
         GetPropertiesRes(Status(Code.OK, ""), {})
@@ -59,11 +59,11 @@
 MESSAGE_DISCONNECT = Message(
     metadata=Metadata(
         run_id=0,
-        task_id="",
+        message_id="",
         group_id="",
         node_id=0,
         ttl="",
-        task_type="reconnect",
+        message_type="reconnect",
     ),
     content=RecordSet(configs={"config": ConfigsRecord({"reason": 0})}),
 )
@@ -134,7 +134,7 @@ def run_client() -> int:
                 message = receive()
 
                 messages_received += 1
-                if message.metadata.task_type == "reconnect":  # type: ignore
+                if message.metadata.message_type == "reconnect":  # type: ignore
                     send(MESSAGE_DISCONNECT)
                     break
 
diff --git a/src/py/flwr/client/message_handler/message_handler.py b/src/py/flwr/client/message_handler/message_handler.py
index 52dbdec7691b..93de7d7d8821 100644
--- a/src/py/flwr/client/message_handler/message_handler.py
+++ b/src/py/flwr/client/message_handler/message_handler.py
@@ -26,10 +26,10 @@
 from flwr.client.typing import ClientFn
 from flwr.common.configsrecord import ConfigsRecord
 from flwr.common.constant import (
-    TASK_TYPE_EVALUATE,
-    TASK_TYPE_FIT,
-    TASK_TYPE_GET_PARAMETERS,
-    TASK_TYPE_GET_PROPERTIES,
+    MESSAGE_TYPE_EVALUATE,
+    MESSAGE_TYPE_FIT,
+    MESSAGE_TYPE_GET_PARAMETERS,
+    MESSAGE_TYPE_GET_PROPERTIES,
 )
 from flwr.common.context import Context
 from flwr.common.message import Message, Metadata
@@ -75,7 +75,7 @@ def handle_control_message(message: Message) -> Tuple[Optional[Message], int]:
     sleep_duration : int
         Number of seconds that the client should disconnect from the server.
     """
-    if message.metadata.task_type == "reconnect":
+    if message.metadata.message_type == "reconnect":
         # Retrieve ReconnectIns from recordset
         recordset = message.content
         seconds = cast(int, recordset.get_configs("config")["seconds"])
@@ -90,11 +90,11 @@ def handle_control_message(message: Message) -> Tuple[Optional[Message], int]:
         out_message = Message(
             metadata=Metadata(
                 run_id=0,
-                task_id="",
+                message_id="",
                 group_id="",
                 node_id=0,
                 ttl="",
-                task_type="reconnect",
+                message_type="reconnect",
             ),
             content=recordset,
         )
@@ -105,7 +105,7 @@ def handle_control_message(message: Message) -> Tuple[Optional[Message], int]:
     return None, 0
 
 
-def handle_legacy_message_from_tasktype(
+def handle_legacy_message_from_msgtype(
     client_fn: ClientFn, message: Message, context: Context
 ) -> Message:
     """Handle legacy message in the inner most mod."""
@@ -113,17 +113,17 @@ def handle_legacy_message_from_tasktype(
 
     client.set_context(context)
 
-    task_type = message.metadata.task_type
+    message_type = message.metadata.message_type
 
     # Handle GetPropertiesIns
-    if task_type == TASK_TYPE_GET_PROPERTIES:
+    if message_type == MESSAGE_TYPE_GET_PROPERTIES:
         get_properties_res = maybe_call_get_properties(
             client=client,
             get_properties_ins=recordset_to_getpropertiesins(message.content),
         )
         out_recordset = getpropertiesres_to_recordset(get_properties_res)
     # Handle GetParametersIns
-    elif task_type == TASK_TYPE_GET_PARAMETERS:
+    elif message_type == MESSAGE_TYPE_GET_PARAMETERS:
         get_parameters_res = maybe_call_get_parameters(
             client=client,
             get_parameters_ins=recordset_to_getparametersins(message.content),
@@ -132,31 +132,31 @@ def handle_legacy_message_from_tasktype(
             get_parameters_res, keep_input=False
         )
     # Handle FitIns
-    elif task_type == TASK_TYPE_FIT:
+    elif message_type == MESSAGE_TYPE_FIT:
         fit_res = maybe_call_fit(
             client=client,
             fit_ins=recordset_to_fitins(message.content, keep_input=True),
         )
         out_recordset = fitres_to_recordset(fit_res, keep_input=False)
     # Handle EvaluateIns
-    elif task_type == TASK_TYPE_EVALUATE:
+    elif message_type == MESSAGE_TYPE_EVALUATE:
         evaluate_res = maybe_call_evaluate(
             client=client,
             evaluate_ins=recordset_to_evaluateins(message.content, keep_input=True),
         )
         out_recordset = evaluateres_to_recordset(evaluate_res)
     else:
-        raise ValueError(f"Invalid task type: {task_type}")
+        raise ValueError(f"Invalid task type: {message_type}")
 
     # Return Message
     out_message = Message(
         metadata=Metadata(
             run_id=0,
-            task_id="",
+            message_id="",
             group_id="",
             node_id=0,
             ttl="",
-            task_type=task_type,
+            message_type=message_type,
         ),
         content=out_recordset,
     )
diff --git a/src/py/flwr/client/message_handler/message_handler_test.py b/src/py/flwr/client/message_handler/message_handler_test.py
index 820de9bbe27c..c4c65d98b833 100644
--- a/src/py/flwr/client/message_handler/message_handler_test.py
+++ b/src/py/flwr/client/message_handler/message_handler_test.py
@@ -34,12 +34,12 @@
 )
 from flwr.common import recordset_compat as compat
 from flwr.common import typing
-from flwr.common.constant import TASK_TYPE_GET_PROPERTIES
+from flwr.common.constant import MESSAGE_TYPE_GET_PROPERTIES
 from flwr.common.context import Context
 from flwr.common.message import Message, Metadata
 from flwr.common.recordset import RecordSet
 
-from .message_handler import handle_legacy_message_from_tasktype
+from .message_handler import handle_legacy_message_from_msgtype
 
 
 class ClientWithoutProps(Client):
@@ -122,17 +122,17 @@ def test_client_without_get_properties() -> None:
     message = Message(
         metadata=Metadata(
             run_id=0,
-            task_id=str(uuid.uuid4()),
+            message_id=str(uuid.uuid4()),
             group_id="",
             node_id=0,
             ttl="",
-            task_type=TASK_TYPE_GET_PROPERTIES,
+            message_type=MESSAGE_TYPE_GET_PROPERTIES,
         ),
         content=recordset,
     )
 
     # Execute
-    actual_msg = handle_legacy_message_from_tasktype(
+    actual_msg = handle_legacy_message_from_msgtype(
         client_fn=_get_client_fn(client),
         message=message,
         context=Context(state=RecordSet()),
@@ -150,7 +150,7 @@ def test_client_without_get_properties() -> None:
     expected_msg = Message(message.metadata, expected_rs)
 
     assert actual_msg.content == expected_msg.content
-    assert actual_msg.metadata.task_type == expected_msg.metadata.task_type
+    assert actual_msg.metadata.message_type == expected_msg.metadata.message_type
 
 
 def test_client_with_get_properties() -> None:
@@ -161,17 +161,17 @@ def test_client_with_get_properties() -> None:
     message = Message(
         metadata=Metadata(
             run_id=0,
-            task_id=str(uuid.uuid4()),
+            message_id=str(uuid.uuid4()),
             group_id="",
             node_id=0,
             ttl="",
-            task_type=TASK_TYPE_GET_PROPERTIES,
+            message_type=MESSAGE_TYPE_GET_PROPERTIES,
         ),
         content=recordset,
     )
 
     # Execute
-    actual_msg = handle_legacy_message_from_tasktype(
+    actual_msg = handle_legacy_message_from_msgtype(
         client_fn=_get_client_fn(client),
         message=message,
         context=Context(state=RecordSet()),
@@ -189,4 +189,4 @@ def test_client_with_get_properties() -> None:
     expected_msg = Message(message.metadata, expected_rs)
 
     assert actual_msg.content == expected_msg.content
-    assert actual_msg.metadata.task_type == expected_msg.metadata.task_type
+    assert actual_msg.metadata.message_type == expected_msg.metadata.message_type
diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
index eba5dec658aa..fa5a9fd24109 100644
--- a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
@@ -24,7 +24,7 @@
 from flwr.common import ndarray_to_bytes, parameters_to_ndarrays
 from flwr.common import recordset_compat as compat
 from flwr.common.configsrecord import ConfigsRecord
-from flwr.common.constant import TASK_TYPE_FIT
+from flwr.common.constant import MESSAGE_TYPE_FIT
 from flwr.common.context import Context
 from flwr.common.logger import log
 from flwr.common.message import Message, Metadata
@@ -168,7 +168,7 @@ def secaggplus_mod(
 ) -> Message:
     """Handle incoming message and return results, following the SecAgg+ protocol."""
     # Ignore non-fit messages
-    if msg.metadata.task_type != TASK_TYPE_FIT:
+    if msg.metadata.message_type != MESSAGE_TYPE_FIT:
         return call_next(msg, ctxt)
 
     # Retrieve local state
@@ -209,11 +209,11 @@ def secaggplus_mod(
     return Message(
         metadata=Metadata(
             run_id=0,
-            task_id="",
+            message_id="",
             group_id="",
             node_id=0,
             ttl="",
-            task_type=TASK_TYPE_FIT,
+            message_type=MESSAGE_TYPE_FIT,
         ),
         content=RecordSet(configs={RECORD_KEY_CONFIGS: ConfigsRecord(res, False)}),
     )
diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
index d0f461e7a8de..4033306d0845 100644
--- a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
@@ -20,7 +20,7 @@
 
 from flwr.client.mod import make_ffn
 from flwr.common.configsrecord import ConfigsRecord
-from flwr.common.constant import TASK_TYPE_FIT
+from flwr.common.constant import MESSAGE_TYPE_FIT
 from flwr.common.context import Context
 from flwr.common.message import Message, Metadata
 from flwr.common.recordset import RecordSet
@@ -59,11 +59,11 @@ def empty_ffn(_: Message, _2: Context) -> Message:
         return Message(
             metadata=Metadata(
                 run_id=0,
-                task_id="",
+                message_id="",
                 group_id="",
                 node_id=0,
                 ttl="",
-                task_type=TASK_TYPE_FIT,
+                message_type=MESSAGE_TYPE_FIT,
             ),
             content=RecordSet(),
         )
@@ -74,11 +74,11 @@ def func(configs: Dict[str, ConfigsRecordValues]) -> Dict[str, ConfigsRecordValu
         in_msg = Message(
             metadata=Metadata(
                 run_id=0,
-                task_id="",
+                message_id="",
                 group_id="",
                 node_id=0,
                 ttl="",
-                task_type=TASK_TYPE_FIT,
+                message_type=MESSAGE_TYPE_FIT,
             ),
             content=RecordSet(configs={RECORD_KEY_CONFIGS: ConfigsRecord(configs)}),
         )
diff --git a/src/py/flwr/client/mod/utils_test.py b/src/py/flwr/client/mod/utils_test.py
index 07b38a250d6c..4a086d9ae3f7 100644
--- a/src/py/flwr/client/mod/utils_test.py
+++ b/src/py/flwr/client/mod/utils_test.py
@@ -75,7 +75,7 @@ def _get_dummy_flower_message() -> Message:
     return Message(
         content=RecordSet(),
         metadata=Metadata(
-            run_id=0, task_id="", group_id="", node_id=0, ttl="", task_type="mock"
+            run_id=0, message_id="", group_id="", node_id=0, ttl="", message_type="mock"
         ),
     )
 
diff --git a/src/py/flwr/common/constant.py b/src/py/flwr/common/constant.py
index 8d1d865f084b..fcafd853a349 100644
--- a/src/py/flwr/common/constant.py
+++ b/src/py/flwr/common/constant.py
@@ -32,7 +32,7 @@
     TRANSPORT_TYPE_REST,
 ]
 
-TASK_TYPE_GET_PROPERTIES = "get_properties"
-TASK_TYPE_GET_PARAMETERS = "get_parameters"
-TASK_TYPE_FIT = "fit"
-TASK_TYPE_EVALUATE = "evaluate"
+MESSAGE_TYPE_GET_PROPERTIES = "get_properties"
+MESSAGE_TYPE_GET_PARAMETERS = "get_parameters"
+MESSAGE_TYPE_FIT = "fit"
+MESSAGE_TYPE_EVALUATE = "evaluate"
diff --git a/src/py/flwr/common/message.py b/src/py/flwr/common/message.py
index ada4a617b60d..9258edccbcd5 100644
--- a/src/py/flwr/common/message.py
+++ b/src/py/flwr/common/message.py
@@ -22,32 +22,32 @@
 
 @dataclass
 class Metadata:
-    """A dataclass holding metadata associated with the current task.
+    """A dataclass holding metadata associated with the current message.
 
     Parameters
     ----------
     run_id : int
         An identifier for the current run.
-    task_id : str
-        An identifier for the current task.
+    message_id : str
+        An identifier for the current message.
     group_id : str
-        An identifier for grouping tasks. In some settings
+        An identifier for grouping messages. In some settings
         this is used as the FL round.
     node_id : int
-        An identifier for the node running a task.
+        An identifier for the node running a message.
     ttl : str
-        Time-to-live for this task.
-    task_type : str
+        Time-to-live for this message.
+    message_type : str
         A string that encodes the action to be executed on
         the receiving end.
     """
 
     run_id: int
-    task_id: str
+    message_id: str
     group_id: str
     node_id: int
     ttl: str
-    task_type: str
+    message_type: str
 
 
 @dataclass
@@ -57,7 +57,7 @@ class Message:
     Parameters
     ----------
     metadata : Metadata
-        A dataclass including information about the task to be executed.
+        A dataclass including information about the message to be executed.
     content : RecordSet
         Holds records either sent by another entity (e.g. sent by the server-side
         logic to a client, or vice-versa) or that will be sent to it.
diff --git a/src/py/flwr/common/serde.py b/src/py/flwr/common/serde.py
index 5a8c5c753136..2808cb88fb5c 100644
--- a/src/py/flwr/common/serde.py
+++ b/src/py/flwr/common/serde.py
@@ -550,7 +550,7 @@ def message_to_taskins(message: Message) -> TaskIns:
     return TaskIns(
         task=Task(
             ttl=message.metadata.ttl,
-            task_type=message.metadata.task_type,
+            task_type=message.metadata.message_type,
             recordset=recordset_to_proto(message.content),
         ),
     )
@@ -561,11 +561,11 @@ def message_from_taskins(taskins: TaskIns) -> Message:
     # Retrieve the Metadata
     metadata = Metadata(
         run_id=taskins.run_id,
-        task_id=taskins.task_id,
+        message_id=taskins.task_id,
         group_id=taskins.group_id,
         node_id=taskins.task.consumer.node_id,
         ttl=taskins.task.ttl,
-        task_type=taskins.task.task_type,
+        message_type=taskins.task.task_type,
     )
 
     # Return the Message
@@ -580,7 +580,7 @@ def message_to_taskres(message: Message) -> TaskRes:
     return TaskRes(
         task=Task(
             ttl=message.metadata.ttl,
-            task_type=message.metadata.task_type,
+            task_type=message.metadata.message_type,
             recordset=recordset_to_proto(message.content),
         ),
     )
@@ -591,11 +591,11 @@ def message_from_taskres(taskres: TaskRes) -> Message:
     # Retrieve the MetaData
     metadata = Metadata(
         run_id=taskres.run_id,
-        task_id=taskres.task_id,
+        message_id=taskres.task_id,
         group_id=taskres.group_id,
         node_id=taskres.task.consumer.node_id,
         ttl=taskres.task.ttl,
-        task_type=taskres.task.task_type,
+        message_type=taskres.task.task_type,
     )
 
     # Return the Message
diff --git a/src/py/flwr/common/serde_test.py b/src/py/flwr/common/serde_test.py
index 1c36f2171149..44085e8d9ab8 100644
--- a/src/py/flwr/common/serde_test.py
+++ b/src/py/flwr/common/serde_test.py
@@ -217,11 +217,11 @@ def metadata(self) -> Metadata:
         """Create a Metadata."""
         return Metadata(
             run_id=self.rng.randint(0, 1 << 30),
-            task_id=self.get_str(64),
+            message_id=self.get_str(64),
             group_id=self.get_str(30),
             node_id=self.rng.randint(0, 1 << 63),
             ttl=self.get_str(10),
-            task_type=self.get_str(10),
+            message_type=self.get_str(10),
         )
 
 
@@ -308,11 +308,11 @@ def test_message_to_and_from_taskins() -> None:
     original = Message(
         metadata=Metadata(
             run_id=0,
-            task_id="",
+            message_id="",
             group_id="",
             node_id=metadata.node_id,
             ttl=metadata.ttl,
-            task_type=metadata.task_type,
+            message_type=metadata.message_type,
         ),
         content=maker.recordset(1, 1, 1),
     )
@@ -320,7 +320,7 @@ def test_message_to_and_from_taskins() -> None:
     # Execute
     taskins = message_to_taskins(original)
     taskins.run_id = metadata.run_id
-    taskins.task_id = metadata.task_id
+    taskins.task_id = metadata.message_id
     taskins.group_id = metadata.group_id
     taskins.task.consumer.node_id = metadata.node_id
     deserialized = message_from_taskins(taskins)
@@ -338,11 +338,11 @@ def test_message_to_and_from_taskres() -> None:
     original = Message(
         metadata=Metadata(
             run_id=0,
-            task_id="",
+            message_id="",
             group_id="",
             node_id=metadata.node_id,
             ttl=metadata.ttl,
-            task_type=metadata.task_type,
+            message_type=metadata.message_type,
         ),
         content=maker.recordset(1, 1, 1),
     )
@@ -350,7 +350,7 @@ def test_message_to_and_from_taskres() -> None:
     # Execute
     taskres = message_to_taskres(original)
     taskres.run_id = metadata.run_id
-    taskres.task_id = metadata.task_id
+    taskres.task_id = metadata.message_id
     taskres.group_id = metadata.group_id
     taskres.task.consumer.node_id = metadata.node_id
     deserialized = message_from_taskres(taskres)
diff --git a/src/py/flwr/server/driver/driver_client_proxy.py b/src/py/flwr/server/driver/driver_client_proxy.py
index e0ff26c035f7..8ea288dbb50c 100644
--- a/src/py/flwr/server/driver/driver_client_proxy.py
+++ b/src/py/flwr/server/driver/driver_client_proxy.py
@@ -22,10 +22,10 @@
 from flwr.common import recordset_compat as compat
 from flwr.common import serde
 from flwr.common.constant import (
-    TASK_TYPE_EVALUATE,
-    TASK_TYPE_FIT,
-    TASK_TYPE_GET_PARAMETERS,
-    TASK_TYPE_GET_PROPERTIES,
+    MESSAGE_TYPE_EVALUATE,
+    MESSAGE_TYPE_FIT,
+    MESSAGE_TYPE_GET_PARAMETERS,
+    MESSAGE_TYPE_GET_PROPERTIES,
 )
 from flwr.common.recordset import RecordSet
 from flwr.proto import driver_pb2, node_pb2, task_pb2  # pylint: disable=E0611
@@ -54,7 +54,7 @@ def get_properties(
         out_recordset = compat.getpropertiesins_to_recordset(ins)
         # Fetch response
         in_recordset = self._send_receive_recordset(
-            out_recordset, TASK_TYPE_GET_PROPERTIES, timeout
+            out_recordset, MESSAGE_TYPE_GET_PROPERTIES, timeout
         )
         # RecordSet to Res
         return compat.recordset_to_getpropertiesres(in_recordset)
@@ -67,7 +67,7 @@ def get_parameters(
         out_recordset = compat.getparametersins_to_recordset(ins)
         # Fetch response
         in_recordset = self._send_receive_recordset(
-            out_recordset, TASK_TYPE_GET_PARAMETERS, timeout
+            out_recordset, MESSAGE_TYPE_GET_PARAMETERS, timeout
         )
         # RecordSet to Res
         return compat.recordset_to_getparametersres(in_recordset, False)
@@ -78,7 +78,7 @@ def fit(self, ins: common.FitIns, timeout: Optional[float]) -> common.FitRes:
         out_recordset = compat.fitins_to_recordset(ins, keep_input=True)
         # Fetch response
         in_recordset = self._send_receive_recordset(
-            out_recordset, TASK_TYPE_FIT, timeout
+            out_recordset, MESSAGE_TYPE_FIT, timeout
         )
         # RecordSet to Res
         return compat.recordset_to_fitres(in_recordset, keep_input=False)
@@ -91,7 +91,7 @@ def evaluate(
         out_recordset = compat.evaluateins_to_recordset(ins, keep_input=True)
         # Fetch response
         in_recordset = self._send_receive_recordset(
-            out_recordset, TASK_TYPE_EVALUATE, timeout
+            out_recordset, MESSAGE_TYPE_EVALUATE, timeout
         )
         # RecordSet to Res
         return compat.recordset_to_evaluateres(in_recordset)
diff --git a/src/py/flwr/server/driver/driver_client_proxy_test.py b/src/py/flwr/server/driver/driver_client_proxy_test.py
index 18277a7ce80c..aa60448bd72e 100644
--- a/src/py/flwr/server/driver/driver_client_proxy_test.py
+++ b/src/py/flwr/server/driver/driver_client_proxy_test.py
@@ -25,10 +25,10 @@
 from flwr.common import recordset_compat as compat
 from flwr.common import serde
 from flwr.common.constant import (
-    TASK_TYPE_EVALUATE,
-    TASK_TYPE_FIT,
-    TASK_TYPE_GET_PARAMETERS,
-    TASK_TYPE_GET_PROPERTIES,
+    MESSAGE_TYPE_EVALUATE,
+    MESSAGE_TYPE_FIT,
+    MESSAGE_TYPE_GET_PARAMETERS,
+    MESSAGE_TYPE_GET_PROPERTIES,
 )
 from flwr.common.typing import (
     Code,
@@ -56,21 +56,21 @@ def _make_task(
     res: Union[GetParametersRes, GetPropertiesRes, FitRes, EvaluateRes]
 ) -> task_pb2.Task:  # pylint: disable=E1101
     if isinstance(res, GetParametersRes):
-        task_type = TASK_TYPE_GET_PARAMETERS
+        message_type = MESSAGE_TYPE_GET_PARAMETERS
         recordset = compat.getparametersres_to_recordset(res, True)
     elif isinstance(res, GetPropertiesRes):
-        task_type = TASK_TYPE_GET_PROPERTIES
+        message_type = MESSAGE_TYPE_GET_PROPERTIES
         recordset = compat.getpropertiesres_to_recordset(res)
     elif isinstance(res, FitRes):
-        task_type = TASK_TYPE_FIT
+        message_type = MESSAGE_TYPE_FIT
         recordset = compat.fitres_to_recordset(res, True)
     elif isinstance(res, EvaluateRes):
-        task_type = TASK_TYPE_EVALUATE
+        message_type = MESSAGE_TYPE_EVALUATE
         recordset = compat.evaluateres_to_recordset(res)
     else:
         raise ValueError(f"Unsupported type: {type(res)}")
     return task_pb2.Task(  # pylint: disable=E1101
-        task_type=task_type,
+        task_type=message_type,
         recordset=serde.recordset_to_proto(recordset),
     )
 

From 06295891c635197ca6629af9518eb4b9500147a2 Mon Sep 17 00:00:00 2001
From: Robert Steiner <robert@flower.dev>
Date: Thu, 15 Feb 2024 11:33:19 +0100
Subject: [PATCH 008/102] Update URLs in READMEs (#2949)

---
 README.md                                     | 50 +++++------
 baselines/README.md                           |  6 +-
 baselines/fedpara/README.md                   | 84 +++++++++----------
 datasets/README.md                            | 16 ++--
 examples/advanced-pytorch/README.md           |  2 +-
 examples/advanced-tensorflow/README.md        |  2 +-
 examples/custom-metrics/README.md             | 12 +--
 examples/flower-via-docker-compose/README.md  |  4 +-
 .../README.md                                 |  2 +-
 examples/quickstart-huggingface/README.md     |  2 +-
 examples/quickstart-pandas/README.md          |  4 +-
 .../quickstart-pytorch-lightning/README.md    |  2 +-
 examples/quickstart-pytorch/README.md         |  2 +-
 examples/quickstart-sklearn-tabular/README.md |  2 +-
 examples/quickstart-tensorflow/README.md      |  2 +-
 examples/simulation-pytorch/README.md         |  4 +-
 examples/simulation-tensorflow/README.md      |  4 +-
 examples/sklearn-logreg-mnist/README.md       |  2 +-
 .../whisper-federated-finetuning/README.md    |  4 +-
 examples/xgboost-comprehensive/README.md      |  6 +-
 examples/xgboost-quickstart/README.md         |  2 +-
 21 files changed, 107 insertions(+), 107 deletions(-)

diff --git a/README.md b/README.md
index e4433e517b88..38a11d951fe7 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,16 @@
 # Flower: A Friendly Federated Learning Framework
 
 <p align="center">
-  <a href="https://flower.dev/">
-    <img src="https://flower.dev/_next/image/?url=%2F_next%2Fstatic%2Fmedia%2Fflower_white_border.c2012e70.png&w=640&q=75" width="140px" alt="Flower Website" />
+  <a href="https://flower.ai/">
+    <img src="https://flower.ai/_next/image/?url=%2F_next%2Fstatic%2Fmedia%2Fflower_white_border.c2012e70.png&w=640&q=75" width="140px" alt="Flower Website" />
   </a>
 </p>
 <p align="center">
-    <a href="https://flower.dev/">Website</a> |
-    <a href="https://flower.dev/blog">Blog</a> |
-    <a href="https://flower.dev/docs/">Docs</a> |
-    <a href="https://flower.dev/conf/flower-summit-2022">Conference</a> |
-    <a href="https://flower.dev/join-slack">Slack</a>
+    <a href="https://flower.ai/">Website</a> |
+    <a href="https://flower.ai/blog">Blog</a> |
+    <a href="https://flower.ai/docs/">Docs</a> |
+    <a href="https://flower.ai/conf/flower-summit-2022">Conference</a> |
+    <a href="https://flower.ai/join-slack">Slack</a>
     <br /><br />
 </p>
 
@@ -18,7 +18,7 @@
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/adap/flower/blob/main/CONTRIBUTING.md)
 ![Build](https://github.com/adap/flower/actions/workflows/framework.yml/badge.svg)
 [![Downloads](https://static.pepy.tech/badge/flwr)](https://pepy.tech/project/flwr)
-[![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.dev/join-slack)
+[![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.ai/join-slack)
 
 Flower (`flwr`) is a framework for building federated learning systems. The
 design of Flower is based on a few guiding principles:
@@ -39,7 +39,7 @@ design of Flower is based on a few guiding principles:
 - **Understandable**: Flower is written with maintainability in mind. The
   community is encouraged to both read and contribute to the codebase.
 
-Meet the Flower community on [flower.dev](https://flower.dev)!
+Meet the Flower community on [flower.ai](https://flower.ai)!
 
 ## Federated Learning Tutorial
 
@@ -73,19 +73,19 @@ Stay tuned, more tutorials are coming soon. Topics include **Privacy and Securit
 
 ## Documentation
 
-[Flower Docs](https://flower.dev/docs):
+[Flower Docs](https://flower.ai/docs):
 
-- [Installation](https://flower.dev/docs/framework/how-to-install-flower.html)
-- [Quickstart (TensorFlow)](https://flower.dev/docs/framework/tutorial-quickstart-tensorflow.html)
-- [Quickstart (PyTorch)](https://flower.dev/docs/framework/tutorial-quickstart-pytorch.html)
-- [Quickstart (Hugging Face)](https://flower.dev/docs/framework/tutorial-quickstart-huggingface.html)
-- [Quickstart (PyTorch Lightning)](https://flower.dev/docs/framework/tutorial-quickstart-pytorch-lightning.html)
-- [Quickstart (Pandas)](https://flower.dev/docs/framework/tutorial-quickstart-pandas.html)
-- [Quickstart (fastai)](https://flower.dev/docs/framework/tutorial-quickstart-fastai.html)
-- [Quickstart (JAX)](https://flower.dev/docs/framework/tutorial-quickstart-jax.html)
-- [Quickstart (scikit-learn)](https://flower.dev/docs/framework/tutorial-quickstart-scikitlearn.html)
-- [Quickstart (Android [TFLite])](https://flower.dev/docs/framework/tutorial-quickstart-android.html)
-- [Quickstart (iOS [CoreML])](https://flower.dev/docs/framework/tutorial-quickstart-ios.html)
+- [Installation](https://flower.ai/docs/framework/how-to-install-flower.html)
+- [Quickstart (TensorFlow)](https://flower.ai/docs/framework/tutorial-quickstart-tensorflow.html)
+- [Quickstart (PyTorch)](https://flower.ai/docs/framework/tutorial-quickstart-pytorch.html)
+- [Quickstart (Hugging Face)](https://flower.ai/docs/framework/tutorial-quickstart-huggingface.html)
+- [Quickstart (PyTorch Lightning)](https://flower.ai/docs/framework/tutorial-quickstart-pytorch-lightning.html)
+- [Quickstart (Pandas)](https://flower.ai/docs/framework/tutorial-quickstart-pandas.html)
+- [Quickstart (fastai)](https://flower.ai/docs/framework/tutorial-quickstart-fastai.html)
+- [Quickstart (JAX)](https://flower.ai/docs/framework/tutorial-quickstart-jax.html)
+- [Quickstart (scikit-learn)](https://flower.ai/docs/framework/tutorial-quickstart-scikitlearn.html)
+- [Quickstart (Android [TFLite])](https://flower.ai/docs/framework/tutorial-quickstart-android.html)
+- [Quickstart (iOS [CoreML])](https://flower.ai/docs/framework/tutorial-quickstart-ios.html)
 
 ## Flower Baselines
 
@@ -112,9 +112,9 @@ Flower Baselines is a collection of community-contributed projects that reproduc
 - [FedAvg](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist)
 - [FedOpt](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization)
 
-Please refer to the [Flower Baselines Documentation](https://flower.dev/docs/baselines/) for a detailed categorization of baselines and for additional info including:
-* [How to use Flower Baselines](https://flower.dev/docs/baselines/how-to-use-baselines.html)
-* [How to contribute a new Flower Baseline](https://flower.dev/docs/baselines/how-to-contribute-baselines.html)
+Please refer to the [Flower Baselines Documentation](https://flower.ai/docs/baselines/) for a detailed categorization of baselines and for additional info including:
+* [How to use Flower Baselines](https://flower.ai/docs/baselines/how-to-use-baselines.html)
+* [How to contribute a new Flower Baseline](https://flower.ai/docs/baselines/how-to-contribute-baselines.html)
 
 ## Flower Usage Examples
 
@@ -151,7 +151,7 @@ Other [examples](https://github.com/adap/flower/tree/main/examples):
 
 ## Community
 
-Flower is built by a wonderful community of researchers and engineers. [Join Slack](https://flower.dev/join-slack) to meet them, [contributions](#contributing-to-flower) are welcome.
+Flower is built by a wonderful community of researchers and engineers. [Join Slack](https://flower.ai/join-slack) to meet them, [contributions](#contributing-to-flower) are welcome.
 
 <a href="https://github.com/adap/flower/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=adap/flower" />
diff --git a/baselines/README.md b/baselines/README.md
index a18c0553b2b4..3a84df02d8de 100644
--- a/baselines/README.md
+++ b/baselines/README.md
@@ -1,7 +1,7 @@
 # Flower Baselines
 
 
-> We are changing the way we structure the Flower baselines. While we complete the transition to the new format, you can still find the existing baselines in the `flwr_baselines` directory. Currently, you can make use of baselines for [FedAvg](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist), [FedOpt](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization), and [LEAF-FEMNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist). 
+> We are changing the way we structure the Flower baselines. While we complete the transition to the new format, you can still find the existing baselines in the `flwr_baselines` directory. Currently, you can make use of baselines for [FedAvg](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist), [FedOpt](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization), and [LEAF-FEMNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist).
 
 > The documentation below has been updated to reflect the new way of using Flower baselines.
 
@@ -23,7 +23,7 @@ Please note that some baselines might include additional files (e.g. a `requirem
 
 ## Running the baselines
 
-Each baseline is self-contained in its own directory. Furthermore, each baseline defines its own Python environment using [Poetry](https://python-poetry.org/docs/) via a `pyproject.toml` file and  [`pyenv`](https://github.com/pyenv/pyenv). If you haven't setup `Poetry` and `pyenv` already on your machine, please take a look at the [Documentation](https://flower.dev/docs/baselines/how-to-use-baselines.html#setting-up-your-machine) for a guide on how to do so. 
+Each baseline is self-contained in its own directory. Furthermore, each baseline defines its own Python environment using [Poetry](https://python-poetry.org/docs/) via a `pyproject.toml` file and  [`pyenv`](https://github.com/pyenv/pyenv). If you haven't setup `Poetry` and `pyenv` already on your machine, please take a look at the [Documentation](https://flower.ai/docs/baselines/how-to-use-baselines.html#setting-up-your-machine) for a guide on how to do so.
 
 Assuming `pyenv` and `Poetry` are already installed on your system. Running a baseline can be done by:
 
@@ -54,7 +54,7 @@ The steps to follow are:
     ```bash
     # This will create a new directory with the same structure as `baseline_template`.
     ./dev/create-baseline.sh <baseline-name>
-    ``` 
+    ```
 3. Then, go inside your baseline directory and continue with the steps detailed in `EXTENDED_README.md` and `README.md`.
 4. Once your code is ready and you have checked that following the instructions in your `README.md` the Python environment can be created correctly and that running the code following your instructions can reproduce the experiments in the paper, you just need to create a Pull Request (PR). Then, the process to merge your baseline into the Flower repo will begin!
 
diff --git a/baselines/fedpara/README.md b/baselines/fedpara/README.md
index 068366aa261c..82efe5fac537 100644
--- a/baselines/fedpara/README.md
+++ b/baselines/fedpara/README.md
@@ -5,7 +5,7 @@ labels: [image classification, personalization, low-rank training, tensor decomp
 dataset: [CIFAR-10, CIFAR-100, MNIST]
 ---
 
-# FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning 
+# FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning
 
 > Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper.
 
@@ -43,7 +43,7 @@ Specifically, it replicates the results for CIFAR-10  and CIFAR-100  in Figure 3
 On a machine with RTX 3090Ti (24GB VRAM) it takes approximately 1h to run each CIFAR-10/100 experiment while using < 12GB of VRAM. You can lower the VRAM footprint my reducing the number of clients allowed to run in parallel in your GPU (do this by raising `client_resources.num_gpus`).
 
 
-**Contributors:** Yahia Salaheldin Shaaban, Omar Mokhtar and Roeia Amr 
+**Contributors:** Yahia Salaheldin Shaaban, Omar Mokhtar and Roeia Amr
 
 
 ## Experimental Setup
@@ -52,48 +52,48 @@ On a machine with RTX 3090Ti (24GB VRAM) it takes approximately 1h to run each C
 
 **Model:**  This baseline implements VGG16 with group normalization.
 
-**Dataset:** 
+**Dataset:**
 
-| Dataset  | #classes | #partitions |  partitioning method IID  | partitioning method non-IID  |
-|:---------|:--------:|:-----------:|:----------------------:| :----------------------:|
-| CIFAR-10  |    10    |     100     | random split | Dirichlet distribution ($\alpha=0.5$)|
-| CIFAR-100  |   100    |     50     | random split| Dirichlet distribution ($\alpha=0.5$)|
+| Dataset   | #classes | #partitions | partitioning method IID |      partitioning method non-IID      |
+| :-------- | :------: | :---------: | :---------------------: | :-----------------------------------: |
+| CIFAR-10  |    10    |     100     |      random split       | Dirichlet distribution ($\alpha=0.5$) |
+| CIFAR-100 |   100    |     50      |      random split       | Dirichlet distribution ($\alpha=0.5$) |
 
 
 **Training Hyperparameters:**
 
-|   |   Cifar10 IID   | Cifar10 Non-IID      | Cifar100 IID     | Cifar100 Non-IID      | MNIST        |
-|---|-------|-------|------|-------|----------|
-| Fraction of client (K) | 16    | 16    | 8    | 8     | 10       |
-| Total rounds (T) | 200   | 200   | 400  | 400   | 100      |
-| Number of SGD epochs (E) | 10    | 5     | 10   | 5     | 5       |
-| Batch size (B) | 64    | 64    | 64   | 64    | 10       |
-| Initial learning rate (η) | 0.1   | 0.1   | 0.1  | 0.1   | 0.1-0.01      |
-| Learning rate decay (τ) | 0.992 | 0.992 | 0.992| 0.992 | 0.999    |
-| Regularization coefficient (λ) | 1     | 1     | 1    | 1     | 0        |
+|                                | Cifar10 IID | Cifar10 Non-IID | Cifar100 IID | Cifar100 Non-IID | MNIST    |
+| ------------------------------ | ----------- | --------------- | ------------ | ---------------- | -------- |
+| Fraction of client (K)         | 16          | 16              | 8            | 8                | 10       |
+| Total rounds (T)               | 200         | 200             | 400          | 400              | 100      |
+| Number of SGD epochs (E)       | 10          | 5               | 10           | 5                | 5        |
+| Batch size (B)                 | 64          | 64              | 64           | 64               | 10       |
+| Initial learning rate (η)      | 0.1         | 0.1             | 0.1          | 0.1              | 0.1-0.01 |
+| Learning rate decay (τ)        | 0.992       | 0.992           | 0.992        | 0.992            | 0.999    |
+| Regularization coefficient (λ) | 1           | 1               | 1            | 1                | 0        |
 
 As for the parameters ratio ($\gamma$) we use the following model sizes. As in the paper, $\gamma=0.1$ is used for CIFAR-10 and $\gamma=0.4$ for CIFAR-100:
 
 | Parameters ratio ($\gamma$) | CIFAR-10 | CIFAR-100 |
-|----------|--------|--------|
-| 1.0 (original) | 15.25M | 15.30M |
-| 0.1      | 1.55M  | - |
-| 0.4      | - | 4.53M  |
+| --------------------------- | -------- | --------- |
+| 1.0 (original)              | 15.25M   | 15.30M    |
+| 0.1                         | 1.55M    | -         |
+| 0.4                         | -        | 4.53M     |
 
 
-### Notes: 
+### Notes:
 - Notably, Fedpara's low-rank training technique heavily relies on initialization, with our experiments revealing that employing a 'Fan-in' He initialization (or Kaiming) renders the model incapable of convergence, resulting in a performance akin to that of a random classifier. We found that only Fan-out initialization yielded the anticipated results, and we postulated that this is attributed to the variance conservation during backward propagation.
 
 - The paper lacks explicit guidance on calculating the rank, aside from the "Rank_min - Rank_max" equation. To address this, we devised an equation aligning with the literature's explanation and constraint, solving a quadratic equation to determine max_rank and utilizing proposition 2 from the paper to establish min_rank.
 
 - The Jacobian correction was not incorporated into our implementation, primarily due to the lack of explicit instructions in the paper regarding the specific implementation of the dual update principle mentioned in the Jacobian correction section.
 
-- It was observed that data generation is crutial for model convergence 
+- It was observed that data generation is crutial for model convergence
 
 ## Environment Setup
 To construct the Python environment follow these steps:
 
-It is assumed that `pyenv` is installed, `poetry` is installed and python 3.10.6 is installed using `pyenv`. Refer to this [documentation](https://flower.dev/docs/baselines/how-to-usef-baselines.html#setting-up-your-machine) to ensure that your machine is ready.
+It is assumed that `pyenv` is installed, `poetry` is installed and python 3.10.6 is installed using `pyenv`. Refer to this [documentation](https://flower.ai/docs/baselines/how-to-usef-baselines.html#setting-up-your-machine) to ensure that your machine is ready.
 
 ```bash
 # Set Python 3.10
@@ -112,7 +112,7 @@ poetry shell
 
 Running `FedPara` is easy. You can run it with default parameters directly or by tweaking them directly on the command line. Some command examples are shown below.
 
-```bash  
+```bash
 # To run fedpara with default parameters
 python -m fedpara.main
 
@@ -138,45 +138,45 @@ To reproduce the curves shown below (which correspond to those in Figure 3 in th
 
 ```bash
 # To run fedpara for non-iid CIFAR-10 on vgg16 for lowrank and original schemes
-python -m fedpara.main --multirun model.param_type=standard,lowrank 
+python -m fedpara.main --multirun model.param_type=standard,lowrank
 # To run fedpara for non-iid CIFAR-100 on vgg16 for lowrank and original schemes
-python -m fedpara.main --config-name cifar100 --multirun model.param_type=standard,lowrank 
+python -m fedpara.main --config-name cifar100 --multirun model.param_type=standard,lowrank
 # To run fedpara for iid CIFAR-10 on vgg16 for lowrank and original schemes
-python -m fedpara.main --multirun model.param_type=standard,lowrank num_epochs=10 dataset_config.partition=iid 
+python -m fedpara.main --multirun model.param_type=standard,lowrank num_epochs=10 dataset_config.partition=iid
 # To run fedpara for iid CIFAR-100 on vgg16 for lowrank and original schemes
 python -m fedpara.main --config-name cifar100 --multirun model.param_type=standard,lowrank num_epochs=10 dataset_config.partition=iid
-# To run fedavg for non-iid MINST on FC 
-python -m fedpara.main --config-name mnist_fedavg 
-# To run fedper for non-iid MINST on FC 
-python -m fedpara.main --config-name mnist_fedper 
-# To run pfedpara for non-iid MINST on FC 
-python -m fedpara.main --config-name mnist_pfedpara 
+# To run fedavg for non-iid MINST on FC
+python -m fedpara.main --config-name mnist_fedavg
+# To run fedper for non-iid MINST on FC
+python -m fedpara.main --config-name mnist_fedper
+# To run pfedpara for non-iid MINST on FC
+python -m fedpara.main --config-name mnist_pfedpara
 ```
 
-#### Communication Cost: 
-Communication costs as measured as described in the paper: 
+#### Communication Cost:
+Communication costs as measured as described in the paper:
 *"FL evaluation typically measures the required rounds to achieve the target accuracy as communication costs, but we instead assess total transferred bit sizes, 2 ×
 (#participants)×(model size)×(#rounds)"*
 
 
 ### CIFAR-100 (Accuracy vs Communication Cost)
 
-| IID | Non-IID |
-|:----:|:----:|
-|![Cifar100 iid](_static/Cifar100_iid.jpeg) | ![Cifar100 non-iid](_static/Cifar100_noniid.jpeg) |
+|                    IID                     |                      Non-IID                      |
+| :----------------------------------------: | :-----------------------------------------------: |
+| ![Cifar100 iid](_static/Cifar100_iid.jpeg) | ![Cifar100 non-iid](_static/Cifar100_noniid.jpeg) |
 
 
 ### CIFAR-10 (Accuracy vs Communication Cost)
 
-| IID | Non-IID |
-|:----:|:----:|
-|![CIFAR10 iid](_static/Cifar10_iid.jpeg) | ![CIFAR10 non-iid](_static/Cifar10_noniid.jpeg) |
+|                   IID                    |                     Non-IID                     |
+| :--------------------------------------: | :---------------------------------------------: |
+| ![CIFAR10 iid](_static/Cifar10_iid.jpeg) | ![CIFAR10 non-iid](_static/Cifar10_noniid.jpeg) |
 
 ### NON-IID MINST (FedAvg vs FedPer vs pFedPara)
 
 The only federated averaging (FedAvg) implementation replicates the results outlined in the paper. However, challenges with convergence were encountered when applying `pFedPara` and `FedPer` methods.
 
-![Personalization algorithms](_static/non-iid_mnist_personalization.png) 
+![Personalization algorithms](_static/non-iid_mnist_personalization.png)
 
 ## Code Acknowledgments
 Our code is inspired from these repos:
diff --git a/datasets/README.md b/datasets/README.md
index 876b6f453fa5..61292fe988bf 100644
--- a/datasets/README.md
+++ b/datasets/README.md
@@ -4,9 +4,9 @@
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/adap/flower/blob/main/CONTRIBUTING.md)
 ![Build](https://github.com/adap/flower/actions/workflows/framework.yml/badge.svg)
 ![Downloads](https://pepy.tech/badge/flwr-datasets)
-[![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.dev/join-slack)
+[![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.ai/join-slack)
 
-Flower Datasets (`flwr-datasets`) is a library to quickly and easily create datasets for federated learning, federated evaluation, and federated analytics. It was created by the `Flower Labs` team that also created Flower: A Friendly Federated Learning Framework. 
+Flower Datasets (`flwr-datasets`) is a library to quickly and easily create datasets for federated learning, federated evaluation, and federated analytics. It was created by the `Flower Labs` team that also created Flower: A Friendly Federated Learning Framework.
 Flower Datasets library supports:
 * **downloading datasets** - choose the dataset from Hugging Face's `datasets`,
 * **partitioning datasets** - customize the partitioning scheme,
@@ -14,10 +14,10 @@ Flower Datasets library supports:
 
 Thanks to using Hugging Face's `datasets` used under the hood, Flower Datasets integrates with the following popular formats/frameworks:
 * Hugging Face,
-* PyTorch, 
-* TensorFlow, 
-* Numpy, 
-* Pandas, 
+* PyTorch,
+* TensorFlow,
+* Numpy,
+* Pandas,
 * Jax,
 * Arrow.
 
@@ -25,7 +25,7 @@ Create **custom partitioning schemes** or choose from the **implemented partitio
 * Partitioner (the abstract base class) `Partitioner`
 * IID partitioning `IidPartitioner(num_partitions)`
 * Natural ID partitioner `NaturalIdPartitioner`
-* Size partitioner (the abstract base class for the partitioners dictating the division based the number of samples) `SizePartitioner` 
+* Size partitioner (the abstract base class for the partitioners dictating the division based the number of samples) `SizePartitioner`
 * Linear partitioner `LinearPartitioner`
 * Square partitioner `SquarePartitioner`
 * Exponential partitioner `ExponentialPartitioner`
@@ -83,7 +83,7 @@ Here are a few of the things that we will work on in future releases:
 * ✅ Support for more datasets (especially the ones that have user id present).
 * ✅ Creation of custom `Partitioner`s.
 * ✅ More out-of-the-box `Partitioner`s.
-* ✅ Passing `Partitioner`s via `FederatedDataset`'s `partitioners` argument. 
+* ✅ Passing `Partitioner`s via `FederatedDataset`'s `partitioners` argument.
 * ✅ Customization of the dataset splitting before the partitioning.
 * Simplification of the dataset transformation to the popular frameworks/types.
 * Creation of the synthetic data,
diff --git a/examples/advanced-pytorch/README.md b/examples/advanced-pytorch/README.md
index 9101105b2618..c1ba85b95879 100644
--- a/examples/advanced-pytorch/README.md
+++ b/examples/advanced-pytorch/README.md
@@ -1,6 +1,6 @@
 # Advanced Flower Example (PyTorch)
 
-This example demonstrates an advanced federated learning setup using Flower with PyTorch. This example uses [Flower Datasets](https://flower.dev/docs/datasets/) and it differs from the quickstart example in the following ways:
+This example demonstrates an advanced federated learning setup using Flower with PyTorch. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) and it differs from the quickstart example in the following ways:
 
 - 10 clients (instead of just 2)
 - Each client holds a local dataset of 5000 training examples and 1000 test examples (note that using the `run.sh` script will only select 10 data samples by default, as the `--toy` argument is set).
diff --git a/examples/advanced-tensorflow/README.md b/examples/advanced-tensorflow/README.md
index b21c0d2545ca..59866fd99a06 100644
--- a/examples/advanced-tensorflow/README.md
+++ b/examples/advanced-tensorflow/README.md
@@ -1,6 +1,6 @@
 # Advanced Flower Example (TensorFlow/Keras)
 
-This example demonstrates an advanced federated learning setup using Flower with TensorFlow/Keras. This example uses [Flower Datasets](https://flower.dev/docs/datasets/) and it differs from the quickstart example in the following ways:
+This example demonstrates an advanced federated learning setup using Flower with TensorFlow/Keras. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) and it differs from the quickstart example in the following ways:
 
 - 10 clients (instead of just 2)
 - Each client holds a local dataset of 1/10 of the train datasets and 80% is training examples and 20% as test examples (note that by default only a small subset of this data is used when running the `run.sh` script)
diff --git a/examples/custom-metrics/README.md b/examples/custom-metrics/README.md
index debcd7919839..317fb6336106 100644
--- a/examples/custom-metrics/README.md
+++ b/examples/custom-metrics/README.md
@@ -9,7 +9,7 @@ The main takeaways of this implementation are:
 - the use of the `output_dict` on the client side - inside `evaluate` method on `client.py`
 - the use of the `evaluate_metrics_aggregation_fn` - to aggregate the metrics on the server side, part of the `strategy` on `server.py`
 
-This example is based on the `quickstart-tensorflow` with CIFAR-10, source [here](https://flower.dev/docs/quickstart-tensorflow.html), with the addition of [Flower Datasets](https://flower.dev/docs/datasets/index.html) to retrieve the CIFAR-10.
+This example is based on the `quickstart-tensorflow` with CIFAR-10, source [here](https://flower.ai/docs/quickstart-tensorflow.html), with the addition of [Flower Datasets](https://flower.ai/docs/datasets/index.html) to retrieve the CIFAR-10.
 
 Using the CIFAR-10 dataset for classification, this is a multi-class classification problem, thus some changes on how to calculate the metrics using `average='micro'` and `np.argmax` is required. For binary classification, this is not required. Also, for unsupervised learning tasks, such as using a deep autoencoder, a custom metric based on reconstruction error could be implemented on client side.
 
@@ -91,16 +91,16 @@ chmod +x run.sh
 ./run.sh
 ```
 
-You will see that Keras is starting a federated training. Have a look to the [Flower Quickstarter documentation](https://flower.dev/docs/quickstart-tensorflow.html) for a detailed explanation. You can add `steps_per_epoch=3` to `model.fit()` if you just want to evaluate that everything works without having to wait for the client-side training to finish (this will save you a lot of time during development).
+You will see that Keras is starting a federated training. Have a look to the [Flower Quickstarter documentation](https://flower.ai/docs/quickstart-tensorflow.html) for a detailed explanation. You can add `steps_per_epoch=3` to `model.fit()` if you just want to evaluate that everything works without having to wait for the client-side training to finish (this will save you a lot of time during development).
 
 Running `run.sh` will result in the following output (after 3 rounds):
 
 ```shell
 INFO flwr 2024-01-17 17:45:23,794 | app.py:228 | app_fit: metrics_distributed {
-    'accuracy': [(1, 0.10000000149011612), (2, 0.10000000149011612), (3, 0.3393000066280365)], 
-    'acc': [(1, 0.1), (2, 0.1), (3, 0.3393)], 
-    'rec': [(1, 0.1), (2, 0.1), (3, 0.3393)], 
-    'prec': [(1, 0.1), (2, 0.1), (3, 0.3393)], 
+    'accuracy': [(1, 0.10000000149011612), (2, 0.10000000149011612), (3, 0.3393000066280365)],
+    'acc': [(1, 0.1), (2, 0.1), (3, 0.3393)],
+    'rec': [(1, 0.1), (2, 0.1), (3, 0.3393)],
+    'prec': [(1, 0.1), (2, 0.1), (3, 0.3393)],
     'f1': [(1, 0.10000000000000002), (2, 0.10000000000000002), (3, 0.3393)]
 }
 ```
diff --git a/examples/flower-via-docker-compose/README.md b/examples/flower-via-docker-compose/README.md
index 1d830e46cbdb..3ef1ac37bcda 100644
--- a/examples/flower-via-docker-compose/README.md
+++ b/examples/flower-via-docker-compose/README.md
@@ -1,7 +1,7 @@
 # Leveraging Flower and Docker for Device Heterogeneity Management in Federated Learning
 
 <p align="center">
-  <img src="https://flower.dev/_next/image/?url=%2F_next%2Fstatic%2Fmedia%2Fflower_white_border.c2012e70.png&w=640&q=75" width="140px" alt="Flower Website" />
+  <img src="https://flower.ai/_next/image/?url=%2F_next%2Fstatic%2Fmedia%2Fflower_white_border.c2012e70.png&w=640&q=75" width="140px" alt="Flower Website" />
   <img src="https://github.com/ChoosyDevs/Choosy/assets/59146613/73d15990-453b-4da6-b8d6-df0f956a127c" width="140px" alt="Docker Logo" />
 </p>
 
@@ -141,7 +141,7 @@ By following these steps, you will have a fully functional federated learning en
 
 ### Data Pipeline with FLWR-Datasets
 
-We have integrated [`flwr-datasets`](https://flower.dev/docs/datasets/) into our data pipeline, which is managed within the `load_data.py` file in the `helpers/` directory. This script facilitates standardized access to datasets across the federated network and incorporates a `data_sampling_percentage` argument. This argument allows users to specify the percentage of the dataset to be used for training and evaluation, accommodating devices with lower memory capabilities to prevent Out-of-Memory (OOM) errors.
+We have integrated [`flwr-datasets`](https://flower.ai/docs/datasets/) into our data pipeline, which is managed within the `load_data.py` file in the `helpers/` directory. This script facilitates standardized access to datasets across the federated network and incorporates a `data_sampling_percentage` argument. This argument allows users to specify the percentage of the dataset to be used for training and evaluation, accommodating devices with lower memory capabilities to prevent Out-of-Memory (OOM) errors.
 
 ### Model Selection and Dataset
 
diff --git a/examples/pytorch-from-centralized-to-federated/README.md b/examples/pytorch-from-centralized-to-federated/README.md
index fccb14158ecd..06ee89dddcac 100644
--- a/examples/pytorch-from-centralized-to-federated/README.md
+++ b/examples/pytorch-from-centralized-to-federated/README.md
@@ -2,7 +2,7 @@
 
 This example demonstrates how an already existing centralized PyTorch-based machine learning project can be federated with Flower.
 
-This introductory example for Flower uses PyTorch, but you're not required to be a PyTorch expert to run the example. The example will help you to understand how Flower can be used to build federated learning use cases based on existing machine learning projects. This example uses [Flower Datasets](https://flower.dev/docs/datasets/) to download, partition and preprocess the CIFAR-10 dataset.
+This introductory example for Flower uses PyTorch, but you're not required to be a PyTorch expert to run the example. The example will help you to understand how Flower can be used to build federated learning use cases based on existing machine learning projects. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the CIFAR-10 dataset.
 
 ## Project Setup
 
diff --git a/examples/quickstart-huggingface/README.md b/examples/quickstart-huggingface/README.md
index fd868aa1fcce..5fdba887f181 100644
--- a/examples/quickstart-huggingface/README.md
+++ b/examples/quickstart-huggingface/README.md
@@ -1,6 +1,6 @@
 # Federated HuggingFace Transformers using Flower and PyTorch
 
-This introductory example to using [HuggingFace](https://huggingface.co) Transformers with Flower with PyTorch. This example has been extended from the [quickstart-pytorch](https://flower.dev/docs/examples/quickstart-pytorch.html) example. The training script closely follows the [HuggingFace course](https://huggingface.co/course/chapter3?fw=pt), so you are encouraged to check that out for a detailed explanation of the transformer pipeline.
+This introductory example to using [HuggingFace](https://huggingface.co) Transformers with Flower with PyTorch. This example has been extended from the [quickstart-pytorch](https://flower.ai/docs/examples/quickstart-pytorch.html) example. The training script closely follows the [HuggingFace course](https://huggingface.co/course/chapter3?fw=pt), so you are encouraged to check that out for a detailed explanation of the transformer pipeline.
 
 Like `quickstart-pytorch`, running this example in itself is also meant to be quite easy.
 
diff --git a/examples/quickstart-pandas/README.md b/examples/quickstart-pandas/README.md
index a25e6ea6ee36..efcda43cf34d 100644
--- a/examples/quickstart-pandas/README.md
+++ b/examples/quickstart-pandas/README.md
@@ -1,6 +1,6 @@
 # Flower Example using Pandas
 
-This introductory example to Flower uses Pandas, but deep knowledge of Pandas is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case. This example uses [Flower Datasets](https://flower.dev/docs/datasets/) to
+This introductory example to Flower uses Pandas, but deep knowledge of Pandas is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to
 download, partition and preprocess the dataset.
 Running this example in itself is quite easy.
 
@@ -79,4 +79,4 @@ Start client 2 in the second terminal:
 $ python3 client.py --node-id 1
 ```
 
-You will see that the server is printing aggregated statistics about the dataset distributed amongst clients. Have a look to the [Flower Quickstarter documentation](https://flower.dev/docs/quickstart-pandas.html) for a detailed explanation.
+You will see that the server is printing aggregated statistics about the dataset distributed amongst clients. Have a look to the [Flower Quickstarter documentation](https://flower.ai/docs/quickstart-pandas.html) for a detailed explanation.
diff --git a/examples/quickstart-pytorch-lightning/README.md b/examples/quickstart-pytorch-lightning/README.md
index 1287b50bca65..1d404a5d714f 100644
--- a/examples/quickstart-pytorch-lightning/README.md
+++ b/examples/quickstart-pytorch-lightning/README.md
@@ -1,6 +1,6 @@
 # Flower Example using PyTorch Lightning
 
-This introductory example to Flower uses PyTorch, but deep knowledge of PyTorch Lightning is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case. Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.dev/docs/datasets/) to download, partition and preprocess the MNIST dataset.
+This introductory example to Flower uses PyTorch, but deep knowledge of PyTorch Lightning is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case. Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the MNIST dataset.
 
 ## Project Setup
 
diff --git a/examples/quickstart-pytorch/README.md b/examples/quickstart-pytorch/README.md
index 6de0dcf7ab32..3b9b9b310608 100644
--- a/examples/quickstart-pytorch/README.md
+++ b/examples/quickstart-pytorch/README.md
@@ -1,6 +1,6 @@
 # Flower Example using PyTorch
 
-This introductory example to Flower uses PyTorch, but deep knowledge of PyTorch is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case. Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.dev/docs/datasets/) to download, partition and preprocess the CIFAR-10 dataset.
+This introductory example to Flower uses PyTorch, but deep knowledge of PyTorch is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case. Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the CIFAR-10 dataset.
 
 ## Project Setup
 
diff --git a/examples/quickstart-sklearn-tabular/README.md b/examples/quickstart-sklearn-tabular/README.md
index d62525c96c18..373aaea5999c 100644
--- a/examples/quickstart-sklearn-tabular/README.md
+++ b/examples/quickstart-sklearn-tabular/README.md
@@ -3,7 +3,7 @@
 This example of Flower uses `scikit-learn`'s `LogisticRegression` model to train a federated learning system on
 "iris" (tabular) dataset.
 It will help you understand how to adapt Flower for use with `scikit-learn`.
-Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.dev/docs/datasets/) to
+Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to
 download, partition and preprocess the dataset.
 
 ## Project Setup
diff --git a/examples/quickstart-tensorflow/README.md b/examples/quickstart-tensorflow/README.md
index 92d38c9340d7..8d5e9434b086 100644
--- a/examples/quickstart-tensorflow/README.md
+++ b/examples/quickstart-tensorflow/README.md
@@ -1,7 +1,7 @@
 # Flower Example using TensorFlow/Keras
 
 This introductory example to Flower uses Keras but deep knowledge of Keras is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case.
-Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.dev/docs/datasets/) to download, partition and preprocess the CIFAR-10 dataset.
+Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the CIFAR-10 dataset.
 
 ## Project Setup
 
diff --git a/examples/simulation-pytorch/README.md b/examples/simulation-pytorch/README.md
index 11b7a3364376..5ba5ec70dc3e 100644
--- a/examples/simulation-pytorch/README.md
+++ b/examples/simulation-pytorch/README.md
@@ -1,6 +1,6 @@
 # Flower Simulation example using PyTorch
 
-This introductory example uses the simulation capabilities of Flower to simulate a large number of clients on a single machine. Take a look at the [Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html) for a deep dive into how Flower simulation works. This example uses [Flower Datasets](https://flower.dev/docs/datasets/) to download, partition and preprocess the MNIST dataset. This examples uses 100 clients by default.
+This introductory example uses the simulation capabilities of Flower to simulate a large number of clients on a single machine. Take a look at the [Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) for a deep dive into how Flower simulation works. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the MNIST dataset. This examples uses 100 clients by default.
 
 ## Running the example (via Jupyter Notebook)
 
@@ -79,4 +79,4 @@ python sim.py --num_cpus=2
 python sim.py --num_cpus=2 --num_gpus=0.2
 ```
 
-Take a look at the [Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html) for more details on how you can customise your simulation.
+Take a look at the [Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) for more details on how you can customise your simulation.
diff --git a/examples/simulation-tensorflow/README.md b/examples/simulation-tensorflow/README.md
index f0d94f343d37..75be823db2eb 100644
--- a/examples/simulation-tensorflow/README.md
+++ b/examples/simulation-tensorflow/README.md
@@ -1,6 +1,6 @@
 # Flower Simulation example using TensorFlow/Keras
 
-This introductory example uses the simulation capabilities of Flower to simulate a large number of clients on a single machine. Take a look at the [Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html) for a deep dive into how Flower simulation works. This example uses [Flower Datasets](https://flower.dev/docs/datasets/) to download, partition and preprocess the MNIST dataset. This examples uses 100 clients by default.
+This introductory example uses the simulation capabilities of Flower to simulate a large number of clients on a single machine. Take a look at the [Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) for a deep dive into how Flower simulation works. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the MNIST dataset. This examples uses 100 clients by default.
 
 ## Running the example (via Jupyter Notebook)
 
@@ -78,4 +78,4 @@ python sim.py --num_cpus=2
 python sim.py --num_cpus=2 --num_gpus=0.2
 ```
 
-Take a look at the [Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html) for more details on how you can customise your simulation.
+Take a look at the [Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) for more details on how you can customise your simulation.
diff --git a/examples/sklearn-logreg-mnist/README.md b/examples/sklearn-logreg-mnist/README.md
index ee3cdfc9768e..50576d98ba3d 100644
--- a/examples/sklearn-logreg-mnist/README.md
+++ b/examples/sklearn-logreg-mnist/README.md
@@ -1,7 +1,7 @@
 # Flower Example using scikit-learn
 
 This example of Flower uses `scikit-learn`'s `LogisticRegression` model to train a federated learning system. It will help you understand how to adapt Flower for use with `scikit-learn`.
-Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.dev/docs/datasets/) to download, partition and preprocess the MNIST dataset.
+Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the MNIST dataset.
 
 ## Project Setup
 
diff --git a/examples/whisper-federated-finetuning/README.md b/examples/whisper-federated-finetuning/README.md
index e89a09519fed..ddebe51247b2 100644
--- a/examples/whisper-federated-finetuning/README.md
+++ b/examples/whisper-federated-finetuning/README.md
@@ -110,7 +110,7 @@ An overview of the FL pipeline built with Flower for this example is illustrated
 3. Once on-site training is completed, each client sends back the (now updated) classification head to the Flower server.
 4. The Flower server aggregates (via FedAvg) the classification heads in order to obtain a new _global_ classification head. This head will be shared with clients in the next round.
 
-Flower supports two ways of doing Federated Learning: simulated and non-simulated FL. The former, managed by the [`VirtualClientEngine`](https://flower.dev/docs/framework/how-to-run-simulations.html), allows you to run large-scale workloads in a system-aware manner, that scales with the resources available on your system (whether it is a laptop, a desktop with a single GPU, or a cluster of GPU servers). The latter is better suited for settings where clients are unique devices (e.g. a server, a smart device, etc). This example shows you how to use both.
+Flower supports two ways of doing Federated Learning: simulated and non-simulated FL. The former, managed by the [`VirtualClientEngine`](https://flower.ai/docs/framework/how-to-run-simulations.html), allows you to run large-scale workloads in a system-aware manner, that scales with the resources available on your system (whether it is a laptop, a desktop with a single GPU, or a cluster of GPU servers). The latter is better suited for settings where clients are unique devices (e.g. a server, a smart device, etc). This example shows you how to use both.
 
 ### Preparing the dataset
 
@@ -147,7 +147,7 @@ INFO flwr 2023-11-08 14:03:57,557 | app.py:229 | app_fit: metrics_centralized {'
 
 With just 5 FL rounds, the global model should be reaching ~95% validation accuracy. A test accuracy of 97% can be reached with 10 rounds of FL training using the default hyperparameters. On an RTX 3090Ti, each round takes ~20-30s depending on the amount of data the clients selected in a round have.
 
-Take a look at the [Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html) for more details on how you can customize your simulation.
+Take a look at the [Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) for more details on how you can customize your simulation.
 
 ### Federated Finetuning (non-simulated)
 
diff --git a/examples/xgboost-comprehensive/README.md b/examples/xgboost-comprehensive/README.md
index 97ecc39b47f2..01fed646d056 100644
--- a/examples/xgboost-comprehensive/README.md
+++ b/examples/xgboost-comprehensive/README.md
@@ -1,7 +1,7 @@
 # Flower Example using XGBoost (Comprehensive)
 
 This example demonstrates a comprehensive federated learning setup using Flower with XGBoost.
-We use [HIGGS](https://archive.ics.uci.edu/dataset/280/higgs) dataset to perform a binary classification task. This examples uses [Flower Datasets](https://flower.dev/docs/datasets/) to retrieve, partition and preprocess the data for each Flower client.
+We use [HIGGS](https://archive.ics.uci.edu/dataset/280/higgs) dataset to perform a binary classification task. This examples uses [Flower Datasets](https://flower.ai/docs/datasets/) to retrieve, partition and preprocess the data for each Flower client.
 It differs from the [xgboost-quickstart](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart) example in the following ways:
 
 - Arguments parsers of server and clients for hyperparameters selection.
@@ -91,7 +91,7 @@ pip install -r requirements.txt
 
 ## Run Federated Learning with XGBoost and Flower
 
-You can run this example in two ways: either by manually launching the server, and then several clients that connect to it; or by launching a Flower simulation. Both run the same workload, yielding identical results. The former is ideal for deployments on different machines, while the latter makes it easy to simulate large client cohorts in a resource-aware manner. You can read more about how Flower Simulation works in the [Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html). The commands shown below assume you have activated your environment (if you decide to use Poetry, you can activate it via `poetry shell`).
+You can run this example in two ways: either by manually launching the server, and then several clients that connect to it; or by launching a Flower simulation. Both run the same workload, yielding identical results. The former is ideal for deployments on different machines, while the latter makes it easy to simulate large client cohorts in a resource-aware manner. You can read more about how Flower Simulation works in the [Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html). The commands shown below assume you have activated your environment (if you decide to use Poetry, you can activate it via `poetry shell`).
 
 ### Independent Client/Server Setup
 
@@ -143,7 +143,7 @@ python sim.py --train-method=cyclic --pool-size=5 --num-rounds=30 --centralised-
 ```
 
 In addition, we provide more options to customise the experimental settings, including data partitioning and centralised/distributed evaluation (see `utils.py`).
-Check the [tutorial](https://flower.dev/docs/framework/tutorial-quickstart-xgboost.html) for a detailed explanation.
+Check the [tutorial](https://flower.ai/docs/framework/tutorial-quickstart-xgboost.html) for a detailed explanation.
 
 ### Expected Experimental Results
 
diff --git a/examples/xgboost-quickstart/README.md b/examples/xgboost-quickstart/README.md
index 5174c236c668..cd99cd4c2895 100644
--- a/examples/xgboost-quickstart/README.md
+++ b/examples/xgboost-quickstart/README.md
@@ -85,4 +85,4 @@ poetry run ./run.sh
 ```
 
 Look at the [code](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart)
-and [tutorial](https://flower.dev/docs/framework/tutorial-quickstart-xgboost.html) for a detailed explanation.
+and [tutorial](https://flower.ai/docs/framework/tutorial-quickstart-xgboost.html) for a detailed explanation.

From 5af1679d1dc712f3a027ebbfb028cf3f633b0afc Mon Sep 17 00:00:00 2001
From: Robert Steiner <robert@flower.dev>
Date: Thu, 15 Feb 2024 11:45:44 +0100
Subject: [PATCH 009/102] Update URL in fedbn README (#2950)

---
 baselines/fedbn/README.md | 52 +++++++++++++++++++--------------------
 1 file changed, 26 insertions(+), 26 deletions(-)

diff --git a/baselines/fedbn/README.md b/baselines/fedbn/README.md
index 4b271bd49851..d50c6f5bb605 100644
--- a/baselines/fedbn/README.md
+++ b/baselines/fedbn/README.md
@@ -34,37 +34,37 @@ dataset: [MNIST, MNIST-M, SVHN, USPS, SynthDigits]
 
 **Model:** A six-layer CNN with 14,219,210 parameters following the structure described in appendix D.2.
 
-**Dataset:**  This baseline makes use of the pre-processed partitions created and open source by the authors of the FedBN paper. You can read more about how those were created [here](https://github.com/med-air/FedBN). Follow the steps below in the `Environment Setup` section to download them. 
+**Dataset:**  This baseline makes use of the pre-processed partitions created and open source by the authors of the FedBN paper. You can read more about how those were created [here](https://github.com/med-air/FedBN). Follow the steps below in the `Environment Setup` section to download them.
 
 
 A more detailed explanation of the datasets is given in the following table.
 
-|     | MNIST     | MNIST-M   | SVHN  |  USPS    | SynthDigits |
-|--- |---        |---        |---    |---            |---    |
-| data type| handwritten digits| MNIST modification randomly colored with colored patches| Street view house numbers | handwritten digits from envelopes by the U.S. Postal Service | Syntehtic digits Windows TM font varying the orientation, blur and stroke colors |
-| color | greyscale | RGB | RGB | greyscale | RGB |
-| pixelsize | 28x28 | 28 x 28 | 32 x32 | 16 x16 | 32 x32 |
-| labels | 0-9 | 0-9 | 1-10 | 0-9 | 1-10 |
-| number of trainset | 60.000 | 60.000 | 73.257 | 9,298 | 50.000 |
-| number of testset| 10.000 | 10.000 | 26.032 | - | - |
-| image shape | (28,28) | (28,28,3) | (32,32,3) | (16,16) | (32,32,3) |
+|                    | MNIST              | MNIST-M                                                  | SVHN                      | USPS                                                         | SynthDigits                                                                      |
+| ------------------ | ------------------ | -------------------------------------------------------- | ------------------------- | ------------------------------------------------------------ | -------------------------------------------------------------------------------- |
+| data type          | handwritten digits | MNIST modification randomly colored with colored patches | Street view house numbers | handwritten digits from envelopes by the U.S. Postal Service | Syntehtic digits Windows TM font varying the orientation, blur and stroke colors |
+| color              | greyscale          | RGB                                                      | RGB                       | greyscale                                                    | RGB                                                                              |
+| pixelsize          | 28x28              | 28 x 28                                                  | 32 x32                    | 16 x16                                                       | 32 x32                                                                           |
+| labels             | 0-9                | 0-9                                                      | 1-10                      | 0-9                                                          | 1-10                                                                             |
+| number of trainset | 60.000             | 60.000                                                   | 73.257                    | 9,298                                                        | 50.000                                                                           |
+| number of testset  | 10.000             | 10.000                                                   | 26.032                    | -                                                            | -                                                                                |
+| image shape        | (28,28)            | (28,28,3)                                                | (32,32,3)                 | (16,16)                                                      | (32,32,3)                                                                        |
 
 
 **Training Hyperparameters:** By default (i.e. if you don't override anything in the config) these main hyperparameters used are shown in the table below. For a complete list of hyperparameters, please refer to the config files in `fedbn/conf`.
 
-| Description | Value |
-| ----------- | ----- |
-| rounds | 10 |
-| num_clients | 5 |
-| strategy_fraction_fit | 1.0 |
-| strategy.fraction_evaluate | 0.0 |
-| training samples per client| 743 |
-| client.l_r | 10E-2 |
-| local epochs | 1 |
-| loss | cross entropy loss |
-| optimizer | SGD |
-| client_resources.num_cpu | 2 |
-| client_resources.num_gpus | 0.0 |
+| Description                 | Value              |
+| --------------------------- | ------------------ |
+| rounds                      | 10                 |
+| num_clients                 | 5                  |
+| strategy_fraction_fit       | 1.0                |
+| strategy.fraction_evaluate  | 0.0                |
+| training samples per client | 743                |
+| client.l_r                  | 10E-2              |
+| local epochs                | 1                  |
+| loss                        | cross entropy loss |
+| optimizer                   | SGD                |
+| client_resources.num_cpu    | 2                  |
+| client_resources.num_gpus   | 0.0                |
 
 ## Environment Setup
 
@@ -93,7 +93,7 @@ cd data ..
 
 ## Running the Experiments
 
-First, activate your environment via `poetry shell`. The commands below show how to run the experiments and modify some of its key hyperparameters via the cli. Each time you run an experiment, the log and results will be stored inside `outputs/<date>/<time>`. Please refer to [the Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html) to learn more about Flower Simulation.
+First, activate your environment via `poetry shell`. The commands below show how to run the experiments and modify some of its key hyperparameters via the cli. Each time you run an experiment, the log and results will be stored inside `outputs/<date>/<time>`. Please refer to [the Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) to learn more about Flower Simulation.
 
 ```bash
 # run with default arguments
@@ -111,7 +111,7 @@ python -m fedbn.main strategy.fraction_evaluate=1 # your code will run slower
 # adjust hyperparameters like the number of rounds or batch size like this
 python -m fedbn.main num_rounds=100 dataset.batch_size
 
-# increase the number of clients like this (note this should be a multiple 
+# increase the number of clients like this (note this should be a multiple
 # of the number of dataset you involve in the experiment -- 5 by default)
 # this means that without changing other hyperparameters, you can only have
 # either 5,10,15,20,25,30,35,40,45 or 50 clients
@@ -132,7 +132,7 @@ python -m fedbn.main client=fedavg
 
 ## Limitations
 
-The pre-processing of the five datasets provided by the authors, imposes some limitations on the number of clients that can be spawned for the experiment. Naturally, this limitation can be circumvented if you edit the code, and in particular the `dataset.DigitsDataset` constructor. The aforementioned limitation happens because each dataset is partitioned into 10 disjoint sets and a 'DigitsDataset' can only be constructed by concatenating any set of such partitions (at least one, at most all 10). _How does the limitation manifest?_ Given that we have 5 datasets, if a client just takes one partition, a FL setup can accommodate 50 clients, one using a different partition. But, if you want for instance each client have 3 partitions of the same dataset (yes, clients can only hold data of one dataset) then the maximum number of clients gets reduced to 20. Following this logic you can see that if a client wants to use all 10 partitions of a given dataset, then only 5 clients can participate in the experiment. 
+The pre-processing of the five datasets provided by the authors, imposes some limitations on the number of clients that can be spawned for the experiment. Naturally, this limitation can be circumvented if you edit the code, and in particular the `dataset.DigitsDataset` constructor. The aforementioned limitation happens because each dataset is partitioned into 10 disjoint sets and a 'DigitsDataset' can only be constructed by concatenating any set of such partitions (at least one, at most all 10). _How does the limitation manifest?_ Given that we have 5 datasets, if a client just takes one partition, a FL setup can accommodate 50 clients, one using a different partition. But, if you want for instance each client have 3 partitions of the same dataset (yes, clients can only hold data of one dataset) then the maximum number of clients gets reduced to 20. Following this logic you can see that if a client wants to use all 10 partitions of a given dataset, then only 5 clients can participate in the experiment.
 
 Another limitation in the current implementation is that there should be the same number of clients for each dataset. Also, all clients should contain the same number of partitions of their respective datasets. You can remove these constrain my editing `dataset.get_data()`.
 

From 277ab1ffaf359f9ffce127b8c289bc0292886c53 Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.dev>
Date: Thu, 15 Feb 2024 15:20:06 +0100
Subject: [PATCH 010/102] Create server-side compatibility package (#2957)

---
 e2e/bare-https/driver.py                      |   2 +-
 e2e/bare/driver.py                            |   2 +-
 e2e/fastai/driver.py                          |   2 +-
 e2e/jax/driver.py                             |   2 +-
 e2e/opacus/driver.py                          |   2 +-
 e2e/pandas/driver.py                          |   2 +-
 e2e/pytorch-lightning/driver.py               |   2 +-
 e2e/pytorch/driver.py                         |   2 +-
 e2e/scikit-learn/driver.py                    |   2 +-
 e2e/tabnet/driver.py                          |   2 +-
 e2e/tensorflow/driver.py                      |   2 +-
 examples/quickstart-cpp/driver.py             |   2 +-
 src/py/flwr/server/__init__.py                |   4 +-
 src/py/flwr/server/app.py                     | 108 +--------------
 src/py/flwr/server/compat/__init__.py         |  22 +++
 src/py/flwr/server/{driver => compat}/app.py  |  11 +-
 .../server/{driver => compat}/app_test.py     |   3 +-
 .../{driver => compat}/driver_client_proxy.py |   2 +-
 .../driver_client_proxy_test.py               |   3 +-
 src/py/flwr/server/driver/__init__.py         |   2 -
 src/py/flwr/server/run_serverapp.py           | 131 ++++++++++++++++++
 21 files changed, 180 insertions(+), 130 deletions(-)
 create mode 100644 src/py/flwr/server/compat/__init__.py
 rename src/py/flwr/server/{driver => compat}/app.py (97%)
 rename src/py/flwr/server/{driver => compat}/app_test.py (98%)
 rename src/py/flwr/server/{driver => compat}/driver_client_proxy.py (99%)
 rename src/py/flwr/server/{driver => compat}/driver_client_proxy_test.py (99%)
 create mode 100644 src/py/flwr/server/run_serverapp.py

diff --git a/e2e/bare-https/driver.py b/e2e/bare-https/driver.py
index dd7c9eab7248..f7bfeb613f6a 100644
--- a/e2e/bare-https/driver.py
+++ b/e2e/bare-https/driver.py
@@ -3,7 +3,7 @@
 
 
 # Start Flower server
-hist = fl.server.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="127.0.0.1:9091",
     config=fl.server.ServerConfig(num_rounds=3),
     root_certificates=Path("certificates/ca.crt").read_bytes(),
diff --git a/e2e/bare/driver.py b/e2e/bare/driver.py
index d428fe757aa9..defc2ad56213 100644
--- a/e2e/bare/driver.py
+++ b/e2e/bare/driver.py
@@ -2,7 +2,7 @@
 
 
 # Start Flower server
-hist = fl.server.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
 )
diff --git a/e2e/fastai/driver.py b/e2e/fastai/driver.py
index b7b1c41ff5a3..cc452ea523ca 100644
--- a/e2e/fastai/driver.py
+++ b/e2e/fastai/driver.py
@@ -1,6 +1,6 @@
 import flwr as fl
 
-hist = fl.server.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
 )
diff --git a/e2e/jax/driver.py b/e2e/jax/driver.py
index b7b1c41ff5a3..cc452ea523ca 100644
--- a/e2e/jax/driver.py
+++ b/e2e/jax/driver.py
@@ -1,6 +1,6 @@
 import flwr as fl
 
-hist = fl.server.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
 )
diff --git a/e2e/opacus/driver.py b/e2e/opacus/driver.py
index 5bc40800c33c..75acd9ccea24 100644
--- a/e2e/opacus/driver.py
+++ b/e2e/opacus/driver.py
@@ -1,6 +1,6 @@
 import flwr as fl
 
-hist = fl.server.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
 )
diff --git a/e2e/pandas/driver.py b/e2e/pandas/driver.py
index 78120fc946f2..f5dc74c9f3f8 100644
--- a/e2e/pandas/driver.py
+++ b/e2e/pandas/driver.py
@@ -3,7 +3,7 @@
 from strategy import FedAnalytics
 
 # Start Flower server
-hist = fl.server.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=1),
     strategy=FedAnalytics(),
diff --git a/e2e/pytorch-lightning/driver.py b/e2e/pytorch-lightning/driver.py
index b7b1c41ff5a3..cc452ea523ca 100644
--- a/e2e/pytorch-lightning/driver.py
+++ b/e2e/pytorch-lightning/driver.py
@@ -1,6 +1,6 @@
 import flwr as fl
 
-hist = fl.server.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
 )
diff --git a/e2e/pytorch/driver.py b/e2e/pytorch/driver.py
index 9f9b076ee75b..2ea4de69a62b 100644
--- a/e2e/pytorch/driver.py
+++ b/e2e/pytorch/driver.py
@@ -18,7 +18,7 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 strategy = fl.server.strategy.FedAvg(evaluate_metrics_aggregation_fn=weighted_average)
 
 # Start Flower server
-hist = fl.server.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
     strategy=strategy,
diff --git a/e2e/scikit-learn/driver.py b/e2e/scikit-learn/driver.py
index e7ce124e5ead..29051d02c6b6 100644
--- a/e2e/scikit-learn/driver.py
+++ b/e2e/scikit-learn/driver.py
@@ -36,7 +36,7 @@ def evaluate(server_round, parameters: fl.common.NDArrays, config):
         evaluate_fn=get_evaluate_fn(model),
         on_fit_config_fn=fit_round,
     )
-    hist = fl.server.driver.start_driver(
+    hist = fl.server.start_driver(
         server_address="0.0.0.0:9091",
         strategy=strategy,
         config=fl.server.ServerConfig(num_rounds=3),
diff --git a/e2e/tabnet/driver.py b/e2e/tabnet/driver.py
index b7b1c41ff5a3..cc452ea523ca 100644
--- a/e2e/tabnet/driver.py
+++ b/e2e/tabnet/driver.py
@@ -1,6 +1,6 @@
 import flwr as fl
 
-hist = fl.server.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
 )
diff --git a/e2e/tensorflow/driver.py b/e2e/tensorflow/driver.py
index 9f9b076ee75b..2ea4de69a62b 100644
--- a/e2e/tensorflow/driver.py
+++ b/e2e/tensorflow/driver.py
@@ -18,7 +18,7 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 strategy = fl.server.strategy.FedAvg(evaluate_metrics_aggregation_fn=weighted_average)
 
 # Start Flower server
-hist = fl.server.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
     strategy=strategy,
diff --git a/examples/quickstart-cpp/driver.py b/examples/quickstart-cpp/driver.py
index 3b3036f7e928..f19cf0e9bd98 100644
--- a/examples/quickstart-cpp/driver.py
+++ b/examples/quickstart-cpp/driver.py
@@ -3,7 +3,7 @@
 
 # Start Flower server for three rounds of federated learning
 if __name__ == "__main__":
-    fl.server.driver.start_driver(
+    fl.server.start_driver(
         server_address="0.0.0.0:9091",
         config=fl.server.ServerConfig(num_rounds=3),
         strategy=FedAvgCpp(),
diff --git a/src/py/flwr/server/__init__.py b/src/py/flwr/server/__init__.py
index b0f95f903811..09372e258861 100644
--- a/src/py/flwr/server/__init__.py
+++ b/src/py/flwr/server/__init__.py
@@ -18,12 +18,13 @@
 from . import driver, strategy
 from .app import run_driver_api as run_driver_api
 from .app import run_fleet_api as run_fleet_api
-from .app import run_server_app as run_server_app
 from .app import run_superlink as run_superlink
 from .app import start_server as start_server
 from .client_manager import ClientManager as ClientManager
 from .client_manager import SimpleClientManager as SimpleClientManager
+from .compat import start_driver as start_driver
 from .history import History as History
+from .run_serverapp import run_server_app as run_server_app
 from .server import Server as Server
 from .server_config import ServerConfig as ServerConfig
 from .serverapp import ServerApp as ServerApp
@@ -40,6 +41,7 @@
     "ServerApp",
     "ServerConfig",
     "SimpleClientManager",
+    "start_driver",
     "start_server",
     "strategy",
 ]
diff --git a/src/py/flwr/server/app.py b/src/py/flwr/server/app.py
index 66adcbdb6b85..dbbf63b0fe5e 100644
--- a/src/py/flwr/server/app.py
+++ b/src/py/flwr/server/app.py
@@ -19,7 +19,7 @@
 import importlib.util
 import sys
 import threading
-from logging import DEBUG, ERROR, INFO, WARN
+from logging import ERROR, INFO, WARN
 from os.path import isfile
 from pathlib import Path
 from signal import SIGINT, SIGTERM, signal
@@ -47,7 +47,6 @@
 from .history import History
 from .server import Server
 from .server_config import ServerConfig
-from .serverapp import ServerApp, load_server_app
 from .strategy import FedAvg, Strategy
 from .superlink.driver.driver_servicer import DriverServicer
 from .superlink.fleet.grpc_bidi.grpc_server import (
@@ -65,72 +64,6 @@
 DATABASE = ":flwr-in-memory-state:"
 
 
-def run_server_app() -> None:
-    """Run Flower server app."""
-    event(EventType.RUN_SERVER_APP_ENTER)
-
-    args = _parse_args_run_server_app().parse_args()
-
-    # Obtain certificates
-    if args.insecure:
-        if args.root_certificates is not None:
-            sys.exit(
-                "Conflicting options: The '--insecure' flag disables HTTPS, "
-                "but '--root-certificates' was also specified. Please remove "
-                "the '--root-certificates' option when running in insecure mode, "
-                "or omit '--insecure' to use HTTPS."
-            )
-        log(
-            WARN,
-            "Option `--insecure` was set. "
-            "Starting insecure HTTP client connected to %s.",
-            args.server,
-        )
-        root_certificates = None
-    else:
-        # Load the certificates if provided, or load the system certificates
-        cert_path = args.root_certificates
-        if cert_path is None:
-            root_certificates = None
-        else:
-            root_certificates = Path(cert_path).read_bytes()
-        log(
-            DEBUG,
-            "Starting secure HTTPS client connected to %s "
-            "with the following certificates: %s.",
-            args.server,
-            cert_path,
-        )
-
-    log(
-        DEBUG,
-        "Flower will load ServerApp `%s`",
-        getattr(args, "server-app"),
-    )
-
-    log(
-        DEBUG,
-        "root_certificates: `%s`",
-        root_certificates,
-    )
-
-    log(WARN, "Not implemented: run_server_app")
-
-    server_app_dir = args.dir
-    if server_app_dir is not None:
-        sys.path.insert(0, server_app_dir)
-
-    def _load() -> ServerApp:
-        server_app: ServerApp = load_server_app(getattr(args, "server-app"))
-        return server_app
-
-    server_app = _load()
-
-    log(DEBUG, "server_app: `%s`", server_app)
-
-    event(EventType.RUN_SERVER_APP_LEAVE)
-
-
 def start_server(  # pylint: disable=too-many-arguments,too-many-locals
     *,
     server_address: str = ADDRESS_FLEET_API_GRPC_BIDI,
@@ -816,42 +749,3 @@ def _add_args_fleet_api(parser: argparse.ArgumentParser) -> None:
         type=int,
         default=1,
     )
-
-
-def _parse_args_run_server_app() -> argparse.ArgumentParser:
-    """Parse flower-server-app command line arguments."""
-    parser = argparse.ArgumentParser(
-        description="Start a Flower server app",
-    )
-
-    parser.add_argument(
-        "server-app",
-        help="For example: `server:app` or `project.package.module:wrapper.app`",
-    )
-    parser.add_argument(
-        "--insecure",
-        action="store_true",
-        help="Run the server app without HTTPS. By default, the app runs with "
-        "HTTPS enabled. Use this flag only if you understand the risks.",
-    )
-    parser.add_argument(
-        "--root-certificates",
-        metavar="ROOT_CERT",
-        type=str,
-        help="Specifies the path to the PEM-encoded root certificate file for "
-        "establishing secure HTTPS connections.",
-    )
-    parser.add_argument(
-        "--server",
-        default="0.0.0.0:9092",
-        help="Server address",
-    )
-    parser.add_argument(
-        "--dir",
-        default="",
-        help="Add specified directory to the PYTHONPATH and load Flower "
-        "app from there."
-        " Default: current working directory.",
-    )
-
-    return parser
diff --git a/src/py/flwr/server/compat/__init__.py b/src/py/flwr/server/compat/__init__.py
new file mode 100644
index 000000000000..3a0c2b4e83a0
--- /dev/null
+++ b/src/py/flwr/server/compat/__init__.py
@@ -0,0 +1,22 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Flower ServerApp compatibility package."""
+
+
+from .app import start_driver as start_driver
+
+__all__ = [
+    "start_driver",
+]
diff --git a/src/py/flwr/server/driver/app.py b/src/py/flwr/server/compat/app.py
similarity index 97%
rename from src/py/flwr/server/driver/app.py
rename to src/py/flwr/server/compat/app.py
index b47454b7b4b6..06debb858c38 100644
--- a/src/py/flwr/server/driver/app.py
+++ b/src/py/flwr/server/compat/app.py
@@ -33,8 +33,8 @@
 from flwr.server.server_config import ServerConfig
 from flwr.server.strategy import Strategy
 
+from ..driver.grpc_driver import GrpcDriver
 from .driver_client_proxy import DriverClientProxy
-from .grpc_driver import GrpcDriver
 
 DEFAULT_SERVER_ADDRESS_DRIVER = "[::]:9091"
 
@@ -111,10 +111,11 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
     # Create the Driver
     if isinstance(root_certificates, str):
         root_certificates = Path(root_certificates).read_bytes()
-    driver = GrpcDriver(
+    grpc_driver = GrpcDriver(
         driver_service_address=address, root_certificates=root_certificates
     )
-    driver.connect()
+
+    grpc_driver.connect()
     lock = threading.Lock()
 
     # Initialize the Driver API server and config
@@ -134,7 +135,7 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
     thread = threading.Thread(
         target=update_client_manager,
         args=(
-            driver,
+            grpc_driver,
             initialized_server.client_manager(),
             lock,
         ),
@@ -149,7 +150,7 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
 
     # Stop the Driver API server and the thread
     with lock:
-        driver.disconnect()
+        grpc_driver.disconnect()
     thread.join()
 
     event(EventType.START_SERVER_LEAVE)
diff --git a/src/py/flwr/server/driver/app_test.py b/src/py/flwr/server/compat/app_test.py
similarity index 98%
rename from src/py/flwr/server/driver/app_test.py
rename to src/py/flwr/server/compat/app_test.py
index 03f490807876..5f8f04ff2a06 100644
--- a/src/py/flwr/server/driver/app_test.py
+++ b/src/py/flwr/server/compat/app_test.py
@@ -26,7 +26,8 @@
 )
 from flwr.proto.node_pb2 import Node  # pylint: disable=E0611
 from flwr.server.client_manager import SimpleClientManager
-from flwr.server.driver.app import update_client_manager
+
+from .app import update_client_manager
 
 
 class TestClientManagerWithDriver(unittest.TestCase):
diff --git a/src/py/flwr/server/driver/driver_client_proxy.py b/src/py/flwr/server/compat/driver_client_proxy.py
similarity index 99%
rename from src/py/flwr/server/driver/driver_client_proxy.py
rename to src/py/flwr/server/compat/driver_client_proxy.py
index 8ea288dbb50c..1dc992106f60 100644
--- a/src/py/flwr/server/driver/driver_client_proxy.py
+++ b/src/py/flwr/server/compat/driver_client_proxy.py
@@ -31,7 +31,7 @@
 from flwr.proto import driver_pb2, node_pb2, task_pb2  # pylint: disable=E0611
 from flwr.server.client_proxy import ClientProxy
 
-from .grpc_driver import GrpcDriver
+from ..driver.grpc_driver import GrpcDriver
 
 SLEEP_TIME = 1
 
diff --git a/src/py/flwr/server/driver/driver_client_proxy_test.py b/src/py/flwr/server/compat/driver_client_proxy_test.py
similarity index 99%
rename from src/py/flwr/server/driver/driver_client_proxy_test.py
rename to src/py/flwr/server/compat/driver_client_proxy_test.py
index aa60448bd72e..de6566622b74 100644
--- a/src/py/flwr/server/driver/driver_client_proxy_test.py
+++ b/src/py/flwr/server/compat/driver_client_proxy_test.py
@@ -44,7 +44,8 @@
     Status,
 )
 from flwr.proto import driver_pb2, node_pb2, task_pb2  # pylint: disable=E0611
-from flwr.server.driver.driver_client_proxy import DriverClientProxy
+
+from .driver_client_proxy import DriverClientProxy
 
 MESSAGE_PARAMETERS = Parameters(tensors=[b"abc"], tensor_type="np")
 
diff --git a/src/py/flwr/server/driver/__init__.py b/src/py/flwr/server/driver/__init__.py
index 1c3b09cc334b..b61f6eebf6a8 100644
--- a/src/py/flwr/server/driver/__init__.py
+++ b/src/py/flwr/server/driver/__init__.py
@@ -15,12 +15,10 @@
 """Flower driver SDK."""
 
 
-from .app import start_driver
 from .driver import Driver
 from .grpc_driver import GrpcDriver
 
 __all__ = [
     "Driver",
     "GrpcDriver",
-    "start_driver",
 ]
diff --git a/src/py/flwr/server/run_serverapp.py b/src/py/flwr/server/run_serverapp.py
new file mode 100644
index 000000000000..35fffcf2d7ba
--- /dev/null
+++ b/src/py/flwr/server/run_serverapp.py
@@ -0,0 +1,131 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Run ServerApp."""
+
+
+import argparse
+import sys
+from logging import DEBUG, WARN
+from pathlib import Path
+
+from flwr.common import EventType, event
+from flwr.common.logger import log
+
+from .serverapp import ServerApp, load_server_app
+
+
+def run_server_app() -> None:
+    """Run Flower server app."""
+    event(EventType.RUN_SERVER_APP_ENTER)
+
+    args = _parse_args_run_server_app().parse_args()
+
+    # Obtain certificates
+    if args.insecure:
+        if args.root_certificates is not None:
+            sys.exit(
+                "Conflicting options: The '--insecure' flag disables HTTPS, "
+                "but '--root-certificates' was also specified. Please remove "
+                "the '--root-certificates' option when running in insecure mode, "
+                "or omit '--insecure' to use HTTPS."
+            )
+        log(
+            WARN,
+            "Option `--insecure` was set. "
+            "Starting insecure HTTP client connected to %s.",
+            args.server,
+        )
+        root_certificates = None
+    else:
+        # Load the certificates if provided, or load the system certificates
+        cert_path = args.root_certificates
+        if cert_path is None:
+            root_certificates = None
+        else:
+            root_certificates = Path(cert_path).read_bytes()
+        log(
+            DEBUG,
+            "Starting secure HTTPS client connected to %s "
+            "with the following certificates: %s.",
+            args.server,
+            cert_path,
+        )
+
+    log(
+        DEBUG,
+        "Flower will load ServerApp `%s`",
+        getattr(args, "server-app"),
+    )
+
+    log(
+        DEBUG,
+        "root_certificates: `%s`",
+        root_certificates,
+    )
+
+    log(WARN, "Not implemented: run_server_app")
+
+    server_app_dir = args.dir
+    if server_app_dir is not None:
+        sys.path.insert(0, server_app_dir)
+
+    def _load() -> ServerApp:
+        server_app: ServerApp = load_server_app(getattr(args, "server-app"))
+        return server_app
+
+    server_app = _load()
+
+    log(DEBUG, "server_app: `%s`", server_app)
+
+    event(EventType.RUN_SERVER_APP_LEAVE)
+
+
+def _parse_args_run_server_app() -> argparse.ArgumentParser:
+    """Parse flower-server-app command line arguments."""
+    parser = argparse.ArgumentParser(
+        description="Start a Flower server app",
+    )
+
+    parser.add_argument(
+        "server-app",
+        help="For example: `server:app` or `project.package.module:wrapper.app`",
+    )
+    parser.add_argument(
+        "--insecure",
+        action="store_true",
+        help="Run the server app without HTTPS. By default, the app runs with "
+        "HTTPS enabled. Use this flag only if you understand the risks.",
+    )
+    parser.add_argument(
+        "--root-certificates",
+        metavar="ROOT_CERT",
+        type=str,
+        help="Specifies the path to the PEM-encoded root certificate file for "
+        "establishing secure HTTPS connections.",
+    )
+    parser.add_argument(
+        "--server",
+        default="0.0.0.0:9092",
+        help="Server address",
+    )
+    parser.add_argument(
+        "--dir",
+        default="",
+        help="Add specified directory to the PYTHONPATH and load Flower "
+        "app from there."
+        " Default: current working directory.",
+    )
+
+    return parser

From 78ded0c95e8ab7911144617631af36d44fb02fbf Mon Sep 17 00:00:00 2001
From: Robert Steiner <robert@flower.dev>
Date: Thu, 15 Feb 2024 17:39:59 +0100
Subject: [PATCH 011/102] Update docs bucket URL (#2947)

---
 .github/workflows/docs.yml |  9 ++++----
 dev/deploy-docs.sh         | 42 --------------------------------------
 dev/deploy-swift-docs.sh   |  6 +++---
 3 files changed, 8 insertions(+), 49 deletions(-)
 delete mode 100755 dev/deploy-docs.sh

diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 94f3495a20ef..a4c769fdf850 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -43,8 +43,9 @@ jobs:
           AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
           AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
           AWS_SECRET_ACCESS_KEY: ${{ secrets. AWS_SECRET_ACCESS_KEY }}
+          DOCS_BUCKET: flower.ai
         run: |
-          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./doc/build/html/ s3://flower.dev/docs/framework
-          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./baselines/doc/build/html/ s3://flower.dev/docs/baselines
-          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./examples/doc/build/html/ s3://flower.dev/docs/examples
-          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./datasets/doc/build/html/ s3://flower.dev/docs/datasets
+          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./doc/build/html/ s3://${{ env.DOCS_BUCKET }}/docs/framework
+          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./baselines/doc/build/html/ s3://${{ env.DOCS_BUCKET }}/docs/baselines
+          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./examples/doc/build/html/ s3://${{ env.DOCS_BUCKET }}/docs/examples
+          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./datasets/doc/build/html/ s3://${{ env.DOCS_BUCKET }}/docs/datasets
diff --git a/dev/deploy-docs.sh b/dev/deploy-docs.sh
deleted file mode 100755
index 69fbd567baf3..000000000000
--- a/dev/deploy-docs.sh
+++ /dev/null
@@ -1,42 +0,0 @@
-#!/bin/bash
-
-# Copyright 2020 Flower Labs GmbH. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-set -e
-cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
-
-ROOT=`pwd`
-
-# Build and deploy Flower Framework docs
-cd doc
-./build-versioned-docs.sh
-cd build/html
-aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./ s3://flower.dev/docs/framework
-
-# Build and deploy Flower Baselines docs
-cd $ROOT
-cd baselines/doc
-make docs
-cd build/html
-aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./ s3://flower.dev/docs/baselines
-
-# Build and deploy Flower Examples docs
-cd $ROOT
-./dev/update-examples.sh
-cd examples/doc
-make docs
-cd build/html
-aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./ s3://flower.dev/docs/examples
diff --git a/dev/deploy-swift-docs.sh b/dev/deploy-swift-docs.sh
index 5abe618f83d3..de0bacb430cd 100755
--- a/dev/deploy-swift-docs.sh
+++ b/dev/deploy-swift-docs.sh
@@ -18,10 +18,10 @@
 set -e
 cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
 
-ROOT=`pwd`
+ROOT=$(pwd)
 
 # Build and deploy Flower iOS docs
-cd $ROOT
+cd "$ROOT"
 ./dev/build-swift-api-ref.sh
 cd SwiftDoc/html
-aws s3 sync --delete --exclude ".*" --cache-control "no-cache" ./ s3://flower.dev/docs/ios
+aws s3 sync --delete --exclude ".*" --cache-control "no-cache" ./ s3://flower.ai/docs/ios

From cc36bca021bc581928fbc96f1ebda7e66635b259 Mon Sep 17 00:00:00 2001
From: Taner Topal <taner@flower.dev>
Date: Thu, 15 Feb 2024 22:14:32 +0100
Subject: [PATCH 012/102] Add pytest-watcher instead of unmainted pytest-watch
 (#2943)

---
 datasets/pyproject.toml |  2 +-
 pyproject.toml          | 12 +++++++++++-
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/datasets/pyproject.toml b/datasets/pyproject.toml
index 7145345f7d94..20bd20d121e7 100644
--- a/datasets/pyproject.toml
+++ b/datasets/pyproject.toml
@@ -68,7 +68,7 @@ pylint = "==2.13.9"
 flake8 = "==3.9.2"
 parameterized = "==0.9.0"
 pytest = "==7.1.2"
-pytest-watch = "==4.2.0"
+pytest-watcher = "==0.4.1"
 ruff = "==0.0.277"
 types-requests = "==2.28.11.17"
 
diff --git a/pyproject.toml b/pyproject.toml
index 16688268f7be..297574ef67ed 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -93,7 +93,7 @@ pylint = "==3.0.3"
 flake8 = "==5.0.4"
 pytest = "==7.4.4"
 pytest-cov = "==4.1.0"
-pytest-watch = "==4.2.0"
+pytest-watcher = "==0.4.1"
 grpcio-tools = "==1.60.0"
 mypy-protobuf = "==3.2.0"
 jupyterlab = "==4.0.12"
@@ -147,6 +147,16 @@ testpaths = [
     "src/py/flwr",
     "src/py/flwr_tool",
 ]
+filterwarnings = "ignore::DeprecationWarning"
+
+[tool.pytest-watcher]
+now = false
+clear = true
+delay = 0.2
+runner = "pytest"
+runner_args = ["-s", "-vvvvv"]
+patterns = ["*.py"]
+ignore_patterns = []
 
 [tool.mypy]
 plugins = [

From 44571c61f68f50a5443a085f83fe717779f0200f Mon Sep 17 00:00:00 2001
From: Taner Topal <taner@flower.dev>
Date: Thu, 15 Feb 2024 22:32:48 +0100
Subject: [PATCH 013/102] Update poetry version to latest (#2954)

---
 .devcontainer/Dockerfile                  | 2 +-
 .github/actions/bootstrap/action.yml      | 2 +-
 baselines/flwr_baselines/dev/bootstrap.sh | 2 +-
 dev/bootstrap.sh                          | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index 61c8d14039db..03849995735d 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -31,7 +31,7 @@ RUN apt-get install -y curl wget gnupg python3 python-is-python3 python3-pip git
 RUN python -m pip install \
     pip==23.3.1 \
     setuptools==68.2.2 \
-    poetry==1.5.1
+    poetry==1.7.1
 
 USER $USERNAME
 ENV PATH="/home/$USERNAME/.local/bin:${PATH}"
diff --git a/.github/actions/bootstrap/action.yml b/.github/actions/bootstrap/action.yml
index 5b0c10f53f5e..7b1716bbc954 100644
--- a/.github/actions/bootstrap/action.yml
+++ b/.github/actions/bootstrap/action.yml
@@ -12,7 +12,7 @@ inputs:
     default: 68.2.2
   poetry-version:
     description: "Version of poetry to be installed using pip"
-    default: 1.5.1
+    default: 1.7.1
 outputs:
   python-version:
     description: "Version range or exact version of Python or PyPy"
diff --git a/baselines/flwr_baselines/dev/bootstrap.sh b/baselines/flwr_baselines/dev/bootstrap.sh
index 0bc322edc0de..a2f3080a82f7 100755
--- a/baselines/flwr_baselines/dev/bootstrap.sh
+++ b/baselines/flwr_baselines/dev/bootstrap.sh
@@ -8,7 +8,7 @@ cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
 # Upgrade/install spcific versions of `pip`, `setuptools`, and `poetry`
 python -m pip install -U pip==23.3.1
 python -m pip install -U setuptools==68.2.2
-python -m pip install -U poetry==1.5.1
+python -m pip install -U poetry==1.7.1
 
 # Use `poetry` to install project dependencies
 python -m poetry install
diff --git a/dev/bootstrap.sh b/dev/bootstrap.sh
index 1700c3774767..b4bb82d73b51 100755
--- a/dev/bootstrap.sh
+++ b/dev/bootstrap.sh
@@ -11,7 +11,7 @@ cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
 # Upgrade/install spcific versions of `pip`, `setuptools`, and `poetry`
 python -m pip install -U pip==23.3.1
 python -m pip install -U setuptools==68.2.2
-python -m pip install -U poetry==1.5.1
+python -m pip install -U poetry==1.7.1
 
 # Use `poetry` to install project dependencies
 python -m poetry install --all-extras

From 488fece30ad5785eedf3ade5adc771de093431e8 Mon Sep 17 00:00:00 2001
From: Robert Steiner <robert@flower.dev>
Date: Fri, 16 Feb 2024 11:03:49 +0100
Subject: [PATCH 014/102] Replace the .dev URLs with the news .ai in the docs
 (#2953)

---
 baselines/doc/source/_templates/base.html     |   4 +-
 baselines/doc/source/conf.py                  |   2 +-
 baselines/doc/source/index.rst                |   4 +-
 datasets/doc/source/_templates/base.html      |   2 +-
 datasets/doc/source/conf.py                   |   2 +-
 datasets/doc/source/index.rst                 |   4 +-
 doc/locales/fr/LC_MESSAGES/framework-docs.po  | 274 ++++++++---------
 .../pt_BR/LC_MESSAGES/framework-docs.po       | 148 +++++-----
 .../zh_Hans/LC_MESSAGES/framework-docs.po     | 276 +++++++++---------
 doc/source/_templates/base.html               |   6 +-
 doc/source/_templates/sidebar/lang.html       |   6 +-
 doc/source/_templates/sidebar/versioning.html |   8 +-
 doc/source/conf.py                            |  12 +-
 .../contributor-explanation-architecture.rst  |   6 +-
 ...contributor-how-to-build-docker-images.rst |   2 +-
 ...ributor-how-to-contribute-translations.rst |   4 +-
 ...ntributor-ref-good-first-contributions.rst |   6 +-
 ...tributor-tutorial-contribute-on-github.rst |  52 ++--
 ...-pytorch-from-centralized-to-federated.rst |  12 +-
 doc/source/how-to-configure-clients.rst       |   6 +-
 doc/source/how-to-install-flower.rst          |   2 +-
 doc/source/how-to-run-flower-using-docker.rst |   4 +-
 doc/source/how-to-upgrade-to-flower-1.0.rst   |   4 +-
 doc/source/index.rst                          |   4 +-
 doc/source/ref-changelog.md                   |  28 +-
 doc/source/ref-example-projects.rst           |  10 +-
 doc/source/ref-faq.rst                        |  10 +-
 doc/source/ref-telemetry.md                   |   4 +-
 doc/source/tutorial-quickstart-ios.rst        |  26 +-
 doc/source/tutorial-quickstart-mxnet.rst      |  22 +-
 doc/source/tutorial-quickstart-pytorch.rst    |  22 +-
 .../tutorial-quickstart-scikitlearn.rst       |  12 +-
 doc/source/tutorial-quickstart-xgboost.rst    |   4 +-
 ...-series-customize-the-client-pytorch.ipynb |   4 +-
 ...ries-get-started-with-flower-pytorch.ipynb |   4 +-
 ...-federated-learning-strategy-pytorch.ipynb |   6 +-
 examples/doc/source/_templates/base.html      |   4 +-
 examples/doc/source/conf.py                   |   2 +-
 38 files changed, 505 insertions(+), 503 deletions(-)

diff --git a/baselines/doc/source/_templates/base.html b/baselines/doc/source/_templates/base.html
index 1cee99053fbe..cc171be9e6b0 100644
--- a/baselines/doc/source/_templates/base.html
+++ b/baselines/doc/source/_templates/base.html
@@ -5,7 +5,7 @@
     <meta charset="utf-8"/>
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark">
-    <link rel="canonical" href="https://flower.dev/docs/baselines/{{ pagename }}.html">
+    <link rel="canonical" href="https://flower.ai/docs/baselines/{{ pagename }}.html">
 
     {%- if metatags %}{{ metatags }}{% endif -%}
 
@@ -99,6 +99,6 @@
     {%- endblock -%}
 
     {%- endblock scripts -%}
-    <script defer data-domain="flower.dev" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
+    <script defer data-domain="flower.ai" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
   </body>
 </html>
diff --git a/baselines/doc/source/conf.py b/baselines/doc/source/conf.py
index dabd421c61cf..dad8650cddaa 100644
--- a/baselines/doc/source/conf.py
+++ b/baselines/doc/source/conf.py
@@ -85,7 +85,7 @@
 html_title = f"Flower Baselines {release}"
 html_logo = "_static/flower-logo.png"
 html_favicon = "_static/favicon.ico"
-html_baseurl = "https://flower.dev/docs/baselines/"
+html_baseurl = "https://flower.ai/docs/baselines/"
 
 html_theme_options = {
     #
diff --git a/baselines/doc/source/index.rst b/baselines/doc/source/index.rst
index 335cfacef1ab..3a19e74b891e 100644
--- a/baselines/doc/source/index.rst
+++ b/baselines/doc/source/index.rst
@@ -1,7 +1,7 @@
 Flower Baselines Documentation
 ==============================
 
-Welcome to Flower Baselines' documentation. `Flower <https://flower.dev>`_ is a friendly federated learning framework.
+Welcome to Flower Baselines' documentation. `Flower <https://flower.ai>`_ is a friendly federated learning framework.
 
 
 Join the Flower Community
@@ -9,7 +9,7 @@ Join the Flower Community
 
 The Flower Community is growing quickly - we're a friendly group of researchers, engineers, students, professionals, academics, and other enthusiasts.
 
-.. button-link:: https://flower.dev/join-slack
+.. button-link:: https://flower.ai/join-slack
     :color: primary
     :shadow:
 
diff --git a/datasets/doc/source/_templates/base.html b/datasets/doc/source/_templates/base.html
index 1d2e417ccc8a..4bc72e03ae24 100644
--- a/datasets/doc/source/_templates/base.html
+++ b/datasets/doc/source/_templates/base.html
@@ -98,6 +98,6 @@
     {%- endblock -%}
 
     {%- endblock scripts -%}
-    <script defer data-domain="flower.dev" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
+    <script defer data-domain="flower.ai" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
   </body>
 </html>
diff --git a/datasets/doc/source/conf.py b/datasets/doc/source/conf.py
index 53d7046bfaac..e5c61b5559cb 100644
--- a/datasets/doc/source/conf.py
+++ b/datasets/doc/source/conf.py
@@ -123,7 +123,7 @@ def find_test_modules(package_path):
 html_title = f"Flower Datasets {release}"
 html_logo = "_static/flower-datasets-logo.png"
 html_favicon = "_static/favicon.ico"
-html_baseurl = "https://flower.dev/docs/datasets/"
+html_baseurl = "https://flower.ai/docs/datasets/"
 
 html_theme_options = {
     #
diff --git a/datasets/doc/source/index.rst b/datasets/doc/source/index.rst
index ae7e7259f504..fd226b308bd5 100644
--- a/datasets/doc/source/index.rst
+++ b/datasets/doc/source/index.rst
@@ -2,7 +2,7 @@ Flower Datasets
 ===============
 
 Flower Datasets (``flwr-datasets``) is a library to quickly and easily create datasets for federated
-learning/analytics/evaluation. It is created by the ``Flower Labs`` team that also created `Flower <https://flower.dev>`_ - a Friendly Federated Learning Framework.
+learning/analytics/evaluation. It is created by the ``Flower Labs`` team that also created `Flower <https://flower.ai>`_ - a Friendly Federated Learning Framework.
 
 Flower Datasets Framework
 -------------------------
@@ -98,7 +98,7 @@ Join the Flower Community
 
 The Flower Community is growing quickly - we're a friendly group of researchers, engineers, students, professionals, academics, and other enthusiasts.
 
-.. button-link:: https://flower.dev/join-slack
+.. button-link:: https://flower.ai/join-slack
     :color: primary
     :shadow:
 
diff --git a/doc/locales/fr/LC_MESSAGES/framework-docs.po b/doc/locales/fr/LC_MESSAGES/framework-docs.po
index 6c8cf7e88486..920a47abab3b 100644
--- a/doc/locales/fr/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/fr/LC_MESSAGES/framework-docs.po
@@ -25,10 +25,10 @@ msgstr "Moteur client Edge"
 
 #: ../../source/contributor-explanation-architecture.rst:7
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with Edge "
+"`Flower <https://flower.ai>`_ core framework architecture with Edge "
 "Client Engine"
 msgstr ""
-"`Flower <https://flower.dev>`_ architecture de base avec Edge Client "
+"`Flower <https://flower.ai>`_ architecture de base avec Edge Client "
 "Engine"
 
 #: ../../source/contributor-explanation-architecture.rst:13
@@ -37,10 +37,10 @@ msgstr "Moteur de client virtuel"
 
 #: ../../source/contributor-explanation-architecture.rst:15
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with Virtual "
+"`Flower <https://flower.ai>`_ core framework architecture with Virtual "
 "Client Engine"
 msgstr ""
-"`Flower <https://flower.dev>`_ architecture de base avec moteur de client"
+"`Flower <https://flower.ai>`_ architecture de base avec moteur de client"
 " virtuel"
 
 #: ../../source/contributor-explanation-architecture.rst:21
@@ -49,10 +49,10 @@ msgstr "Moteur client virtuel et moteur client Edge dans la même charge de trav
 
 #: ../../source/contributor-explanation-architecture.rst:23
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with both "
+"`Flower <https://flower.ai>`_ core framework architecture with both "
 "Virtual Client Engine and Edge Client Engine"
 msgstr ""
-"`Flower <https://flower.dev>`_ architecture de base avec un moteur de "
+"`Flower <https://flower.ai>`_ architecture de base avec un moteur de "
 "client virtuel et un moteur de client périphérique"
 
 #: ../../source/contributor-how-to-build-docker-images.rst:2
@@ -87,7 +87,7 @@ msgstr ""
 #: ../../source/contributor-how-to-build-docker-images.rst:19
 msgid ""
 "Please follow the first section on `Run Flower using Docker "
-"<https://flower.dev/docs/framework/how-to-run-flower-using-docker>`_ "
+"<https://flower.ai/docs/framework/how-to-run-flower-using-docker>`_ "
 "which covers this step in more detail."
 msgstr ""
 
@@ -296,7 +296,7 @@ msgstr "Explications"
 
 #: ../../source/contributor-how-to-contribute-translations.rst:4
 msgid ""
-"Since `Flower 1.5 <https://flower.dev/docs/framework/ref-"
+"Since `Flower 1.5 <https://flower.ai/docs/framework/ref-"
 "changelog.html#v1-5-0-2023-08-31>`_ we have introduced translations to "
 "our doc pages, but, as you might have noticed, the translations are often"
 " imperfect. If you speak languages other than English, you might be able "
@@ -393,7 +393,7 @@ msgstr ""
 #: ../../source/contributor-how-to-contribute-translations.rst:69
 msgid ""
 "If you want to add a new language, you will first have to contact us, "
-"either on `Slack <https://flower.dev/join-slack>`_, or by opening an "
+"either on `Slack <https://flower.ai/join-slack>`_, or by opening an "
 "issue on our `GitHub repo <https://github.com/adap/flower>`_."
 msgstr ""
 
@@ -1320,12 +1320,12 @@ msgstr "Demande pour une nouvelle Flower Baseline"
 #: ../../source/contributor-ref-good-first-contributions.rst:25
 msgid ""
 "If you are not familiar with Flower Baselines, you should probably check-"
-"out our `contributing guide for baselines <https://flower.dev/docs"
+"out our `contributing guide for baselines <https://flower.ai/docs"
 "/contributing-baselines.html>`_."
 msgstr ""
 "Si tu n'es pas familier avec les Flower Baselines, tu devrais "
 "probablement consulter notre `guide de contribution pour les baselines "
-"<https://flower.dev/docs/contributing-baselines.html>`_."
+"<https://flower.ai/docs/contributing-baselines.html>`_."
 
 #: ../../source/contributor-ref-good-first-contributions.rst:27
 msgid ""
@@ -1445,14 +1445,14 @@ msgstr ""
 msgid ""
 "If you're familiar with how contributing on GitHub works, you can "
 "directly checkout our `getting started guide for contributors "
-"<https://flower.dev/docs/contributor-tutorial-get-started-"
+"<https://flower.ai/docs/contributor-tutorial-get-started-"
 "as-a-contributor.html>`_."
 msgstr ""
 "Si tu es familier avec le fonctionnement des contributions sur GitHub, tu"
 " peux directement consulter notre `guide de démarrage pour les "
-"contributeurs <https://flower.dev/docs/getting-started-for-"
+"contributeurs <https://flower.ai/docs/getting-started-for-"
 "contributors.html>`_ et des exemples de `bonnes premières contributions "
-"<https://flower.dev/docs/good-first-contributions.html>`_."
+"<https://flower.ai/docs/good-first-contributions.html>`_."
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:11
 msgid "Setting up the repository"
@@ -2052,11 +2052,11 @@ msgstr ""
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:280
 msgid ""
-"Build the docs and check the result: `<https://flower.dev/docs/writing-"
+"Build the docs and check the result: `<https://flower.ai/docs/writing-"
 "documentation.html#edit-an-existing-page>`_"
 msgstr ""
 "Construis les documents et vérifie le résultat : "
-"`<https://flower.dev/docs/writing-documentation.html#edit-an-existing-"
+"`<https://flower.ai/docs/writing-documentation.html#edit-an-existing-"
 "page>`_"
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:283
@@ -2263,11 +2263,11 @@ msgstr ""
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:346
 msgid ""
-"`Good first contributions <https://flower.dev/docs/framework/contributor-"
+"`Good first contributions <https://flower.ai/docs/framework/contributor-"
 "ref-good-first-contributions.html>`_, where you should particularly look "
 "into the :code:`baselines` contributions."
 msgstr ""
-"`Bonnes premières contributions <https://flower.dev/docs/framework"
+"`Bonnes premières contributions <https://flower.ai/docs/framework"
 "/contributor-ref-good-first-contributions.html>`_, où vous devriez "
 "particulièrement regarder les contributions :code:`baselines`."
 
@@ -2646,7 +2646,7 @@ msgid ""
 "designed for non-iid data. We are using PyTorch to train a Convolutional "
 "Neural Network(with Batch Normalization layers) on the CIFAR-10 dataset. "
 "When applying FedBN, only few changes needed compared to `Example: "
-"PyTorch - From Centralized To Federated <https://flower.dev/docs/examples"
+"PyTorch - From Centralized To Federated <https://flower.ai/docs/examples"
 "/pytorch-from-centralized-to-federated.html>`_."
 msgstr ""
 "Ce tutoriel te montrera comment utiliser Flower pour construire une "
@@ -2657,7 +2657,7 @@ msgstr ""
 "couches de normalisation par lots) sur l'ensemble de données CIFAR-10. "
 "Lors de l'application de FedBN, seules quelques modifications sont "
 "nécessaires par rapport à `Exemple : PyTorch - De la centralisation à la "
-"fédération <https://flower.dev/docs/example-pytorch-from-centralized-to-"
+"fédération <https://flower.ai/docs/example-pytorch-from-centralized-to-"
 "federated.html>`_."
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:9
@@ -2669,12 +2669,12 @@ msgstr "Formation centralisée"
 #, fuzzy
 msgid ""
 "All files are revised based on `Example: PyTorch - From Centralized To "
-"Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-"
+"Federated <https://flower.ai/docs/examples/pytorch-from-centralized-to-"
 "federated.html>`_. The only thing to do is modifying the file called "
 ":code:`cifar.py`, revised part is shown below:"
 msgstr ""
 "Tous les fichiers sont révisés sur la base de `Exemple : PyTorch - From "
-"Centralized To Federated <https://flower.dev/docs/example-pytorch-from-"
+"Centralized To Federated <https://flower.ai/docs/example-pytorch-from-"
 "centralized-to-federated.html>`_. La seule chose à faire est de modifier "
 "le fichier appelé :code:`cifar.py`, la partie révisée est montrée ci-"
 "dessous :"
@@ -2715,20 +2715,20 @@ msgstr "Formation fédérée"
 #, fuzzy
 msgid ""
 "If you have read `Example: PyTorch - From Centralized To Federated "
-"<https://flower.dev/docs/examples/pytorch-from-centralized-to-"
+"<https://flower.ai/docs/examples/pytorch-from-centralized-to-"
 "federated.html>`_, the following parts are easy to follow, onyl "
 ":code:`get_parameters` and :code:`set_parameters` function in "
 ":code:`client.py` needed to revise. If not, please read the `Example: "
-"PyTorch - From Centralized To Federated <https://flower.dev/docs/examples"
+"PyTorch - From Centralized To Federated <https://flower.ai/docs/examples"
 "/pytorch-from-centralized-to-federated.html>`_. first."
 msgstr ""
 "Si vous avez lu `Exemple : PyTorch - From Centralized To Federated "
-"<https://flower.dev/docs/example-pytorch-from-centralized-to-"
+"<https://flower.ai/docs/example-pytorch-from-centralized-to-"
 "federated.html>`_, les parties suivantes sont faciles à suivre, seules "
 "les fonctions :code:`get_parameters` et :code:`set_parameters` dans "
 ":code:`client.py` ont besoin d'être révisées. Si ce n'est pas le cas, "
 "veuillez lire `Exemple : PyTorch - From Centralized To Federated "
-"<https://flower.dev/docs/example-pytorch-from-centralized-to-"
+"<https://flower.ai/docs/example-pytorch-from-centralized-to-"
 "federated.html>`. d'abord."
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:56
@@ -5599,7 +5599,7 @@ msgstr ""
 msgid ""
 "This can be achieved by customizing an existing strategy or by "
 "`implementing a custom strategy from scratch "
-"<https://flower.dev/docs/framework/how-to-implement-strategies.html>`_. "
+"<https://flower.ai/docs/framework/how-to-implement-strategies.html>`_. "
 "Here's a nonsensical example that customizes :code:`FedAvg` by adding a "
 "custom ``\"hello\": \"world\"`` configuration key/value pair to the "
 "config dict of a *single client* (only the first client in the list, the "
@@ -5608,7 +5608,7 @@ msgid ""
 msgstr ""
 "Ceci peut être réalisé en personnalisant une stratégie existante ou en "
 "`mettant en œuvre une stratégie personnalisée à partir de zéro "
-"<https://flower.dev/docs/framework/how-to-implement-strategies.html>`_. "
+"<https://flower.ai/docs/framework/how-to-implement-strategies.html>`_. "
 "Voici un exemple absurde qui personnalise :code:`FedAvg` en ajoutant une "
 "paire clé/valeur de configuration personnalisée ``\"hello\" : \"world\"``"
 " au config dict d'un *seul client* (uniquement le premier client de la "
@@ -6335,7 +6335,7 @@ msgstr "Installer Flower"
 
 #: ../../source/how-to-install-flower.rst:60
 msgid ""
-"`How to run Flower using Docker <https://flower.dev/docs/framework/how-"
+"`How to run Flower using Docker <https://flower.ai/docs/framework/how-"
 "to-run-flower-using-docker.html>`_"
 msgstr ""
 
@@ -6780,7 +6780,7 @@ msgstr ""
 msgid ""
 "The ``--insecure`` flag enables insecure communication (using HTTP, not "
 "HTTPS) and should only be used for testing purposes. We strongly "
-"recommend enabling `SSL <https://flower.dev/docs/framework/how-to-run-"
+"recommend enabling `SSL <https://flower.ai/docs/framework/how-to-run-"
 "flower-using-docker.html#enabling-ssl-for-secure-connections>`_ when "
 "deploying to a production environment."
 msgstr ""
@@ -6828,7 +6828,7 @@ msgstr ""
 #: ../../source/how-to-run-flower-using-docker.rst:92
 msgid ""
 "For testing purposes, you can generate your own self-signed certificates."
-" The `Enable SSL connections <https://flower.dev/docs/framework/how-to-"
+" The `Enable SSL connections <https://flower.ai/docs/framework/how-to-"
 "enable-ssl-connections.html#certificates>`_ page contains a section that "
 "will guide you through the process."
 msgstr ""
@@ -7722,13 +7722,13 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples>`_ are already updated"
 " to Flower 1.0, they can serve as a reference for using the Flower 1.0 "
 "API. If there are further questionsm, `join the Flower Slack "
-"<https://flower.dev/join-slack/>`_ and use the channgel ``#questions``."
+"<https://flower.ai/join-slack/>`_ and use the channgel ``#questions``."
 msgstr ""
 "La plupart des `exemples de code Flower officiels "
 "<https://github.com/adap/flower/tree/main/examples>`_ sont déjà mis à "
 "jour vers Flower 1.0, ils peuvent servir de référence pour l'utilisation "
 "de l'API Flower 1.0. Si vous avez d'autres questions, `joins le Slack "
-"Flower <https://flower.dev/join-slack/>`_ et utilise le canal "
+"Flower <https://flower.ai/join-slack/>`_ et utilise le canal "
 "``#questions``."
 
 #: ../../source/how-to-use-built-in-mods.rst:2
@@ -8053,10 +8053,10 @@ msgstr "Rédiger de la documentation"
 
 #: ../../source/index.rst:7
 msgid ""
-"Welcome to Flower's documentation. `Flower <https://flower.dev>`_ is a "
+"Welcome to Flower's documentation. `Flower <https://flower.ai>`_ is a "
 "friendly federated learning framework."
 msgstr ""
-"Bienvenue sur la documentation de Flower. `Flower <https://flower.dev>`_ "
+"Bienvenue sur la documentation de Flower. `Flower <https://flower.ai>`_ "
 "est un framework de federated learning convivial et facile à utiliser."
 
 #: ../../source/index.rst:11
@@ -12748,7 +12748,7 @@ msgstr ""
 msgid ""
 "The Flower server can now be run using an official Docker image. A new "
 "how-to guide explains [how to run Flower using "
-"Docker](https://flower.dev/docs/framework/how-to-run-flower-using-"
+"Docker](https://flower.ai/docs/framework/how-to-run-flower-using-"
 "docker.html). An official Flower client Docker image will follow."
 msgstr ""
 
@@ -12797,7 +12797,7 @@ msgstr ""
 #: ../../source/ref-changelog.md:55
 msgid ""
 "Several code examples were updated to use [Flower "
-"Datasets](https://flower.dev/docs/datasets/)."
+"Datasets](https://flower.ai/docs/datasets/)."
 msgstr ""
 
 #: ../../source/ref-changelog.md:57
@@ -13592,10 +13592,10 @@ msgstr ""
 #: ../../source/ref-changelog.md:216
 msgid ""
 "Comprehensive documentation includes a new [how-to run "
-"simulations](https://flower.dev/docs/framework/how-to-run-"
+"simulations](https://flower.ai/docs/framework/how-to-run-"
 "simulations.html) guide, new [simulation-"
-"pytorch](https://flower.dev/docs/examples/simulation-pytorch.html) and "
-"[simulation-tensorflow](https://flower.dev/docs/examples/simulation-"
+"pytorch](https://flower.ai/docs/examples/simulation-pytorch.html) and "
+"[simulation-tensorflow](https://flower.ai/docs/examples/simulation-"
 "tensorflow.html) notebooks, and a new [YouTube tutorial "
 "series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)."
 msgstr ""
@@ -13635,7 +13635,7 @@ msgstr ""
 #: ../../source/ref-changelog.md:220
 msgid ""
 "Much effort went into a completely restructured Flower docs experience. "
-"The documentation on [flower.dev/docs](flower.dev/docs) is now divided "
+"The documentation on [flower.ai/docs](flower.ai/docs) is now divided "
 "into Flower Framework, Flower Baselines, Flower Android SDK, Flower iOS "
 "SDK, and code example projects."
 msgstr ""
@@ -14028,14 +14028,14 @@ msgstr ""
 #: ../../source/ref-changelog.md:306
 #, fuzzy
 msgid ""
-"A new [entry-level tutorial](https://flower.dev/docs/framework/tutorial-"
+"A new [entry-level tutorial](https://flower.ai/docs/framework/tutorial-"
 "what-is-federated-learning.html) in our documentation explains the basics"
 " of Fedetated Learning. It enables anyone who's unfamiliar with Federated"
 " Learning to start their journey with Flower. Forward it to anyone who's "
 "interested in Federated Learning!"
 msgstr ""
 "Un nouveau [tutoriel d'entrée de gamme] "
-"(https://flower.dev/docs/tutorial/Flower-0-What-is-FL.html) dans notre "
+"(https://flower.ai/docs/tutorial/Flower-0-What-is-FL.html) dans notre "
 "documentation explique les bases de l'apprentissage fédéré. Il permet à "
 "tous ceux qui ne connaissent pas l'apprentissage fédéré de commencer leur"
 " voyage avec Flower. Fais-le suivre à tous ceux qui s'intéressent à "
@@ -14633,7 +14633,7 @@ msgid ""
 "revisit well known papers from the literature, and be suitable for "
 "integration in your own application or for experimentation, in order to "
 "deepen your knowledge of FL in general. Today's release is the first in "
-"this series. [Read more.](https://flower.dev/blog/2023-01-12-fl-starter-"
+"this series. [Read more.](https://flower.ai/blog/2023-01-12-fl-starter-"
 "pack-fedavg-mnist-cnn/)"
 msgstr ""
 "Au cours des prochaines semaines, nous publierons un certain nombre de "
@@ -14643,7 +14643,7 @@ msgstr ""
 "propre application ou à l'expérimentation, afin d'approfondir votre "
 "connaissance de FL en général. La publication d'aujourd'hui est la "
 "première de cette série. [Lire la "
-"suite.](https://flower.dev/blog/2023-01-12-fl-starter-pack-fedavg-mnist-"
+"suite.](https://flower.ai/blog/2023-01-12-fl-starter-pack-fedavg-mnist-"
 "cnn/)"
 
 #: ../../source/ref-changelog.md:422
@@ -14692,38 +14692,38 @@ msgstr ""
 #: ../../source/ref-changelog.md:430
 #, fuzzy
 msgid ""
-"[An Introduction to Federated Learning](https://flower.dev/docs/framework"
+"[An Introduction to Federated Learning](https://flower.ai/docs/framework"
 "/tutorial-get-started-with-flower-pytorch.html)"
 msgstr ""
 "[Une introduction à l'apprentissage fédéré] "
-"(https://flower.dev/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html)"
+"(https://flower.ai/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html)"
 
 #: ../../source/ref-changelog.md:431
 #, fuzzy
 msgid ""
-"[Strategies in Federated Learning](https://flower.dev/docs/framework"
+"[Strategies in Federated Learning](https://flower.ai/docs/framework"
 "/tutorial-use-a-federated-learning-strategy-pytorch.html)"
 msgstr ""
 "[Stratégies d'apprentissage fédéré] "
-"(https://flower.dev/docs/tutorial/Flower-2-Strategies-in-FL-PyTorch.html)"
+"(https://flower.ai/docs/tutorial/Flower-2-Strategies-in-FL-PyTorch.html)"
 
 #: ../../source/ref-changelog.md:432
 #, fuzzy
 msgid ""
-"[Building a Strategy](https://flower.dev/docs/framework/tutorial-build-a"
+"[Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a"
 "-strategy-from-scratch-pytorch.html)"
 msgstr ""
 "[Construire une stratégie] "
-"(https://flower.dev/docs/tutorial/Flower-3-Building-a-Strategy-"
+"(https://flower.ai/docs/tutorial/Flower-3-Building-a-Strategy-"
 "PyTorch.html)"
 
 #: ../../source/ref-changelog.md:433
 #, fuzzy
 msgid ""
-"[Client and NumPyClient](https://flower.dev/docs/framework/tutorial-"
+"[Client and NumPyClient](https://flower.ai/docs/framework/tutorial-"
 "customize-the-client-pytorch.html)"
 msgstr ""
-"[Client et NumPyClient] (https://flower.dev/docs/tutorial/Flower-4"
+"[Client et NumPyClient] (https://flower.ai/docs/tutorial/Flower-4"
 "-Client-and-NumPyClient-PyTorch.html)"
 
 #: ../../source/ref-changelog.md:435
@@ -14761,13 +14761,13 @@ msgid ""
 "**Flower is a friendly framework for collaborative AI and data science.**"
 " Staying true to this statement, Flower makes it easy to disable "
 "telemetry for users who do not want to share anonymous usage metrics. "
-"[Read more.](https://flower.dev/docs/telemetry.html)."
+"[Read more.](https://flower.ai/docs/telemetry.html)."
 msgstr ""
 "**Flower est un cadre convivial pour l'IA collaborative et la science des"
 " données.** Restant fidèle à cette déclaration, Flower permet de "
 "désactiver facilement la télémétrie pour les utilisateurs qui ne "
 "souhaitent pas partager des métriques d'utilisation anonymes.[Lire la "
-"suite.](https://flower.dev/docs/telemetry.html)."
+"suite.](https://flower.ai/docs/telemetry.html)."
 
 #: ../../source/ref-changelog.md:441
 msgid ""
@@ -14945,11 +14945,11 @@ msgstr ""
 #: ../../source/ref-changelog.md:471
 msgid ""
 "One highlight is the new [first time contributor "
-"guide](https://flower.dev/docs/first-time-contributors.html): if you've "
+"guide](https://flower.ai/docs/first-time-contributors.html): if you've "
 "never contributed on GitHub before, this is the perfect place to start!"
 msgstr ""
 "L'un des points forts est le nouveau [guide du premier contributeur] "
-"(https://flower.dev/docs/first-time-contributors.html) : si tu n'as "
+"(https://flower.ai/docs/first-time-contributors.html) : si tu n'as "
 "jamais contribué sur GitHub auparavant, c'est l'endroit idéal pour "
 "commencer !"
 
@@ -15851,18 +15851,18 @@ msgid ""
 "The first preview release of Flower Baselines has arrived! We're "
 "kickstarting Flower Baselines with implementations of FedOpt (FedYogi, "
 "FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how "
-"to use [Flower Baselines](https://flower.dev/docs/using-baselines.html). "
+"to use [Flower Baselines](https://flower.ai/docs/using-baselines.html). "
 "With this first preview release we're also inviting the community to "
-"[contribute their own baselines](https://flower.dev/docs/contributing-"
+"[contribute their own baselines](https://flower.ai/docs/contributing-"
 "baselines.html)."
 msgstr ""
 "La première version préliminaire de Flower Baselines est arrivée ! Nous "
 "démarrons Flower Baselines avec des implémentations de FedOpt (FedYogi, "
 "FedAdam, FedAdagrad), FedBN, et FedAvgM. Consultez la documentation sur "
-"l'utilisation de [Flower Baselines](https://flower.dev/docs/using-"
+"l'utilisation de [Flower Baselines](https://flower.ai/docs/using-"
 "baselines.html). Avec cette première version préliminaire, nous invitons "
 "également la communauté à [contribuer à leurs propres lignes de "
-"base](https://flower.dev/docs/contributing-baselines.html)."
+"base](https://flower.ai/docs/contributing-baselines.html)."
 
 #: ../../source/ref-changelog.md:662
 msgid ""
@@ -16071,12 +16071,12 @@ msgstr ""
 #, fuzzy
 msgid ""
 "New documentation for [implementing "
-"strategies](https://flower.dev/docs/framework/how-to-implement-"
+"strategies](https://flower.ai/docs/framework/how-to-implement-"
 "strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), "
 "[#1175](https://github.com/adap/flower/pull/1175))"
 msgstr ""
 "Nouvelle documentation pour [mettre en œuvre des "
-"stratégies](https://flower.dev/docs/framework/how-to-implement-"
+"stratégies](https://flower.ai/docs/framework/how-to-implement-"
 "strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), "
 "[#1175](https://github.com/adap/flower/pull/1175))"
 
@@ -17295,18 +17295,18 @@ msgstr ""
 #: ../../source/ref-example-projects.rst:26
 #, fuzzy
 msgid ""
-"`Quickstart TensorFlow (Tutorial) <https://flower.dev/docs/framework"
+"`Quickstart TensorFlow (Tutorial) <https://flower.ai/docs/framework"
 "/tutorial-quickstart-tensorflow.html>`_"
 msgstr ""
-"`Quickstart TensorFlow (Tutorial) <https://flower.dev/docs/quickstart-"
+"`Quickstart TensorFlow (Tutorial) <https://flower.ai/docs/quickstart-"
 "tensorflow.html>`_"
 
 #: ../../source/ref-example-projects.rst:27
 msgid ""
-"`Quickstart TensorFlow (Blog Post) <https://flower.dev/blog/2020-12-11"
+"`Quickstart TensorFlow (Blog Post) <https://flower.ai/blog/2020-12-11"
 "-federated-learning-in-less-than-20-lines-of-code>`_"
 msgstr ""
-"`Quickstart TensorFlow (Blog Post) <https://flower.dev/blog/2020-12-11"
+"`Quickstart TensorFlow (Blog Post) <https://flower.ai/blog/2020-12-11"
 "-federated-learning-in-less-than-20-lines-of-code>`_"
 
 #: ../../source/ref-example-projects.rst:31
@@ -17334,10 +17334,10 @@ msgstr ""
 #: ../../source/ref-example-projects.rst:37
 #, fuzzy
 msgid ""
-"`Quickstart PyTorch (Tutorial) <https://flower.dev/docs/framework"
+"`Quickstart PyTorch (Tutorial) <https://flower.ai/docs/framework"
 "/tutorial-quickstart-pytorch.html>`_"
 msgstr ""
-"`Quickstart PyTorch (Tutorial) <https://flower.dev/docs/quickstart-"
+"`Quickstart PyTorch (Tutorial) <https://flower.ai/docs/quickstart-"
 "pytorch.html>`_"
 
 #: ../../source/ref-example-projects.rst:41
@@ -17367,11 +17367,11 @@ msgstr ""
 #, fuzzy
 msgid ""
 "`PyTorch: From Centralized To Federated (Tutorial) "
-"<https://flower.dev/docs/framework/example-pytorch-from-centralized-to-"
+"<https://flower.ai/docs/framework/example-pytorch-from-centralized-to-"
 "federated.html>`_"
 msgstr ""
 "`PyTorch : De la centralisation à la fédération (Tutoriel) "
-"<https://flower.dev/docs/example-pytorch-from-centralized-to-"
+"<https://flower.ai/docs/example-pytorch-from-centralized-to-"
 "federated.html>`_"
 
 #: ../../source/ref-example-projects.rst:50
@@ -17399,10 +17399,10 @@ msgstr ""
 #: ../../source/ref-example-projects.rst:55
 msgid ""
 "`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 msgstr ""
 "`L'apprentissage fédéré sur Raspberry Pi et Nvidia Jetson (Blog Post) "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 
 #: ../../source/ref-example-projects.rst:60
 msgid "Legacy Examples (`flwr_example`)"
@@ -17633,12 +17633,12 @@ msgstr ""
 #, fuzzy
 msgid ""
 "Find the `blog post about federated learning on embedded device here "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 " and the corresponding `GitHub code example "
 "<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_."
 msgstr ""
 "Trouve le `blog post about federated learning on embedded device ici "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 " et l'exemple de code GitHub correspondant "
 "<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_."
 
@@ -17652,22 +17652,22 @@ msgstr ""
 #, fuzzy
 msgid ""
 "Yes, it does. Please take a look at our `blog post "
-"<https://flower.dev/blog/2021-12-15-federated-learning-on-android-"
+"<https://flower.ai/blog/2021-12-15-federated-learning-on-android-"
 "devices-with-flower>`_ or check out the code examples:"
 msgstr ""
 "Oui. Jetez un coup d'œil à notre `blog post "
-"<https://flower.dev/blog/2021-12-15-federated-learning-on-android-"
+"<https://flower.ai/blog/2021-12-15-federated-learning-on-android-"
 "devices-with-flower>`_ ou consultez l'`exemple de code Android sur GitHub"
 " <https://github.com/adap/flower/tree/main/examples/android>`_."
 
 #: ../../source/ref-faq.rst:21
 msgid ""
-"`Android Kotlin example <https://flower.dev/docs/examples/android-"
+"`Android Kotlin example <https://flower.ai/docs/examples/android-"
 "kotlin.html>`_"
 msgstr ""
 
 #: ../../source/ref-faq.rst:22
-msgid "`Android Java example <https://flower.dev/docs/examples/android.html>`_"
+msgid "`Android Java example <https://flower.ai/docs/examples/android.html>`_"
 msgstr ""
 
 #: ../../source/ref-faq.rst
@@ -17932,13 +17932,13 @@ msgstr ""
 msgid ""
 "You may delete the source ID at any time. If you wish for all events "
 "logged under a specific source ID to be deleted, you can send a deletion "
-"request mentioning the source ID to `telemetry@flower.dev`. All events "
+"request mentioning the source ID to `telemetry@flower.ai`. All events "
 "related to that source ID will then be permanently deleted."
 msgstr ""
 "Tu peux supprimer l'identifiant de la source à tout moment. Si tu "
 "souhaites que tous les événements enregistrés sous un identifiant de "
 "source spécifique soient supprimés, tu peux envoyer une demande de "
-"suppression mentionnant l'identifiant de source à `telemetry@flower.dev`."
+"suppression mentionnant l'identifiant de source à `telemetry@flower.ai`."
 " Tous les événements liés à cet identifiant de source seront alors "
 "définitivement supprimés."
 
@@ -18005,13 +18005,13 @@ msgstr "Comment nous contacter"
 msgid ""
 "We want to hear from you. If you have any feedback or ideas on how to "
 "improve the way we handle anonymous usage metrics, reach out to us via "
-"[Slack](https://flower.dev/join-slack/) (channel `#telemetry`) or email "
-"(`telemetry@flower.dev`)."
+"[Slack](https://flower.ai/join-slack/) (channel `#telemetry`) or email "
+"(`telemetry@flower.ai`)."
 msgstr ""
 "Si tu as des commentaires ou des idées pour améliorer la façon dont nous "
 "traitons les mesures d'utilisation anonymes, contacte-nous via "
-"[Slack](https://flower.dev/join-slack/) (canal `#telemetry`) ou par "
-"courriel (`telemetry@flower.dev`)."
+"[Slack](https://flower.ai/join-slack/) (canal `#telemetry`) ou par "
+"courriel (`telemetry@flower.ai`)."
 
 #: ../../source/tutorial-quickstart-android.rst:-1
 msgid ""
@@ -18305,11 +18305,11 @@ msgstr ""
 msgid ""
 "First of all, for running the Flower Python server, it is recommended to "
 "create a virtual environment and run everything within a `virtualenv "
-"<https://flower.dev/docs/recommended-env-setup.html>`_. For the Flower "
+"<https://flower.ai/docs/recommended-env-setup.html>`_. For the Flower "
 "client implementation in iOS, it is recommended to use Xcode as our IDE."
 msgstr ""
 "Tout d'abord, il est recommandé de créer un environnement virtuel et de "
-"tout exécuter au sein d'un `virtualenv <https://flower.dev/docs"
+"tout exécuter au sein d'un `virtualenv <https://flower.ai/docs"
 "/recommended-env-setup.html>`_."
 
 #: ../../source/tutorial-quickstart-ios.rst:15
@@ -18519,11 +18519,11 @@ msgstr ""
 #: ../../source/tutorial-quickstart-scikitlearn.rst:12
 msgid ""
 "It is recommended to create a virtual environment and run everything "
-"within this `virtualenv <https://flower.dev/docs/recommended-env-"
+"within this `virtualenv <https://flower.ai/docs/recommended-env-"
 "setup.html>`_."
 msgstr ""
 "Il est recommandé de créer un environnement virtuel et de tout exécuter "
-"dans ce `virtualenv <https://flower.dev/docs/recommended-env-"
+"dans ce `virtualenv <https://flower.ai/docs/recommended-env-"
 "setup.html>`_."
 
 #: ../../source/tutorial-quickstart-mxnet.rst:18
@@ -18851,11 +18851,11 @@ msgstr ""
 #: ../../source/tutorial-quickstart-xgboost.rst:39
 msgid ""
 "First of all, it is recommended to create a virtual environment and run "
-"everything within a `virtualenv <https://flower.dev/docs/recommended-env-"
+"everything within a `virtualenv <https://flower.ai/docs/recommended-env-"
 "setup.html>`_."
 msgstr ""
 "Tout d'abord, il est recommandé de créer un environnement virtuel et de "
-"tout exécuter au sein d'un `virtualenv <https://flower.dev/docs"
+"tout exécuter au sein d'un `virtualenv <https://flower.ai/docs"
 "/recommended-env-setup.html>`_."
 
 #: ../../source/tutorial-quickstart-pytorch.rst:29
@@ -19758,7 +19758,7 @@ msgid ""
 "comprehensive>`_), we provide more options to define various experimental"
 " setups, including aggregation strategies, data partitioning and "
 "centralised/distributed evaluation. We also support `Flower simulation "
-"<https://flower.dev/docs/framework/how-to-run-simulations.html>`_ making "
+"<https://flower.ai/docs/framework/how-to-run-simulations.html>`_ making "
 "it easy to simulate large client cohorts in a resource-aware manner. "
 "Let's take a look!"
 msgstr ""
@@ -19972,32 +19972,32 @@ msgstr "Élaborer une stratégie à partir de zéro"
 msgid ""
 "Welcome to the third part of the Flower federated learning tutorial. In "
 "previous parts of this tutorial, we introduced federated learning with "
-"PyTorch and Flower (`part 1 <https://flower.dev/docs/framework/tutorial-"
+"PyTorch and Flower (`part 1 <https://flower.ai/docs/framework/tutorial-"
 "get-started-with-flower-pytorch.html>`__) and we learned how strategies "
 "can be used to customize the execution on both the server and the clients"
-" (`part 2 <https://flower.dev/docs/framework/tutorial-use-a-federated-"
+" (`part 2 <https://flower.ai/docs/framework/tutorial-use-a-federated-"
 "learning-strategy-pytorch.html>`__)."
 msgstr ""
 "Bienvenue dans la troisième partie du tutoriel sur l'apprentissage fédéré"
 " Flower. Dans les parties précédentes de ce tutoriel, nous avons présenté"
 " l'apprentissage fédéré avec PyTorch et Flower (`partie 1 "
-"<https://flower.dev/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__) "
+"<https://flower.ai/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__) "
 "et nous avons appris comment les stratégies peuvent être utilisées pour "
 "personnaliser l'exécution à la fois sur le serveur et sur les clients "
-"(`partie 2 <https://flower.dev/docs/tutorial/Flower-2-Strategies-in-FL-"
+"(`partie 2 <https://flower.ai/docs/tutorial/Flower-2-Strategies-in-FL-"
 "PyTorch.html>`__)."
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:13
 msgid ""
 "In this notebook, we'll continue to customize the federated learning "
 "system we built previously by creating a custom version of FedAvg (again,"
-" using `Flower <https://flower.dev/>`__ and `PyTorch "
+" using `Flower <https://flower.ai/>`__ and `PyTorch "
 "<https://pytorch.org/>`__)."
 msgstr ""
 "Dans ce carnet, nous allons continuer à personnaliser le système "
 "d'apprentissage fédéré que nous avons construit précédemment en créant "
 "une version personnalisée de FedAvg (encore une fois, en utilisant "
-"`Flower <https://flower.dev/>`__ et `PyTorch <https://pytorch.org/>`__)."
+"`Flower <https://flower.ai/>`__ et `PyTorch <https://pytorch.org/>`__)."
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:15
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:16
@@ -20006,13 +20006,13 @@ msgstr ""
 msgid ""
 "`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ and join "
 "the Flower community on Slack to connect, ask questions, and get help: "
-"`Join Slack <https://flower.dev/join-slack>`__ 🌼 We'd love to hear from "
+"`Join Slack <https://flower.ai/join-slack>`__ 🌼 We'd love to hear from "
 "you in the ``#introductions`` channel! And if anything is unclear, head "
 "over to the ``#questions`` channel."
 msgstr ""
 "`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ et "
 "rejoignez la communauté Flower sur Slack pour vous connecter, poser des "
-"questions et obtenir de l'aide : `Join Slack <https://flower.dev/join-"
+"questions et obtenir de l'aide : `Join Slack <https://flower.ai/join-"
 "slack>`__ 🌼 Nous serions ravis d'avoir de vos nouvelles dans le canal "
 "``#introductions`` ! Et si quelque chose n'est pas clair, rendez-vous sur"
 " le canal ``#questions``."
@@ -20205,10 +20205,10 @@ msgstr ""
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:369
 msgid ""
 "Before you continue, make sure to join the Flower community on Slack: "
-"`Join Slack <https://flower.dev/join-slack/>`__"
+"`Join Slack <https://flower.ai/join-slack/>`__"
 msgstr ""
 "Avant de continuer, n'oublie pas de rejoindre la communauté Flower sur "
-"Slack : `Join Slack <https://flower.dev/join-slack/>`__"
+"Slack : `Join Slack <https://flower.ai/join-slack/>`__"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:552
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:950
@@ -20226,12 +20226,12 @@ msgstr ""
 #, fuzzy
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 4 "
-"<https://flower.dev/docs/framework/tutorial-customize-the-client-"
+"<https://flower.ai/docs/framework/tutorial-customize-the-client-"
 "pytorch.html>`__ introduces ``Client``, the flexible API underlying "
 "``NumPyClient``."
 msgstr ""
 "Le `Tutoriel d'apprentissage fédéré Flower - Partie 4 "
-"<https://flower.dev/docs/tutorial/Flower-4-Client-and-NumPyClient-"
+"<https://flower.ai/docs/tutorial/Flower-4-Client-and-NumPyClient-"
 "PyTorch.html>`__ présente ``Client``, l'API flexible qui sous-tend "
 "``NumPyClient``."
 
@@ -20245,24 +20245,24 @@ msgstr "Création du client IMDBC"
 msgid ""
 "Welcome to the fourth part of the Flower federated learning tutorial. In "
 "the previous parts of this tutorial, we introduced federated learning "
-"with PyTorch and Flower (`part 1 <https://flower.dev/docs/framework"
+"with PyTorch and Flower (`part 1 <https://flower.ai/docs/framework"
 "/tutorial-get-started-with-flower-pytorch.html>`__), we learned how "
 "strategies can be used to customize the execution on both the server and "
-"the clients (`part 2 <https://flower.dev/docs/framework/tutorial-use-a"
+"the clients (`part 2 <https://flower.ai/docs/framework/tutorial-use-a"
 "-federated-learning-strategy-pytorch.html>`__), and we built our own "
-"custom strategy from scratch (`part 3 <https://flower.dev/docs/framework"
+"custom strategy from scratch (`part 3 <https://flower.ai/docs/framework"
 "/tutorial-build-a-strategy-from-scratch-pytorch.html>`__)."
 msgstr ""
 "Bienvenue dans la quatrième partie du tutoriel sur l'apprentissage fédéré"
 " Flower. Dans les parties précédentes de ce tutoriel, nous avons présenté"
 " l'apprentissage fédéré avec PyTorch et Flower (`partie 1 "
-"<https://flower.dev/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__),"
+"<https://flower.ai/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__),"
 " nous avons appris comment les stratégies peuvent être utilisées pour "
 "personnaliser l'exécution à la fois sur le serveur et les clients "
-"(`partie 2 <https://flower.dev/docs/tutorial/Flower-2-Strategies-in-FL-"
+"(`partie 2 <https://flower.ai/docs/tutorial/Flower-2-Strategies-in-FL-"
 "PyTorch.html>`__), et nous avons construit notre propre stratégie "
 "personnalisée à partir de zéro (`partie 3 - WIP "
-"<https://flower.dev/docs/tutorial/Flower-3-Building-a-Strategy-"
+"<https://flower.ai/docs/tutorial/Flower-3-Building-a-Strategy-"
 "PyTorch.html>`__)."
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:14
@@ -20677,8 +20677,8 @@ msgstr ""
 "dans le tutoriel, nous te recommandons les ressources suivantes :"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:954
-msgid "`Read Flower Docs <https://flower.dev/docs/>`__"
-msgstr "`Lire les docs sur les fleurs <https://flower.dev/docs/>`__"
+msgid "`Read Flower Docs <https://flower.ai/docs/>`__"
+msgstr "`Lire les docs sur les fleurs <https://flower.ai/docs/>`__"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:955
 msgid ""
@@ -20692,18 +20692,18 @@ msgstr ""
 #, fuzzy
 msgid ""
 "`Use Flower Baselines for your research "
-"<https://flower.dev/docs/baselines/>`__"
+"<https://flower.ai/docs/baselines/>`__"
 msgstr ""
 "`Utilise les lignes de base des fleurs pour ta recherche "
-"<https://flower.dev/docs/using-baselines.html>`__"
+"<https://flower.ai/docs/using-baselines.html>`__"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:957
 #, fuzzy
 msgid ""
-"`Watch Flower Summit 2023 videos <https://flower.dev/conf/flower-"
+"`Watch Flower Summit 2023 videos <https://flower.ai/conf/flower-"
 "summit-2023/>`__"
 msgstr ""
-"`Regardez les vidéos du Flower Summit 2022 <https://flower.dev/conf"
+"`Regardez les vidéos du Flower Summit 2022 <https://flower.ai/conf"
 "/flower-summit-2022/>`__"
 
 #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:9
@@ -20719,7 +20719,7 @@ msgstr "Bienvenue au tutoriel sur l'apprentissage fédéré de la fleur !"
 #, fuzzy
 msgid ""
 "In this notebook, we'll build a federated learning system using Flower, "
-"`Flower Datasets <https://flower.dev/docs/datasets/>`__ and PyTorch. In "
+"`Flower Datasets <https://flower.ai/docs/datasets/>`__ and PyTorch. In "
 "part 1, we use PyTorch for the model training pipeline and data loading. "
 "In part 2, we continue to federate the PyTorch-based pipeline using "
 "Flower."
@@ -21384,12 +21384,12 @@ msgstr ""
 #, fuzzy
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 2 "
-"<https://flower.dev/docs/framework/tutorial-use-a-federated-learning-"
+"<https://flower.ai/docs/framework/tutorial-use-a-federated-learning-"
 "strategy-pytorch.html>`__ goes into more depth about strategies and all "
 "the advanced things you can build with them."
 msgstr ""
 "Le `Tutoriel d'apprentissage fédéré Flower - Partie 2 "
-"<https://flower.dev/docs/tutorial/Flower-2-Strategies-in-FL-"
+"<https://flower.ai/docs/tutorial/Flower-2-Strategies-in-FL-"
 "PyTorch.html>`__ va plus en profondeur sur les stratégies et toutes les "
 "choses avancées que tu peux construire avec elles."
 
@@ -21403,23 +21403,23 @@ msgstr "Stratégie de moyenne fédérée."
 msgid ""
 "Welcome to the next part of the federated learning tutorial. In previous "
 "parts of this tutorial, we introduced federated learning with PyTorch and"
-" Flower (`part 1 <https://flower.dev/docs/framework/tutorial-get-started-"
+" Flower (`part 1 <https://flower.ai/docs/framework/tutorial-get-started-"
 "with-flower-pytorch.html>`__)."
 msgstr ""
 "Bienvenue dans la prochaine partie du tutoriel sur l'apprentissage "
 "fédéré. Dans les parties précédentes de ce tutoriel, nous avons présenté "
 "l'apprentissage fédéré avec PyTorch et Flower (`partie 1 "
-"<https://flower.dev/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__)."
+"<https://flower.ai/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__)."
 
 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:13
 msgid ""
 "In this notebook, we'll begin to customize the federated learning system "
 "we built in the introductory notebook (again, using `Flower "
-"<https://flower.dev/>`__ and `PyTorch <https://pytorch.org/>`__)."
+"<https://flower.ai/>`__ and `PyTorch <https://pytorch.org/>`__)."
 msgstr ""
 "Dans ce carnet, nous allons commencer à personnaliser le système "
 "d'apprentissage fédéré que nous avons construit dans le carnet "
-"d'introduction (toujours en utilisant `Flower <https://flower.dev/>`__ et"
+"d'introduction (toujours en utilisant `Flower <https://flower.ai/>`__ et"
 " `PyTorch <https://pytorch.org/>`__)."
 
 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:17
@@ -21725,12 +21725,12 @@ msgstr ""
 #, fuzzy
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 3 "
-"<https://flower.dev/docs/framework/tutorial-build-a-strategy-from-"
+"<https://flower.ai/docs/framework/tutorial-build-a-strategy-from-"
 "scratch-pytorch.html>`__ shows how to build a fully custom ``Strategy`` "
 "from scratch."
 msgstr ""
 "Le `Tutoriel d'apprentissage fédéré Flower - Partie 3 [WIP] "
-"<https://flower.dev/docs/tutorial/Flower-3-Building-a-Strategy-"
+"<https://flower.ai/docs/tutorial/Flower-3-Building-a-Strategy-"
 "PyTorch.html>`__ montre comment construire une ``Stratégie`` entièrement "
 "personnalisée à partir de zéro."
 
@@ -21767,13 +21767,13 @@ msgstr ""
 msgid ""
 "`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ and join "
 "the open-source Flower community on Slack to connect, ask questions, and "
-"get help: `Join Slack <https://flower.dev/join-slack>`__ 🌼 We'd love to "
+"get help: `Join Slack <https://flower.ai/join-slack>`__ 🌼 We'd love to "
 "hear from you in the ``#introductions`` channel! And if anything is "
 "unclear, head over to the ``#questions`` channel."
 msgstr ""
 "`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ et "
 "rejoignez la communauté Flower sur Slack pour vous connecter, poser des "
-"questions et obtenir de l'aide : `Join Slack <https://flower.dev/join-"
+"questions et obtenir de l'aide : `Join Slack <https://flower.ai/join-"
 "slack>`__ 🌼 Nous serions ravis d'avoir de vos nouvelles dans le canal "
 "``#introductions`` ! Et si quelque chose n'est pas clair, rendez-vous sur"
 " le canal ``#questions``."
@@ -22523,12 +22523,12 @@ msgstr ""
 #, fuzzy
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 1 "
-"<https://flower.dev/docs/framework/tutorial-get-started-with-flower-"
+"<https://flower.ai/docs/framework/tutorial-get-started-with-flower-"
 "pytorch.html>`__ shows how to build a simple federated learning system "
 "with PyTorch and Flower."
 msgstr ""
 "Le `Tutoriel d'apprentissage fédéré Flower - Partie 1 "
-"<https://flower.dev/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__ "
+"<https://flower.ai/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__ "
 "montre comment construire un système d'apprentissage fédéré simple avec "
 "PyTorch et Flower."
 
@@ -24573,10 +24573,10 @@ msgstr ""
 
 #~ msgid ""
 #~ "`Android Kotlin example "
-#~ "<https://flower.dev/docs/examples/android-kotlin.html>`_"
+#~ "<https://flower.ai/docs/examples/android-kotlin.html>`_"
 #~ msgstr ""
 
-#~ msgid "`Android Java example <https://flower.dev/docs/examples/android.html>`_"
+#~ msgid "`Android Java example <https://flower.ai/docs/examples/android.html>`_"
 #~ msgstr ""
 
 #~ msgid "Build a strategy from scratch"
@@ -24629,7 +24629,7 @@ msgstr ""
 #~ "Flower Python server, it is recommended"
 #~ " to create a virtual environment and"
 #~ " run everything within a `virtualenv "
-#~ "<https://flower.dev/docs/recommended-env-setup.html>`_."
+#~ "<https://flower.ai/docs/recommended-env-setup.html>`_."
 #~ " For the Flower client implementation "
 #~ "in iOS, it is recommended to use"
 #~ " Xcode as our IDE."
@@ -24638,7 +24638,7 @@ msgstr ""
 #~ "serveur Flower Python, il est recommandé"
 #~ " de créer un environnement virtuel et"
 #~ " de tout exécuter au sein d'un "
-#~ "`virtualenv <https://flower.dev/docs/recommended-env-"
+#~ "`virtualenv <https://flower.ai/docs/recommended-env-"
 #~ "setup.html>`_. Pour l'implémentation du client"
 #~ " Flower dans iOS, il est recommandé"
 #~ " d'utiliser Xcode comme notre IDE."
@@ -24901,7 +24901,7 @@ msgstr ""
 #~ "<https://github.com/adap/flower>`__ ⭐️ and join "
 #~ "the open-source Flower community on "
 #~ "Slack to connect, ask questions, and "
-#~ "get help: `Join Slack <https://flower.dev"
+#~ "get help: `Join Slack <https://flower.ai"
 #~ "/join-slack>`__ 🌼 We'd love to hear"
 #~ " from you in the ``#introductions`` "
 #~ "channel! And if anything is unclear, "
@@ -24912,7 +24912,7 @@ msgstr ""
 #~ " la communauté open-source Flower sur"
 #~ " Slack pour vous connecter, poser des"
 #~ " questions et obtenir de l'aide : "
-#~ "`Join Slack <https://flower.dev/join-slack>`__ "
+#~ "`Join Slack <https://flower.ai/join-slack>`__ "
 #~ "🌼 Nous serions ravis d'avoir de "
 #~ "vos nouvelles dans le canal "
 #~ "``#introductions`` ! Et si quelque chose"
@@ -25242,7 +25242,7 @@ msgstr ""
 #~ "Please make sure to add your "
 #~ "baseline or experiment to the "
 #~ "corresponding directory as explained in "
-#~ "`Executing Baseline <https://flower.dev/docs/using-"
+#~ "`Executing Baseline <https://flower.ai/docs/using-"
 #~ "baselines.html>`_. Give your baseline the "
 #~ "unique identifier. For example, :code:`fedbn`"
 #~ " refers to the paper \"FedBN: "
@@ -25474,7 +25474,7 @@ msgstr ""
 #~ " papers. If you want to add a"
 #~ " new baseline or experiment, please "
 #~ "check the `Contributing Baselines "
-#~ "<https://flower.dev/docs/contributing-baselines.html>`_ "
+#~ "<https://flower.ai/docs/contributing-baselines.html>`_ "
 #~ "section."
 #~ msgstr ""
 
diff --git a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
index 5314e0041a99..359458e8db57 100644
--- a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
@@ -29,7 +29,7 @@ msgstr ""
 
 #: ../../source/contributor-explanation-architecture.rst:7
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with Edge "
+"`Flower <https://flower.ai>`_ core framework architecture with Edge "
 "Client Engine"
 msgstr ""
 
@@ -39,7 +39,7 @@ msgstr ""
 
 #: ../../source/contributor-explanation-architecture.rst:15
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with Virtual "
+"`Flower <https://flower.ai>`_ core framework architecture with Virtual "
 "Client Engine"
 msgstr ""
 
@@ -49,7 +49,7 @@ msgstr ""
 
 #: ../../source/contributor-explanation-architecture.rst:23
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with both "
+"`Flower <https://flower.ai>`_ core framework architecture with both "
 "Virtual Client Engine and Edge Client Engine"
 msgstr ""
 
@@ -84,7 +84,7 @@ msgstr ""
 #: ../../source/contributor-how-to-build-docker-images.rst:19
 msgid ""
 "Please follow the first section on `Run Flower using Docker "
-"<https://flower.dev/docs/framework/how-to-run-flower-using-docker>`_ "
+"<https://flower.ai/docs/framework/how-to-run-flower-using-docker>`_ "
 "which covers this step in more detail."
 msgstr ""
 
@@ -280,7 +280,7 @@ msgstr ""
 
 #: ../../source/contributor-how-to-contribute-translations.rst:4
 msgid ""
-"Since `Flower 1.5 <https://flower.dev/docs/framework/ref-"
+"Since `Flower 1.5 <https://flower.ai/docs/framework/ref-"
 "changelog.html#v1-5-0-2023-08-31>`_ we have introduced translations to "
 "our doc pages, but, as you might have noticed, the translations are often"
 " imperfect. If you speak languages other than English, you might be able "
@@ -376,7 +376,7 @@ msgstr ""
 #: ../../source/contributor-how-to-contribute-translations.rst:69
 msgid ""
 "If you want to add a new language, you will first have to contact us, "
-"either on `Slack <https://flower.dev/join-slack>`_, or by opening an "
+"either on `Slack <https://flower.ai/join-slack>`_, or by opening an "
 "issue on our `GitHub repo <https://github.com/adap/flower>`_."
 msgstr ""
 
@@ -1114,7 +1114,7 @@ msgstr ""
 #: ../../source/contributor-ref-good-first-contributions.rst:25
 msgid ""
 "If you are not familiar with Flower Baselines, you should probably check-"
-"out our `contributing guide for baselines <https://flower.dev/docs"
+"out our `contributing guide for baselines <https://flower.ai/docs"
 "/contributing-baselines.html>`_."
 msgstr ""
 
@@ -1209,7 +1209,7 @@ msgstr ""
 msgid ""
 "If you're familiar with how contributing on GitHub works, you can "
 "directly checkout our `getting started guide for contributors "
-"<https://flower.dev/docs/contributor-tutorial-get-started-"
+"<https://flower.ai/docs/contributor-tutorial-get-started-"
 "as-a-contributor.html>`_."
 msgstr ""
 
@@ -1667,7 +1667,7 @@ msgstr ""
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:280
 msgid ""
-"Build the docs and check the result: `<https://flower.dev/docs/writing-"
+"Build the docs and check the result: `<https://flower.ai/docs/writing-"
 "documentation.html#edit-an-existing-page>`_"
 msgstr ""
 
@@ -1835,7 +1835,7 @@ msgstr ""
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:346
 msgid ""
-"`Good first contributions <https://flower.dev/docs/framework/contributor-"
+"`Good first contributions <https://flower.ai/docs/framework/contributor-"
 "ref-good-first-contributions.html>`_, where you should particularly look "
 "into the :code:`baselines` contributions."
 msgstr ""
@@ -2173,7 +2173,7 @@ msgid ""
 "designed for non-iid data. We are using PyTorch to train a Convolutional "
 "Neural Network(with Batch Normalization layers) on the CIFAR-10 dataset. "
 "When applying FedBN, only few changes needed compared to `Example: "
-"PyTorch - From Centralized To Federated <https://flower.dev/docs/examples"
+"PyTorch - From Centralized To Federated <https://flower.ai/docs/examples"
 "/pytorch-from-centralized-to-federated.html>`_."
 msgstr ""
 
@@ -2185,7 +2185,7 @@ msgstr ""
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:10
 msgid ""
 "All files are revised based on `Example: PyTorch - From Centralized To "
-"Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-"
+"Federated <https://flower.ai/docs/examples/pytorch-from-centralized-to-"
 "federated.html>`_. The only thing to do is modifying the file called "
 ":code:`cifar.py`, revised part is shown below:"
 msgstr ""
@@ -2217,11 +2217,11 @@ msgstr ""
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:53
 msgid ""
 "If you have read `Example: PyTorch - From Centralized To Federated "
-"<https://flower.dev/docs/examples/pytorch-from-centralized-to-"
+"<https://flower.ai/docs/examples/pytorch-from-centralized-to-"
 "federated.html>`_, the following parts are easy to follow, onyl "
 ":code:`get_parameters` and :code:`set_parameters` function in "
 ":code:`client.py` needed to revise. If not, please read the `Example: "
-"PyTorch - From Centralized To Federated <https://flower.dev/docs/examples"
+"PyTorch - From Centralized To Federated <https://flower.ai/docs/examples"
 "/pytorch-from-centralized-to-federated.html>`_. first."
 msgstr ""
 
@@ -4260,7 +4260,7 @@ msgstr ""
 msgid ""
 "This can be achieved by customizing an existing strategy or by "
 "`implementing a custom strategy from scratch "
-"<https://flower.dev/docs/framework/how-to-implement-strategies.html>`_. "
+"<https://flower.ai/docs/framework/how-to-implement-strategies.html>`_. "
 "Here's a nonsensical example that customizes :code:`FedAvg` by adding a "
 "custom ``\"hello\": \"world\"`` configuration key/value pair to the "
 "config dict of a *single client* (only the first client in the list, the "
@@ -4806,7 +4806,7 @@ msgstr ""
 
 #: ../../source/how-to-install-flower.rst:60
 msgid ""
-"`How to run Flower using Docker <https://flower.dev/docs/framework/how-"
+"`How to run Flower using Docker <https://flower.ai/docs/framework/how-"
 "to-run-flower-using-docker.html>`_"
 msgstr ""
 
@@ -5153,7 +5153,7 @@ msgstr ""
 msgid ""
 "The ``--insecure`` flag enables insecure communication (using HTTP, not "
 "HTTPS) and should only be used for testing purposes. We strongly "
-"recommend enabling `SSL <https://flower.dev/docs/framework/how-to-run-"
+"recommend enabling `SSL <https://flower.ai/docs/framework/how-to-run-"
 "flower-using-docker.html#enabling-ssl-for-secure-connections>`_ when "
 "deploying to a production environment."
 msgstr ""
@@ -5200,7 +5200,7 @@ msgstr ""
 #: ../../source/how-to-run-flower-using-docker.rst:92
 msgid ""
 "For testing purposes, you can generate your own self-signed certificates."
-" The `Enable SSL connections <https://flower.dev/docs/framework/how-to-"
+" The `Enable SSL connections <https://flower.ai/docs/framework/how-to-"
 "enable-ssl-connections.html#certificates>`_ page contains a section that "
 "will guide you through the process."
 msgstr ""
@@ -5974,7 +5974,7 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples>`_ are already updated"
 " to Flower 1.0, they can serve as a reference for using the Flower 1.0 "
 "API. If there are further questionsm, `join the Flower Slack "
-"<https://flower.dev/join-slack/>`_ and use the channgel ``#questions``."
+"<https://flower.ai/join-slack/>`_ and use the channgel ``#questions``."
 msgstr ""
 
 #: ../../source/how-to-use-built-in-mods.rst:2
@@ -6255,7 +6255,7 @@ msgstr ""
 
 #: ../../source/index.rst:7
 msgid ""
-"Welcome to Flower's documentation. `Flower <https://flower.dev>`_ is a "
+"Welcome to Flower's documentation. `Flower <https://flower.ai>`_ is a "
 "friendly federated learning framework."
 msgstr ""
 
@@ -10772,7 +10772,7 @@ msgstr ""
 msgid ""
 "The Flower server can now be run using an official Docker image. A new "
 "how-to guide explains [how to run Flower using "
-"Docker](https://flower.dev/docs/framework/how-to-run-flower-using-"
+"Docker](https://flower.ai/docs/framework/how-to-run-flower-using-"
 "docker.html). An official Flower client Docker image will follow."
 msgstr ""
 
@@ -10806,7 +10806,7 @@ msgstr ""
 #: ../../source/ref-changelog.md:55
 msgid ""
 "Several code examples were updated to use [Flower "
-"Datasets](https://flower.dev/docs/datasets/)."
+"Datasets](https://flower.ai/docs/datasets/)."
 msgstr ""
 
 #: ../../source/ref-changelog.md:57
@@ -11405,10 +11405,10 @@ msgstr ""
 #: ../../source/ref-changelog.md:216
 msgid ""
 "Comprehensive documentation includes a new [how-to run "
-"simulations](https://flower.dev/docs/framework/how-to-run-"
+"simulations](https://flower.ai/docs/framework/how-to-run-"
 "simulations.html) guide, new [simulation-"
-"pytorch](https://flower.dev/docs/examples/simulation-pytorch.html) and "
-"[simulation-tensorflow](https://flower.dev/docs/examples/simulation-"
+"pytorch](https://flower.ai/docs/examples/simulation-pytorch.html) and "
+"[simulation-tensorflow](https://flower.ai/docs/examples/simulation-"
 "tensorflow.html) notebooks, and a new [YouTube tutorial "
 "series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)."
 msgstr ""
@@ -11448,7 +11448,7 @@ msgstr ""
 #: ../../source/ref-changelog.md:220
 msgid ""
 "Much effort went into a completely restructured Flower docs experience. "
-"The documentation on [flower.dev/docs](flower.dev/docs) is now divided "
+"The documentation on [flower.ai/docs](flower.ai/docs) is now divided "
 "into Flower Framework, Flower Baselines, Flower Android SDK, Flower iOS "
 "SDK, and code example projects."
 msgstr ""
@@ -11742,7 +11742,7 @@ msgstr ""
 
 #: ../../source/ref-changelog.md:306
 msgid ""
-"A new [entry-level tutorial](https://flower.dev/docs/framework/tutorial-"
+"A new [entry-level tutorial](https://flower.ai/docs/framework/tutorial-"
 "what-is-federated-learning.html) in our documentation explains the basics"
 " of Fedetated Learning. It enables anyone who's unfamiliar with Federated"
 " Learning to start their journey with Flower. Forward it to anyone who's "
@@ -12149,7 +12149,7 @@ msgid ""
 "revisit well known papers from the literature, and be suitable for "
 "integration in your own application or for experimentation, in order to "
 "deepen your knowledge of FL in general. Today's release is the first in "
-"this series. [Read more.](https://flower.dev/blog/2023-01-12-fl-starter-"
+"this series. [Read more.](https://flower.ai/blog/2023-01-12-fl-starter-"
 "pack-fedavg-mnist-cnn/)"
 msgstr ""
 
@@ -12183,25 +12183,25 @@ msgstr ""
 
 #: ../../source/ref-changelog.md:430
 msgid ""
-"[An Introduction to Federated Learning](https://flower.dev/docs/framework"
+"[An Introduction to Federated Learning](https://flower.ai/docs/framework"
 "/tutorial-get-started-with-flower-pytorch.html)"
 msgstr ""
 
 #: ../../source/ref-changelog.md:431
 msgid ""
-"[Strategies in Federated Learning](https://flower.dev/docs/framework"
+"[Strategies in Federated Learning](https://flower.ai/docs/framework"
 "/tutorial-use-a-federated-learning-strategy-pytorch.html)"
 msgstr ""
 
 #: ../../source/ref-changelog.md:432
 msgid ""
-"[Building a Strategy](https://flower.dev/docs/framework/tutorial-build-a"
+"[Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a"
 "-strategy-from-scratch-pytorch.html)"
 msgstr ""
 
 #: ../../source/ref-changelog.md:433
 msgid ""
-"[Client and NumPyClient](https://flower.dev/docs/framework/tutorial-"
+"[Client and NumPyClient](https://flower.ai/docs/framework/tutorial-"
 "customize-the-client-pytorch.html)"
 msgstr ""
 
@@ -12228,7 +12228,7 @@ msgid ""
 "**Flower is a friendly framework for collaborative AI and data science.**"
 " Staying true to this statement, Flower makes it easy to disable "
 "telemetry for users who do not want to share anonymous usage metrics. "
-"[Read more.](https://flower.dev/docs/telemetry.html)."
+"[Read more.](https://flower.ai/docs/telemetry.html)."
 msgstr ""
 
 #: ../../source/ref-changelog.md:441
@@ -12346,7 +12346,7 @@ msgstr ""
 #: ../../source/ref-changelog.md:471
 msgid ""
 "One highlight is the new [first time contributor "
-"guide](https://flower.dev/docs/first-time-contributors.html): if you've "
+"guide](https://flower.ai/docs/first-time-contributors.html): if you've "
 "never contributed on GitHub before, this is the perfect place to start!"
 msgstr ""
 
@@ -12981,9 +12981,9 @@ msgid ""
 "The first preview release of Flower Baselines has arrived! We're "
 "kickstarting Flower Baselines with implementations of FedOpt (FedYogi, "
 "FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how "
-"to use [Flower Baselines](https://flower.dev/docs/using-baselines.html). "
+"to use [Flower Baselines](https://flower.ai/docs/using-baselines.html). "
 "With this first preview release we're also inviting the community to "
-"[contribute their own baselines](https://flower.dev/docs/contributing-"
+"[contribute their own baselines](https://flower.ai/docs/contributing-"
 "baselines.html)."
 msgstr ""
 
@@ -13135,7 +13135,7 @@ msgstr ""
 #: ../../source/ref-changelog.md:706
 msgid ""
 "New documentation for [implementing "
-"strategies](https://flower.dev/docs/framework/how-to-implement-"
+"strategies](https://flower.ai/docs/framework/how-to-implement-"
 "strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), "
 "[#1175](https://github.com/adap/flower/pull/1175))"
 msgstr ""
@@ -13994,13 +13994,13 @@ msgstr ""
 
 #: ../../source/ref-example-projects.rst:26
 msgid ""
-"`Quickstart TensorFlow (Tutorial) <https://flower.dev/docs/framework"
+"`Quickstart TensorFlow (Tutorial) <https://flower.ai/docs/framework"
 "/tutorial-quickstart-tensorflow.html>`_"
 msgstr ""
 
 #: ../../source/ref-example-projects.rst:27
 msgid ""
-"`Quickstart TensorFlow (Blog Post) <https://flower.dev/blog/2020-12-11"
+"`Quickstart TensorFlow (Blog Post) <https://flower.ai/blog/2020-12-11"
 "-federated-learning-in-less-than-20-lines-of-code>`_"
 msgstr ""
 
@@ -14023,7 +14023,7 @@ msgstr ""
 
 #: ../../source/ref-example-projects.rst:37
 msgid ""
-"`Quickstart PyTorch (Tutorial) <https://flower.dev/docs/framework"
+"`Quickstart PyTorch (Tutorial) <https://flower.ai/docs/framework"
 "/tutorial-quickstart-pytorch.html>`_"
 msgstr ""
 
@@ -14047,7 +14047,7 @@ msgstr ""
 #: ../../source/ref-example-projects.rst:46
 msgid ""
 "`PyTorch: From Centralized To Federated (Tutorial) "
-"<https://flower.dev/docs/framework/example-pytorch-from-centralized-to-"
+"<https://flower.ai/docs/framework/example-pytorch-from-centralized-to-"
 "federated.html>`_"
 msgstr ""
 
@@ -14070,7 +14070,7 @@ msgstr ""
 #: ../../source/ref-example-projects.rst:55
 msgid ""
 "`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 msgstr ""
 
 #: ../../source/ref-example-projects.rst:60
@@ -14257,7 +14257,7 @@ msgstr ""
 #: ../../source/ref-faq.rst:15
 msgid ""
 "Find the `blog post about federated learning on embedded device here "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 " and the corresponding `GitHub code example "
 "<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_."
 msgstr ""
@@ -14269,18 +14269,18 @@ msgstr ""
 #: ../../source/ref-faq.rst:19
 msgid ""
 "Yes, it does. Please take a look at our `blog post "
-"<https://flower.dev/blog/2021-12-15-federated-learning-on-android-"
+"<https://flower.ai/blog/2021-12-15-federated-learning-on-android-"
 "devices-with-flower>`_ or check out the code examples:"
 msgstr ""
 
 #: ../../source/ref-faq.rst:21
 msgid ""
-"`Android Kotlin example <https://flower.dev/docs/examples/android-"
+"`Android Kotlin example <https://flower.ai/docs/examples/android-"
 "kotlin.html>`_"
 msgstr ""
 
 #: ../../source/ref-faq.rst:22
-msgid "`Android Java example <https://flower.dev/docs/examples/android.html>`_"
+msgid "`Android Java example <https://flower.ai/docs/examples/android.html>`_"
 msgstr ""
 
 #: ../../source/ref-faq.rst
@@ -14466,7 +14466,7 @@ msgstr ""
 msgid ""
 "You may delete the source ID at any time. If you wish for all events "
 "logged under a specific source ID to be deleted, you can send a deletion "
-"request mentioning the source ID to `telemetry@flower.dev`. All events "
+"request mentioning the source ID to `telemetry@flower.ai`. All events "
 "related to that source ID will then be permanently deleted."
 msgstr ""
 
@@ -14514,8 +14514,8 @@ msgstr ""
 msgid ""
 "We want to hear from you. If you have any feedback or ideas on how to "
 "improve the way we handle anonymous usage metrics, reach out to us via "
-"[Slack](https://flower.dev/join-slack/) (channel `#telemetry`) or email "
-"(`telemetry@flower.dev`)."
+"[Slack](https://flower.ai/join-slack/) (channel `#telemetry`) or email "
+"(`telemetry@flower.ai`)."
 msgstr ""
 
 #: ../../source/tutorial-quickstart-android.rst:-1
@@ -14737,7 +14737,7 @@ msgstr ""
 msgid ""
 "First of all, for running the Flower Python server, it is recommended to "
 "create a virtual environment and run everything within a `virtualenv "
-"<https://flower.dev/docs/recommended-env-setup.html>`_. For the Flower "
+"<https://flower.ai/docs/recommended-env-setup.html>`_. For the Flower "
 "client implementation in iOS, it is recommended to use Xcode as our IDE."
 msgstr ""
 
@@ -14918,7 +14918,7 @@ msgstr ""
 #: ../../source/tutorial-quickstart-scikitlearn.rst:12
 msgid ""
 "It is recommended to create a virtual environment and run everything "
-"within this `virtualenv <https://flower.dev/docs/recommended-env-"
+"within this `virtualenv <https://flower.ai/docs/recommended-env-"
 "setup.html>`_."
 msgstr ""
 
@@ -15166,7 +15166,7 @@ msgstr ""
 #: ../../source/tutorial-quickstart-xgboost.rst:39
 msgid ""
 "First of all, it is recommended to create a virtual environment and run "
-"everything within a `virtualenv <https://flower.dev/docs/recommended-env-"
+"everything within a `virtualenv <https://flower.ai/docs/recommended-env-"
 "setup.html>`_."
 msgstr ""
 
@@ -15876,7 +15876,7 @@ msgid ""
 "comprehensive>`_), we provide more options to define various experimental"
 " setups, including aggregation strategies, data partitioning and "
 "centralised/distributed evaluation. We also support `Flower simulation "
-"<https://flower.dev/docs/framework/how-to-run-simulations.html>`_ making "
+"<https://flower.ai/docs/framework/how-to-run-simulations.html>`_ making "
 "it easy to simulate large client cohorts in a resource-aware manner. "
 "Let's take a look!"
 msgstr ""
@@ -16077,10 +16077,10 @@ msgstr ""
 msgid ""
 "Welcome to the third part of the Flower federated learning tutorial. In "
 "previous parts of this tutorial, we introduced federated learning with "
-"PyTorch and Flower (`part 1 <https://flower.dev/docs/framework/tutorial-"
+"PyTorch and Flower (`part 1 <https://flower.ai/docs/framework/tutorial-"
 "get-started-with-flower-pytorch.html>`__) and we learned how strategies "
 "can be used to customize the execution on both the server and the clients"
-" (`part 2 <https://flower.dev/docs/framework/tutorial-use-a-federated-"
+" (`part 2 <https://flower.ai/docs/framework/tutorial-use-a-federated-"
 "learning-strategy-pytorch.html>`__)."
 msgstr ""
 
@@ -16088,7 +16088,7 @@ msgstr ""
 msgid ""
 "In this notebook, we'll continue to customize the federated learning "
 "system we built previously by creating a custom version of FedAvg (again,"
-" using `Flower <https://flower.dev/>`__ and `PyTorch "
+" using `Flower <https://flower.ai/>`__ and `PyTorch "
 "<https://pytorch.org/>`__)."
 msgstr ""
 
@@ -16099,7 +16099,7 @@ msgstr ""
 msgid ""
 "`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ and join "
 "the Flower community on Slack to connect, ask questions, and get help: "
-"`Join Slack <https://flower.dev/join-slack>`__ 🌼 We'd love to hear from "
+"`Join Slack <https://flower.ai/join-slack>`__ 🌼 We'd love to hear from "
 "you in the ``#introductions`` channel! And if anything is unclear, head "
 "over to the ``#questions`` channel."
 msgstr ""
@@ -16248,7 +16248,7 @@ msgstr ""
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:369
 msgid ""
 "Before you continue, make sure to join the Flower community on Slack: "
-"`Join Slack <https://flower.dev/join-slack/>`__"
+"`Join Slack <https://flower.ai/join-slack/>`__"
 msgstr ""
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:552
@@ -16264,7 +16264,7 @@ msgstr ""
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:554
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 4 "
-"<https://flower.dev/docs/framework/tutorial-customize-the-client-"
+"<https://flower.ai/docs/framework/tutorial-customize-the-client-"
 "pytorch.html>`__ introduces ``Client``, the flexible API underlying "
 "``NumPyClient``."
 msgstr ""
@@ -16277,12 +16277,12 @@ msgstr ""
 msgid ""
 "Welcome to the fourth part of the Flower federated learning tutorial. In "
 "the previous parts of this tutorial, we introduced federated learning "
-"with PyTorch and Flower (`part 1 <https://flower.dev/docs/framework"
+"with PyTorch and Flower (`part 1 <https://flower.ai/docs/framework"
 "/tutorial-get-started-with-flower-pytorch.html>`__), we learned how "
 "strategies can be used to customize the execution on both the server and "
-"the clients (`part 2 <https://flower.dev/docs/framework/tutorial-use-a"
+"the clients (`part 2 <https://flower.ai/docs/framework/tutorial-use-a"
 "-federated-learning-strategy-pytorch.html>`__), and we built our own "
-"custom strategy from scratch (`part 3 <https://flower.dev/docs/framework"
+"custom strategy from scratch (`part 3 <https://flower.ai/docs/framework"
 "/tutorial-build-a-strategy-from-scratch-pytorch.html>`__)."
 msgstr ""
 
@@ -16565,7 +16565,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:954
-msgid "`Read Flower Docs <https://flower.dev/docs/>`__"
+msgid "`Read Flower Docs <https://flower.ai/docs/>`__"
 msgstr ""
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:955
@@ -16577,12 +16577,12 @@ msgstr ""
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:956
 msgid ""
 "`Use Flower Baselines for your research "
-"<https://flower.dev/docs/baselines/>`__"
+"<https://flower.ai/docs/baselines/>`__"
 msgstr ""
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:957
 msgid ""
-"`Watch Flower Summit 2023 videos <https://flower.dev/conf/flower-"
+"`Watch Flower Summit 2023 videos <https://flower.ai/conf/flower-"
 "summit-2023/>`__"
 msgstr ""
 
@@ -16598,7 +16598,7 @@ msgstr ""
 #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:13
 msgid ""
 "In this notebook, we'll build a federated learning system using Flower, "
-"`Flower Datasets <https://flower.dev/docs/datasets/>`__ and PyTorch. In "
+"`Flower Datasets <https://flower.ai/docs/datasets/>`__ and PyTorch. In "
 "part 1, we use PyTorch for the model training pipeline and data loading. "
 "In part 2, we continue to federate the PyTorch-based pipeline using "
 "Flower."
@@ -17034,7 +17034,7 @@ msgstr ""
 #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:733
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 2 "
-"<https://flower.dev/docs/framework/tutorial-use-a-federated-learning-"
+"<https://flower.ai/docs/framework/tutorial-use-a-federated-learning-"
 "strategy-pytorch.html>`__ goes into more depth about strategies and all "
 "the advanced things you can build with them."
 msgstr ""
@@ -17047,7 +17047,7 @@ msgstr ""
 msgid ""
 "Welcome to the next part of the federated learning tutorial. In previous "
 "parts of this tutorial, we introduced federated learning with PyTorch and"
-" Flower (`part 1 <https://flower.dev/docs/framework/tutorial-get-started-"
+" Flower (`part 1 <https://flower.ai/docs/framework/tutorial-get-started-"
 "with-flower-pytorch.html>`__)."
 msgstr ""
 
@@ -17055,7 +17055,7 @@ msgstr ""
 msgid ""
 "In this notebook, we'll begin to customize the federated learning system "
 "we built in the introductory notebook (again, using `Flower "
-"<https://flower.dev/>`__ and `PyTorch <https://pytorch.org/>`__)."
+"<https://flower.ai/>`__ and `PyTorch <https://pytorch.org/>`__)."
 msgstr ""
 
 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:17
@@ -17253,7 +17253,7 @@ msgstr ""
 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:719
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 3 "
-"<https://flower.dev/docs/framework/tutorial-build-a-strategy-from-"
+"<https://flower.ai/docs/framework/tutorial-build-a-strategy-from-"
 "scratch-pytorch.html>`__ shows how to build a fully custom ``Strategy`` "
 "from scratch."
 msgstr ""
@@ -17281,7 +17281,7 @@ msgstr ""
 msgid ""
 "`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ and join "
 "the open-source Flower community on Slack to connect, ask questions, and "
-"get help: `Join Slack <https://flower.dev/join-slack>`__ 🌼 We'd love to "
+"get help: `Join Slack <https://flower.ai/join-slack>`__ 🌼 We'd love to "
 "hear from you in the ``#introductions`` channel! And if anything is "
 "unclear, head over to the ``#questions`` channel."
 msgstr ""
@@ -17797,7 +17797,7 @@ msgstr ""
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:373
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 1 "
-"<https://flower.dev/docs/framework/tutorial-get-started-with-flower-"
+"<https://flower.ai/docs/framework/tutorial-get-started-with-flower-"
 "pytorch.html>`__ shows how to build a simple federated learning system "
 "with PyTorch and Flower."
 msgstr ""
@@ -17897,10 +17897,10 @@ msgstr ""
 #~ "If you're familiar with how contributing"
 #~ " on GitHub works, you can directly"
 #~ " checkout our `getting started guide "
-#~ "for contributors <https://flower.dev/docs/getting-"
+#~ "for contributors <https://flower.ai/docs/getting-"
 #~ "started-for-contributors.html>`_ and examples "
 #~ "of `good first contributions "
-#~ "<https://flower.dev/docs/good-first-contributions.html>`_."
+#~ "<https://flower.ai/docs/good-first-contributions.html>`_."
 #~ msgstr ""
 
 #~ msgid ""
diff --git a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
index a627c41c9277..720de8578261 100644
--- a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
@@ -29,9 +29,9 @@ msgstr "边缘客户端引擎"
 
 #: ../../source/contributor-explanation-architecture.rst:7
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with Edge "
+"`Flower <https://flower.ai>`_ core framework architecture with Edge "
 "Client Engine"
-msgstr "具有边缘客户端引擎的`Flower <https://flower.dev>`核心架构"
+msgstr "具有边缘客户端引擎的`Flower <https://flower.ai>`核心架构"
 
 #: ../../source/contributor-explanation-architecture.rst:13
 msgid "Virtual Client Engine"
@@ -39,9 +39,9 @@ msgstr "虚拟客户端引擎"
 
 #: ../../source/contributor-explanation-architecture.rst:15
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with Virtual "
+"`Flower <https://flower.ai>`_ core framework architecture with Virtual "
 "Client Engine"
-msgstr "具有虚拟客户端引擎的`Flower <https://flower.dev>`核心架构"
+msgstr "具有虚拟客户端引擎的`Flower <https://flower.ai>`核心架构"
 
 #: ../../source/contributor-explanation-architecture.rst:21
 msgid "Virtual Client Engine and Edge Client Engine in the same workload"
@@ -49,9 +49,9 @@ msgstr "可同步进行的虚拟客户端引擎和边缘客户端引擎"
 
 #: ../../source/contributor-explanation-architecture.rst:23
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with both "
+"`Flower <https://flower.ai>`_ core framework architecture with both "
 "Virtual Client Engine and Edge Client Engine"
-msgstr "具有虚拟客户端引擎和边缘客户端引擎的`Flower <https://flower.dev>`核心架构"
+msgstr "具有虚拟客户端引擎和边缘客户端引擎的`Flower <https://flower.ai>`核心架构"
 
 #: ../../source/contributor-how-to-build-docker-images.rst:2
 msgid "How to build Docker Flower images locally"
@@ -85,7 +85,7 @@ msgstr ""
 #: ../../source/contributor-how-to-build-docker-images.rst:19
 msgid ""
 "Please follow the first section on `Run Flower using Docker "
-"<https://flower.dev/docs/framework/how-to-run-flower-using-docker>`_ "
+"<https://flower.ai/docs/framework/how-to-run-flower-using-docker>`_ "
 "which covers this step in more detail."
 msgstr ""
 
@@ -293,7 +293,7 @@ msgstr "贡献译文"
 
 #: ../../source/contributor-how-to-contribute-translations.rst:4
 msgid ""
-"Since `Flower 1.5 <https://flower.dev/docs/framework/ref-"
+"Since `Flower 1.5 <https://flower.ai/docs/framework/ref-"
 "changelog.html#v1-5-0-2023-08-31>`_ we have introduced translations to "
 "our doc pages, but, as you might have noticed, the translations are often"
 " imperfect. If you speak languages other than English, you might be able "
@@ -302,7 +302,7 @@ msgid ""
 "also be a great opportunity for those wanting to become open source "
 "contributors with little prerequistes."
 msgstr ""
-"从 `Flower 1.5 <https://flower.dev/docs/framework/ref-"
+"从 `Flower 1.5 <https://flower.ai/docs/framework/ref-"
 "changelog.html#v1-5-0-2023-08-31>`_ "
 "开始，我们在文档页面中引入了翻译，但正如你可能已经注意到的，这些翻译往往并不完美。如果您会说英语以外的语言，也许您可以帮助我们翻译这些文档，让更多的人了解"
 " Federated Learning！对于那些想成为开源贡献者的人来说，这也是一个很好的机会。"
@@ -407,10 +407,10 @@ msgstr "添加新语言"
 #: ../../source/contributor-how-to-contribute-translations.rst:69
 msgid ""
 "If you want to add a new language, you will first have to contact us, "
-"either on `Slack <https://flower.dev/join-slack>`_, or by opening an "
+"either on `Slack <https://flower.ai/join-slack>`_, or by opening an "
 "issue on our `GitHub repo <https://github.com/adap/flower>`_."
 msgstr ""
-"如果您想添加新语言，请先联系我们，可以在 `Slack <https://flower.dev/join-slack>`_ 上联系，也可以在我们的"
+"如果您想添加新语言，请先联系我们，可以在 `Slack <https://flower.ai/join-slack>`_ 上联系，也可以在我们的"
 " `GitHub repo <https://github.com/adap/flower>`_ 上提交问题。"
 
 #: ../../source/contributor-how-to-create-new-messages.rst:2
@@ -1225,10 +1225,10 @@ msgstr "Flower 基线申请"
 #: ../../source/contributor-ref-good-first-contributions.rst:25
 msgid ""
 "If you are not familiar with Flower Baselines, you should probably check-"
-"out our `contributing guide for baselines <https://flower.dev/docs"
+"out our `contributing guide for baselines <https://flower.ai/docs"
 "/contributing-baselines.html>`_."
 msgstr ""
-"如果您对 Flower Baselines 还不熟悉，也许应该看看我们的 \"基线贡献指南 <https://flower.dev/docs"
+"如果您对 Flower Baselines 还不熟悉，也许应该看看我们的 \"基线贡献指南 <https://flower.ai/docs"
 "/contributing-baselines.html>`_\"。"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:27
@@ -1330,12 +1330,12 @@ msgstr "本指南适用于想参与 Flower，但不习惯为 GitHub 项目贡献
 msgid ""
 "If you're familiar with how contributing on GitHub works, you can "
 "directly checkout our `getting started guide for contributors "
-"<https://flower.dev/docs/contributor-tutorial-get-started-"
+"<https://flower.ai/docs/contributor-tutorial-get-started-"
 "as-a-contributor.html>`_."
 msgstr ""
-"如果您熟悉如何在 GitHub 上贡献，可以直接查看我们的 \"贡献者入门指南\" <https://flower.dev/docs"
+"如果您熟悉如何在 GitHub 上贡献，可以直接查看我们的 \"贡献者入门指南\" <https://flower.ai/docs"
 "/getting-started-for-contributors.html>`_ 和 \"优秀的首次贡献示例\" "
-"<https://flower.dev/docs/good-first-contributions.html>`_。"
+"<https://flower.ai/docs/good-first-contributions.html>`_。"
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:11
 msgid "Setting up the repository"
@@ -1812,10 +1812,10 @@ msgstr "在 `.rst` 文件中进行修改（注意，标题下的破折号应与
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:280
 msgid ""
-"Build the docs and check the result: `<https://flower.dev/docs/writing-"
+"Build the docs and check the result: `<https://flower.ai/docs/writing-"
 "documentation.html#edit-an-existing-page>`_"
 msgstr ""
-"构建文档并检查结果： `<https://flower.dev/docs/writing-documentation.html#edit-an-"
+"构建文档并检查结果： `<https://flower.ai/docs/writing-documentation.html#edit-an-"
 "existing-page>`_"
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:283
@@ -1993,11 +1993,11 @@ msgstr "一旦您完成了第一份 PR，并希望做出更多贡献，请务必
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:346
 msgid ""
-"`Good first contributions <https://flower.dev/docs/framework/contributor-"
+"`Good first contributions <https://flower.ai/docs/framework/contributor-"
 "ref-good-first-contributions.html>`_, where you should particularly look "
 "into the :code:`baselines` contributions."
 msgstr ""
-"好的第一批贡献 <https://flower.dev/docs/framework/contributor-ref-good-first-"
+"好的第一批贡献 <https://flower.ai/docs/framework/contributor-ref-good-first-"
 "contributions.html>`_，在这里你应该特别看看 :code:`baselines` 的贡献。"
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:350
@@ -2351,13 +2351,13 @@ msgid ""
 "designed for non-iid data. We are using PyTorch to train a Convolutional "
 "Neural Network(with Batch Normalization layers) on the CIFAR-10 dataset. "
 "When applying FedBN, only few changes needed compared to `Example: "
-"PyTorch - From Centralized To Federated <https://flower.dev/docs/examples"
+"PyTorch - From Centralized To Federated <https://flower.ai/docs/examples"
 "/pytorch-from-centralized-to-federated.html>`_."
 msgstr ""
 "本教程将向您展示如何使用 Flower 为现有的机器学习框架构建一个联邦学习的版本，并使用 \"FedBN <https://github.com"
 "/med-air/FedBN>`_\"（一种针对非 iid 数据设计的联邦训练策略）。我们使用 PyTorch 在 CIFAR-10 "
 "数据集上训练一个卷积神经网络（带有Batch Normalization层）。在应用 FedBN 时，只需对 `示例: PyTorch - "
-"从集中式到联邦式 <https://flower.dev/docs/examples/pytorch-from-centralized-to-"
+"从集中式到联邦式 <https://flower.ai/docs/examples/pytorch-from-centralized-to-"
 "federated.html>`_ 做少量改动。"
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:9
@@ -2368,11 +2368,11 @@ msgstr "集中式训练"
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:10
 msgid ""
 "All files are revised based on `Example: PyTorch - From Centralized To "
-"Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-"
+"Federated <https://flower.ai/docs/examples/pytorch-from-centralized-to-"
 "federated.html>`_. The only thing to do is modifying the file called "
 ":code:`cifar.py`, revised part is shown below:"
 msgstr ""
-"所有文件均根据 `示例： PyTorch -从集中式到联邦式 <https://flower.dev/docs/examples/pytorch-"
+"所有文件均根据 `示例： PyTorch -从集中式到联邦式 <https://flower.ai/docs/examples/pytorch-"
 "from-centralized-to-federated.html>`_。唯一要做的就是修改名为 :code:`cifar.py` "
 "的文件，修改部分如下所示："
 
@@ -2405,17 +2405,17 @@ msgstr "联邦培训"
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:53
 msgid ""
 "If you have read `Example: PyTorch - From Centralized To Federated "
-"<https://flower.dev/docs/examples/pytorch-from-centralized-to-"
+"<https://flower.ai/docs/examples/pytorch-from-centralized-to-"
 "federated.html>`_, the following parts are easy to follow, onyl "
 ":code:`get_parameters` and :code:`set_parameters` function in "
 ":code:`client.py` needed to revise. If not, please read the `Example: "
-"PyTorch - From Centralized To Federated <https://flower.dev/docs/examples"
+"PyTorch - From Centralized To Federated <https://flower.ai/docs/examples"
 "/pytorch-from-centralized-to-federated.html>`_. first."
 msgstr ""
-"如果你读过 `示例： PyTorch - 从集中式到联邦式 <https://flower.dev/docs/examples/pytorch-"
+"如果你读过 `示例： PyTorch - 从集中式到联邦式 <https://flower.ai/docs/examples/pytorch-"
 "from-centralized-to-federated.html>`_，下面的部分就很容易理解了，只需要修改 "
 ":code:`get_parameters` 和 :code:`set_parameters` 中的 :code:`client.py` "
-"函数。如果没有，请阅读 `示例： PyTorch - 从集中式到联邦式 <https://flower.dev/docs/examples"
+"函数。如果没有，请阅读 `示例： PyTorch - 从集中式到联邦式 <https://flower.ai/docs/examples"
 "/pytorch-from-centralized-to-federated.html>`_。"
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:56
@@ -4714,14 +4714,14 @@ msgstr "在某些情况下，有必要向不同的客户端发送不同的配置
 msgid ""
 "This can be achieved by customizing an existing strategy or by "
 "`implementing a custom strategy from scratch "
-"<https://flower.dev/docs/framework/how-to-implement-strategies.html>`_. "
+"<https://flower.ai/docs/framework/how-to-implement-strategies.html>`_. "
 "Here's a nonsensical example that customizes :code:`FedAvg` by adding a "
 "custom ``\"hello\": \"world\"`` configuration key/value pair to the "
 "config dict of a *single client* (only the first client in the list, the "
 "other clients in this round to not receive this \"special\" config "
 "value):"
 msgstr ""
-"这可以通过定制现有策略或 `从头开始实施一个定制策略 <https://flower.dev/docs/framework/how-to-"
+"这可以通过定制现有策略或 `从头开始实施一个定制策略 <https://flower.ai/docs/framework/how-to-"
 "implement-"
 "strategies.html>`_来实现。下面是一个无厘头的例子，`FedAvg`通过在*单个客户端*的配置指令（config "
 "dict）中添加自定义的``\"hello\"： \"world\"``配置键/值对添加到此的配置 dict "
@@ -5339,10 +5339,10 @@ msgstr "安装Flower"
 #: ../../source/how-to-install-flower.rst:60
 #, fuzzy
 msgid ""
-"`How to run Flower using Docker <https://flower.dev/docs/framework/how-"
+"`How to run Flower using Docker <https://flower.ai/docs/framework/how-"
 "to-run-flower-using-docker.html>`_"
 msgstr ""
-"`TensorFlow快速入门 (教程) <https://flower.dev/docs/framework/tutorial-"
+"`TensorFlow快速入门 (教程) <https://flower.ai/docs/framework/tutorial-"
 "quickstart-tensorflow.html>`_"
 
 #: ../../source/how-to-install-flower.rst:63
@@ -5705,7 +5705,7 @@ msgstr ""
 msgid ""
 "The ``--insecure`` flag enables insecure communication (using HTTP, not "
 "HTTPS) and should only be used for testing purposes. We strongly "
-"recommend enabling `SSL <https://flower.dev/docs/framework/how-to-run-"
+"recommend enabling `SSL <https://flower.ai/docs/framework/how-to-run-"
 "flower-using-docker.html#enabling-ssl-for-secure-connections>`_ when "
 "deploying to a production environment."
 msgstr ""
@@ -5753,7 +5753,7 @@ msgstr ""
 #: ../../source/how-to-run-flower-using-docker.rst:92
 msgid ""
 "For testing purposes, you can generate your own self-signed certificates."
-" The `Enable SSL connections <https://flower.dev/docs/framework/how-to-"
+" The `Enable SSL connections <https://flower.ai/docs/framework/how-to-"
 "enable-ssl-connections.html#certificates>`_ page contains a section that "
 "will guide you through the process."
 msgstr ""
@@ -6671,11 +6671,11 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples>`_ are already updated"
 " to Flower 1.0, they can serve as a reference for using the Flower 1.0 "
 "API. If there are further questionsm, `join the Flower Slack "
-"<https://flower.dev/join-slack/>`_ and use the channgel ``#questions``."
+"<https://flower.ai/join-slack/>`_ and use the channgel ``#questions``."
 msgstr ""
 "大多数官方的 `Flower 代码示例 <https://github.com/adap/flower/tree/main/examples>`_"
 " 已经更新到 Flower 1.0，它们可以作为使用 Flower 1.0 API 的参考。如果还有其他问题，请加入 Flower Slack "
-"<https://flower.dev/join-slack/>`_ 并使用 \"#questions``\"。"
+"<https://flower.ai/join-slack/>`_ 并使用 \"#questions``\"。"
 
 #: ../../source/how-to-use-built-in-mods.rst:2
 msgid "Use Built-in Mods"
@@ -6965,9 +6965,9 @@ msgstr "Flower 框架文档"
 
 #: ../../source/index.rst:7
 msgid ""
-"Welcome to Flower's documentation. `Flower <https://flower.dev>`_ is a "
+"Welcome to Flower's documentation. `Flower <https://flower.ai>`_ is a "
 "friendly federated learning framework."
-msgstr "欢迎访问 Flower 文档。`Flower <https://flower.dev>`_ 是一个友好的联邦学习框架。"
+msgstr "欢迎访问 Flower 文档。`Flower <https://flower.ai>`_ 是一个友好的联邦学习框架。"
 
 #: ../../source/index.rst:11
 msgid "Join the Flower Community"
@@ -11681,7 +11681,7 @@ msgstr ""
 msgid ""
 "The Flower server can now be run using an official Docker image. A new "
 "how-to guide explains [how to run Flower using "
-"Docker](https://flower.dev/docs/framework/how-to-run-flower-using-"
+"Docker](https://flower.ai/docs/framework/how-to-run-flower-using-"
 "docker.html). An official Flower client Docker image will follow."
 msgstr ""
 
@@ -11725,7 +11725,7 @@ msgstr ""
 #: ../../source/ref-changelog.md:55
 msgid ""
 "Several code examples were updated to use [Flower "
-"Datasets](https://flower.dev/docs/datasets/)."
+"Datasets](https://flower.ai/docs/datasets/)."
 msgstr ""
 
 #: ../../source/ref-changelog.md:57
@@ -12473,17 +12473,17 @@ msgstr ""
 #: ../../source/ref-changelog.md:216
 msgid ""
 "Comprehensive documentation includes a new [how-to run "
-"simulations](https://flower.dev/docs/framework/how-to-run-"
+"simulations](https://flower.ai/docs/framework/how-to-run-"
 "simulations.html) guide, new [simulation-"
-"pytorch](https://flower.dev/docs/examples/simulation-pytorch.html) and "
-"[simulation-tensorflow](https://flower.dev/docs/examples/simulation-"
+"pytorch](https://flower.ai/docs/examples/simulation-pytorch.html) and "
+"[simulation-tensorflow](https://flower.ai/docs/examples/simulation-"
 "tensorflow.html) notebooks, and a new [YouTube tutorial "
 "series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)."
 msgstr ""
-"综合文档包括新的[how-to run simulations](https://flower.dev/docs/framework/how-"
+"综合文档包括新的[how-to run simulations](https://flower.ai/docs/framework/how-"
 "to-run-simulations.html) guide, new [simulation-"
-"pytorch](https://flower.dev/docs/examples/simulation-pytorch.html) and "
-"[simulation-tensorflow](https://flower.dev/docs/examples/simulation-"
+"pytorch](https://flower.ai/docs/examples/simulation-pytorch.html) and "
+"[simulation-tensorflow](https://flower.ai/docs/examples/simulation-"
 "tensorflow.html) notebooks, and a new [YouTube tutorial "
 "series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)。"
 
@@ -12549,11 +12549,11 @@ msgstr ""
 #: ../../source/ref-changelog.md:220
 msgid ""
 "Much effort went into a completely restructured Flower docs experience. "
-"The documentation on [flower.dev/docs](flower.dev/docs) is now divided "
+"The documentation on [flower.ai/docs](flower.ai/docs) is now divided "
 "into Flower Framework, Flower Baselines, Flower Android SDK, Flower iOS "
 "SDK, and code example projects."
 msgstr ""
-"Flower 文档体验的全面重构耗费了大量精力。现在，[flower.dev/docs](flower.dev/docs)上的文档分为 "
+"Flower 文档体验的全面重构耗费了大量精力。现在，[flower.ai/docs](flower.ai/docs)上的文档分为 "
 "Flower Framework、Flower Baselines、Flower Android SDK、Flower iOS SDK "
 "和代码示例项目。"
 
@@ -12944,13 +12944,13 @@ msgstr ""
 
 #: ../../source/ref-changelog.md:306
 msgid ""
-"A new [entry-level tutorial](https://flower.dev/docs/framework/tutorial-"
+"A new [entry-level tutorial](https://flower.ai/docs/framework/tutorial-"
 "what-is-federated-learning.html) in our documentation explains the basics"
 " of Fedetated Learning. It enables anyone who's unfamiliar with Federated"
 " Learning to start their journey with Flower. Forward it to anyone who's "
 "interested in Federated Learning!"
 msgstr ""
-"我们的文档中新增了一个[入门级教程](https://flower.dev/docs/framework/tutorial-what-is-"
+"我们的文档中新增了一个[入门级教程](https://flower.ai/docs/framework/tutorial-what-is-"
 "federated-learning.html)，解释了联邦学习的基础知识。它让任何不熟悉联邦学习的人都能开始 Flower "
 "之旅。请转发给对联邦学习感兴趣的人！"
 
@@ -13518,12 +13518,12 @@ msgid ""
 "revisit well known papers from the literature, and be suitable for "
 "integration in your own application or for experimentation, in order to "
 "deepen your knowledge of FL in general. Today's release is the first in "
-"this series. [Read more.](https://flower.dev/blog/2023-01-12-fl-starter-"
+"this series. [Read more.](https://flower.ai/blog/2023-01-12-fl-starter-"
 "pack-fedavg-mnist-cnn/)"
 msgstr ""
 "在未来几周内，我们将发布一些新的参考，特别是对 FL "
 "新手有用的方法。它们通常会重温文献中的知名论文，适合集成到您自己的应用程序中或用于实验，以加深您对 FL "
-"的总体了解。今天发布的是该系列中的第一篇。[阅读全文](https://flower.dev/blog/2023-01-12-fl-"
+"的总体了解。今天发布的是该系列中的第一篇。[阅读全文](https://flower.ai/blog/2023-01-12-fl-"
 "starter-pack-fedavg-mnist-cnn/)"
 
 #: ../../source/ref-changelog.md:422
@@ -13563,34 +13563,34 @@ msgstr ""
 
 #: ../../source/ref-changelog.md:430
 msgid ""
-"[An Introduction to Federated Learning](https://flower.dev/docs/framework"
+"[An Introduction to Federated Learning](https://flower.ai/docs/framework"
 "/tutorial-get-started-with-flower-pytorch.html)"
 msgstr ""
-"[联邦学习简介](https://flower.dev/docs/framework/tutorial-get-started-with-"
+"[联邦学习简介](https://flower.ai/docs/framework/tutorial-get-started-with-"
 "flower-pytorch.html)"
 
 #: ../../source/ref-changelog.md:431
 msgid ""
-"[Strategies in Federated Learning](https://flower.dev/docs/framework"
+"[Strategies in Federated Learning](https://flower.ai/docs/framework"
 "/tutorial-use-a-federated-learning-strategy-pytorch.html)"
 msgstr ""
-"[联邦学习策略](https://flower.dev/docs/framework/tutorial-use-a-federated-"
+"[联邦学习策略](https://flower.ai/docs/framework/tutorial-use-a-federated-"
 "learning-strategy-pytorch.html)"
 
 #: ../../source/ref-changelog.md:432
 msgid ""
-"[Building a Strategy](https://flower.dev/docs/framework/tutorial-build-a"
+"[Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a"
 "-strategy-from-scratch-pytorch.html)"
 msgstr ""
-"[制定策略](https://flower.dev/docs/framework/tutorial-build-a-strategy-from-"
+"[制定策略](https://flower.ai/docs/framework/tutorial-build-a-strategy-from-"
 "scratch-pytorch.html)"
 
 #: ../../source/ref-changelog.md:433
 msgid ""
-"[Client and NumPyClient](https://flower.dev/docs/framework/tutorial-"
+"[Client and NumPyClient](https://flower.ai/docs/framework/tutorial-"
 "customize-the-client-pytorch.html)"
 msgstr ""
-"[客户端和 NumPyClient](https://flower.dev/docs/framework/tutorial-customize-"
+"[客户端和 NumPyClient](https://flower.ai/docs/framework/tutorial-customize-"
 "the-client-pytorch.html)"
 
 #: ../../source/ref-changelog.md:435
@@ -13622,10 +13622,10 @@ msgid ""
 "**Flower is a friendly framework for collaborative AI and data science.**"
 " Staying true to this statement, Flower makes it easy to disable "
 "telemetry for users who do not want to share anonymous usage metrics. "
-"[Read more.](https://flower.dev/docs/telemetry.html)."
+"[Read more.](https://flower.ai/docs/telemetry.html)."
 msgstr ""
 "**Flower 是一个用于协作式人工智能和数据科学的友好框架。** Flower "
-"遵循这一声明，让不想分享匿名使用指标的用户可以轻松禁用遥测技术。[阅读全文](https://flower.dev/docs/telemetry.html)。"
+"遵循这一声明，让不想分享匿名使用指标的用户可以轻松禁用遥测技术。[阅读全文](https://flower.ai/docs/telemetry.html)。"
 
 #: ../../source/ref-changelog.md:441
 msgid ""
@@ -13787,10 +13787,10 @@ msgstr ""
 #: ../../source/ref-changelog.md:471
 msgid ""
 "One highlight is the new [first time contributor "
-"guide](https://flower.dev/docs/first-time-contributors.html): if you've "
+"guide](https://flower.ai/docs/first-time-contributors.html): if you've "
 "never contributed on GitHub before, this is the perfect place to start!"
 msgstr ""
-"其中一个亮点是新的[首次贡献者指南](https://flower.dev/docs/first-time-"
+"其中一个亮点是新的[首次贡献者指南](https://flower.ai/docs/first-time-"
 "contributors.html)：如果你以前从未在 GitHub 上做过贡献，这将是一个完美的开始！"
 
 #: ../../source/ref-changelog.md:477
@@ -14590,15 +14590,15 @@ msgid ""
 "The first preview release of Flower Baselines has arrived! We're "
 "kickstarting Flower Baselines with implementations of FedOpt (FedYogi, "
 "FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how "
-"to use [Flower Baselines](https://flower.dev/docs/using-baselines.html). "
+"to use [Flower Baselines](https://flower.ai/docs/using-baselines.html). "
 "With this first preview release we're also inviting the community to "
-"[contribute their own baselines](https://flower.dev/docs/contributing-"
+"[contribute their own baselines](https://flower.ai/docs/contributing-"
 "baselines.html)."
 msgstr ""
 "Flower Baselines 的第一个预览版已经发布！我们通过实现 "
 "FedOpt（FedYogi、FedAdam、FedAdagrad）、FedBN 和 FedAvgM 来启动 Flower "
-"Baselines。请查阅文档了解如何使用 [Flower Baselines](https://flower.dev/docs/using-"
-"baselines.html)。在首次发布预览版时，我们还邀请社区成员[贡献自己的Baselines](https://flower.dev/docs"
+"Baselines。请查阅文档了解如何使用 [Flower Baselines](https://flower.ai/docs/using-"
+"baselines.html)。在首次发布预览版时，我们还邀请社区成员[贡献自己的Baselines](https://flower.ai/docs"
 "/contributing-baselines.html)。"
 
 #: ../../source/ref-changelog.md:662
@@ -14779,11 +14779,11 @@ msgstr ""
 #: ../../source/ref-changelog.md:706
 msgid ""
 "New documentation for [implementing "
-"strategies](https://flower.dev/docs/framework/how-to-implement-"
+"strategies](https://flower.ai/docs/framework/how-to-implement-"
 "strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), "
 "[#1175](https://github.com/adap/flower/pull/1175))"
 msgstr ""
-"[实施战略](https://flower.dev/docs/framework/how-to-implement-"
+"[实施战略](https://flower.ai/docs/framework/how-to-implement-"
 "strategies.html) 的新文件（[#1097](https://github.com/adap/flower/pull/1097), "
 "[#1175](https://github.com/adap/flower/pull/1175)"
 
@@ -15807,18 +15807,18 @@ msgstr ""
 
 #: ../../source/ref-example-projects.rst:26
 msgid ""
-"`Quickstart TensorFlow (Tutorial) <https://flower.dev/docs/framework"
+"`Quickstart TensorFlow (Tutorial) <https://flower.ai/docs/framework"
 "/tutorial-quickstart-tensorflow.html>`_"
 msgstr ""
-"`TensorFlow快速入门 (教程) <https://flower.dev/docs/framework/tutorial-"
+"`TensorFlow快速入门 (教程) <https://flower.ai/docs/framework/tutorial-"
 "quickstart-tensorflow.html>`_"
 
 #: ../../source/ref-example-projects.rst:27
 msgid ""
-"`Quickstart TensorFlow (Blog Post) <https://flower.dev/blog/2020-12-11"
+"`Quickstart TensorFlow (Blog Post) <https://flower.ai/blog/2020-12-11"
 "-federated-learning-in-less-than-20-lines-of-code>`_"
 msgstr ""
-"`TensorFlow快速入门 (博客) <https://flower.dev/blog/2020-12-11-federated-"
+"`TensorFlow快速入门 (博客) <https://flower.ai/blog/2020-12-11-federated-"
 "learning-in-less-than-20-lines-of-code>`_"
 
 #: ../../source/ref-example-projects.rst:31
@@ -15842,10 +15842,10 @@ msgstr ""
 
 #: ../../source/ref-example-projects.rst:37
 msgid ""
-"`Quickstart PyTorch (Tutorial) <https://flower.dev/docs/framework"
+"`Quickstart PyTorch (Tutorial) <https://flower.ai/docs/framework"
 "/tutorial-quickstart-pytorch.html>`_"
 msgstr ""
-"`PyTorch快速入门 (教程) <https://flower.dev/docs/framework/tutorial-quickstart-"
+"`PyTorch快速入门 (教程) <https://flower.ai/docs/framework/tutorial-quickstart-"
 "pytorch.html>`_"
 
 #: ../../source/ref-example-projects.rst:41
@@ -15870,10 +15870,10 @@ msgstr ""
 #: ../../source/ref-example-projects.rst:46
 msgid ""
 "`PyTorch: From Centralized To Federated (Tutorial) "
-"<https://flower.dev/docs/framework/example-pytorch-from-centralized-to-"
+"<https://flower.ai/docs/framework/example-pytorch-from-centralized-to-"
 "federated.html>`_"
 msgstr ""
-"PyTorch： 从集中式到联邦式（教程） <https://flower.dev/docs/framework/example-pytorch-"
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 "from-centralized-to-federated.html>`_"
 
 #: ../../source/ref-example-projects.rst:50
@@ -15897,10 +15897,10 @@ msgstr ""
 #: ../../source/ref-example-projects.rst:55
 msgid ""
 "`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 msgstr ""
 "Raspberry Pi和 Nvidia Jetson 上的联邦学习（博客） "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 
 #: ../../source/ref-example-projects.rst:60
 msgid "Legacy Examples (`flwr_example`)"
@@ -16104,12 +16104,12 @@ msgstr ":fa:`eye,mr-1` 如何在 Raspberry Pi 上运行联邦学习？"
 #: ../../source/ref-faq.rst:15
 msgid ""
 "Find the `blog post about federated learning on embedded device here "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 " and the corresponding `GitHub code example "
 "<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_."
 msgstr ""
 "请点击此处查看有关嵌入式设备联邦学习的 "
-"\"博文\"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_和相应的"
+"\"博文\"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_和相应的"
 " \"GitHub 代码示例\"<https://github.com/adap/flower/tree/main/examples"
 "/embedded-devices>`_。"
 
@@ -16120,23 +16120,23 @@ msgstr ":fa:`eye,mr-1` Flower 是否支持安卓设备上的联邦学习？"
 #: ../../source/ref-faq.rst:19
 msgid ""
 "Yes, it does. Please take a look at our `blog post "
-"<https://flower.dev/blog/2021-12-15-federated-learning-on-android-"
+"<https://flower.ai/blog/2021-12-15-federated-learning-on-android-"
 "devices-with-flower>`_ or check out the code examples:"
 msgstr ""
-"是的，确实如此。请查看我们的 \"博客文章 <https://flower.dev/blog/2021-12-15-federated-"
+"是的，确实如此。请查看我们的 \"博客文章 <https://flower.ai/blog/2021-12-15-federated-"
 "learning-on-android-devices-with-flower>`_\" 或查看代码示例："
 
 #: ../../source/ref-faq.rst:21
 msgid ""
-"`Android Kotlin example <https://flower.dev/docs/examples/android-"
+"`Android Kotlin example <https://flower.ai/docs/examples/android-"
 "kotlin.html>`_"
 msgstr ""
-"`Android Kotlin 示例 <https://flower.dev/docs/examples/android-"
+"`Android Kotlin 示例 <https://flower.ai/docs/examples/android-"
 "kotlin.html>`_"
 
 #: ../../source/ref-faq.rst:22
-msgid "`Android Java example <https://flower.dev/docs/examples/android.html>`_"
-msgstr "Android Java 示例 <https://flower.dev/docs/examples/android.html>`_"
+msgid "`Android Java example <https://flower.ai/docs/examples/android.html>`_"
+msgstr "Android Java 示例 <https://flower.ai/docs/examples/android.html>`_"
 
 #: ../../source/ref-faq.rst
 msgid ":fa:`eye,mr-1` Can I combine federated learning with blockchain?"
@@ -16347,10 +16347,10 @@ msgstr ""
 msgid ""
 "You may delete the source ID at any time. If you wish for all events "
 "logged under a specific source ID to be deleted, you can send a deletion "
-"request mentioning the source ID to `telemetry@flower.dev`. All events "
+"request mentioning the source ID to `telemetry@flower.ai`. All events "
 "related to that source ID will then be permanently deleted."
 msgstr ""
-"您可以随时删除源 ID。如果您希望删除特定源 ID 下记录的所有事件，可以向 `telemetry@flower.dev` "
+"您可以随时删除源 ID。如果您希望删除特定源 ID 下记录的所有事件，可以向 `telemetry@flower.ai` "
 "发送删除请求，并提及该源 ID。届时，与该源 ID 相关的所有事件都将被永久删除。"
 
 #: ../../source/ref-telemetry.md:46
@@ -16402,11 +16402,11 @@ msgstr "如何联系我们"
 msgid ""
 "We want to hear from you. If you have any feedback or ideas on how to "
 "improve the way we handle anonymous usage metrics, reach out to us via "
-"[Slack](https://flower.dev/join-slack/) (channel `#telemetry`) or email "
-"(`telemetry@flower.dev`)."
+"[Slack](https://flower.ai/join-slack/) (channel `#telemetry`) or email "
+"(`telemetry@flower.ai`)."
 msgstr ""
-"我们希望听到您的意见。如果您对如何改进我们处理匿名使用指标的方式有任何反馈或想法，请通过 [Slack](https://flower.dev"
-"/join-slack/) （频道 `#telemetry`）或电子邮件 (`telemetry@flower.dev`)与我们联系。"
+"我们希望听到您的意见。如果您对如何改进我们处理匿名使用指标的方式有任何反馈或想法，请通过 [Slack](https://flower.ai"
+"/join-slack/) （频道 `#telemetry`）或电子邮件 (`telemetry@flower.ai`)与我们联系。"
 
 #: ../../source/tutorial-quickstart-android.rst:-1
 msgid ""
@@ -16654,11 +16654,11 @@ msgstr "在本教程中，我们将学习如何在 iOS 设备上使用 Flower 
 msgid ""
 "First of all, for running the Flower Python server, it is recommended to "
 "create a virtual environment and run everything within a `virtualenv "
-"<https://flower.dev/docs/recommended-env-setup.html>`_. For the Flower "
+"<https://flower.ai/docs/recommended-env-setup.html>`_. For the Flower "
 "client implementation in iOS, it is recommended to use Xcode as our IDE."
 msgstr ""
 "首先，为了运行 Flower Python 服务器，建议创建一个虚拟环境，并在 `virtualenv "
-"<https://flower.dev/docs/recommended-env-setup.html>`_ 中运行一切。对于在 iOS 中实现 "
+"<https://flower.ai/docs/recommended-env-setup.html>`_ 中运行一切。对于在 iOS 中实现 "
 "Flower 客户端，建议使用 Xcode 作为我们的集成开发环境。"
 
 #: ../../source/tutorial-quickstart-ios.rst:15
@@ -16863,10 +16863,10 @@ msgstr "在本教程中，我们将学习如何使用 Flower 和 MXNet 在 MNIST
 #: ../../source/tutorial-quickstart-scikitlearn.rst:12
 msgid ""
 "It is recommended to create a virtual environment and run everything "
-"within this `virtualenv <https://flower.dev/docs/recommended-env-"
+"within this `virtualenv <https://flower.ai/docs/recommended-env-"
 "setup.html>`_."
 msgstr ""
-"建议创建一个虚拟环境，并在此 `virtualenv <https://flower.dev/docs/recommended-env-"
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 "setup.html>`_ 中运行所有内容。"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:18
@@ -17137,10 +17137,10 @@ msgstr "在本教程中，我们将学习如何使用 Flower 和 PyTorch 在 CIF
 #: ../../source/tutorial-quickstart-xgboost.rst:39
 msgid ""
 "First of all, it is recommended to create a virtual environment and run "
-"everything within a `virtualenv <https://flower.dev/docs/recommended-env-"
+"everything within a `virtualenv <https://flower.ai/docs/recommended-env-"
 "setup.html>`_."
 msgstr ""
-"首先，建议创建一个虚拟环境，并在 `virtualenv <https://flower.dev/docs/recommended-env-"
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 "setup.html>`_ 中运行一切。"
 
 #: ../../source/tutorial-quickstart-pytorch.rst:29
@@ -17966,7 +17966,7 @@ msgid ""
 "comprehensive>`_), we provide more options to define various experimental"
 " setups, including aggregation strategies, data partitioning and "
 "centralised/distributed evaluation. We also support `Flower simulation "
-"<https://flower.dev/docs/framework/how-to-run-simulations.html>`_ making "
+"<https://flower.ai/docs/framework/how-to-run-simulations.html>`_ making "
 "it easy to simulate large client cohorts in a resource-aware manner. "
 "Let's take a look!"
 msgstr ""
@@ -18191,26 +18191,26 @@ msgstr "从零开始制定策略"
 msgid ""
 "Welcome to the third part of the Flower federated learning tutorial. In "
 "previous parts of this tutorial, we introduced federated learning with "
-"PyTorch and Flower (`part 1 <https://flower.dev/docs/framework/tutorial-"
+"PyTorch and Flower (`part 1 <https://flower.ai/docs/framework/tutorial-"
 "get-started-with-flower-pytorch.html>`__) and we learned how strategies "
 "can be used to customize the execution on both the server and the clients"
-" (`part 2 <https://flower.dev/docs/framework/tutorial-use-a-federated-"
+" (`part 2 <https://flower.ai/docs/framework/tutorial-use-a-federated-"
 "learning-strategy-pytorch.html>`__)."
 msgstr ""
 "欢迎来到 Flower 联邦学习教程的第三部分。在本教程的前几部分，我们介绍了 PyTorch 和 Flower 的联邦学习（`part 1 "
-"<https://flower.dev/docs/framework/tutorial-get-started-with-flower-"
+"<https://flower.ai/docs/framework/tutorial-get-started-with-flower-"
 "pytorch.html>`__），并学习了如何使用策略来定制服务器和客户端的执行（`part 2 "
-"<https://flower.dev/docs/framework/tutorial-use-a-federated-learning-"
+"<https://flower.ai/docs/framework/tutorial-use-a-federated-learning-"
 "strategy-pytorch.html>`__）。"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:13
 msgid ""
 "In this notebook, we'll continue to customize the federated learning "
 "system we built previously by creating a custom version of FedAvg (again,"
-" using `Flower <https://flower.dev/>`__ and `PyTorch "
+" using `Flower <https://flower.ai/>`__ and `PyTorch "
 "<https://pytorch.org/>`__)."
 msgstr ""
-"在本笔记中，我们将通过创建 FedAvg 的自定义版本（再次使用 `Flower <https://flower.dev/>`__ 和 "
+"在本笔记中，我们将通过创建 FedAvg 的自定义版本（再次使用 `Flower <https://flower.ai/>`__ 和 "
 "`PyTorch <https://pytorch.org/>`__），继续定制我们之前构建的联邦学习系统。"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:15
@@ -18220,12 +18220,12 @@ msgstr ""
 msgid ""
 "`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ and join "
 "the Flower community on Slack to connect, ask questions, and get help: "
-"`Join Slack <https://flower.dev/join-slack>`__ 🌼 We'd love to hear from "
+"`Join Slack <https://flower.ai/join-slack>`__ 🌼 We'd love to hear from "
 "you in the ``#introductions`` channel! And if anything is unclear, head "
 "over to the ``#questions`` channel."
 msgstr ""
 "`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ 并加入 Slack "
-"上的 Flower 社区，进行交流、提问并获得帮助： 加入 Slack <https://flower.dev/join-slack>`__ 🌼 "
+"上的 Flower 社区，进行交流、提问并获得帮助： 加入 Slack <https://flower.ai/join-slack>`__ 🌼 "
 "我们希望在 ``#introductions`` 频道听到您的声音！如果有任何不清楚的地方，请访问 ``#questions`` 频道。"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:17
@@ -18387,9 +18387,9 @@ msgstr ""
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:369
 msgid ""
 "Before you continue, make sure to join the Flower community on Slack: "
-"`Join Slack <https://flower.dev/join-slack/>`__"
+"`Join Slack <https://flower.ai/join-slack/>`__"
 msgstr ""
-"在继续之前，请务必加入 Slack 上的 Flower 社区：`Join Slack <https://flower.dev/join-"
+"在继续之前，请务必加入 Slack 上的 Flower 社区：`Join Slack <https://flower.ai/join-"
 "slack/>`__"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:552
@@ -18405,11 +18405,11 @@ msgstr "如果您需要帮助，我们有专门的 ``#questions`` 频道，但
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:554
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 4 "
-"<https://flower.dev/docs/framework/tutorial-customize-the-client-"
+"<https://flower.ai/docs/framework/tutorial-customize-the-client-"
 "pytorch.html>`__ introduces ``Client``, the flexible API underlying "
 "``NumPyClient``."
 msgstr ""
-"Flower联邦学习教程 - 第4部分 <https://flower.dev/docs/framework/tutorial-"
+"Flower联邦学习教程 - 第4部分 <https://flower.ai/docs/framework/tutorial-"
 "customize-the-client-pytorch.html>`__ "
 "介绍了``Client``，它是``NumPyClient``底层的灵活应用程序接口。"
 
@@ -18421,20 +18421,20 @@ msgstr "自定义客户端"
 msgid ""
 "Welcome to the fourth part of the Flower federated learning tutorial. In "
 "the previous parts of this tutorial, we introduced federated learning "
-"with PyTorch and Flower (`part 1 <https://flower.dev/docs/framework"
+"with PyTorch and Flower (`part 1 <https://flower.ai/docs/framework"
 "/tutorial-get-started-with-flower-pytorch.html>`__), we learned how "
 "strategies can be used to customize the execution on both the server and "
-"the clients (`part 2 <https://flower.dev/docs/framework/tutorial-use-a"
+"the clients (`part 2 <https://flower.ai/docs/framework/tutorial-use-a"
 "-federated-learning-strategy-pytorch.html>`__), and we built our own "
-"custom strategy from scratch (`part 3 <https://flower.dev/docs/framework"
+"custom strategy from scratch (`part 3 <https://flower.ai/docs/framework"
 "/tutorial-build-a-strategy-from-scratch-pytorch.html>`__)."
 msgstr ""
 "欢迎来到 Flower 联邦学习教程的第四部分。在本教程的前几部分中，我们介绍了 PyTorch 和 Flower 的联邦学习（`part 1 "
-"<https://flower.dev/docs/framework/tutorial-get-started-with-flower-"
+"<https://flower.ai/docs/framework/tutorial-get-started-with-flower-"
 "pytorch.html>`__），了解了如何使用策略来定制服务器和客户端的执行（`part 2 "
-"<https://flower.dev/docs/framework/tutorial-use-a-federated-learning-"
+"<https://flower.ai/docs/framework/tutorial-use-a-federated-learning-"
 "strategy-pytorch.html>`__），并从头开始构建了我们自己的定制策略（`part 3 "
-"<https://flower.dev/docs/framework/tutorial-build-a-strategy-from-"
+"<https://flower.ai/docs/framework/tutorial-build-a-strategy-from-"
 "scratch-pytorch.html>`__）。"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:14
@@ -18759,8 +18759,8 @@ msgid ""
 msgstr "这暂时是 Flower 教程的最后一部分，恭喜您！您现在已经具备了理解其余文档的能力。本教程还有许多内容没有涉及，我们推荐您参考以下资源："
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:954
-msgid "`Read Flower Docs <https://flower.dev/docs/>`__"
-msgstr "阅读Flower文档 <https://flower.dev/docs/>`__"
+msgid "`Read Flower Docs <https://flower.ai/docs/>`__"
+msgstr "阅读Flower文档 <https://flower.ai/docs/>`__"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:955
 msgid ""
@@ -18771,14 +18771,14 @@ msgstr "查看 Flower 代码示例 <https://github.com/adap/flower/tree/main/exa
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:956
 msgid ""
 "`Use Flower Baselines for your research "
-"<https://flower.dev/docs/baselines/>`__"
-msgstr "使用 \"Flower Baselines \"进行研究 <https://flower.dev/docs/baselines/>`__"
+"<https://flower.ai/docs/baselines/>`__"
+msgstr "使用 \"Flower Baselines \"进行研究 <https://flower.ai/docs/baselines/>`__"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:957
 msgid ""
-"`Watch Flower Summit 2023 videos <https://flower.dev/conf/flower-"
+"`Watch Flower Summit 2023 videos <https://flower.ai/conf/flower-"
 "summit-2023/>`__"
-msgstr "观看 2023 年Flower峰会视频 <https://flower.dev/conf/flower-summit-2023/>`__"
+msgstr "观看 2023 年Flower峰会视频 <https://flower.ai/conf/flower-summit-2023/>`__"
 
 #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:9
 msgid "Get started with Flower"
@@ -18793,7 +18793,7 @@ msgstr "欢迎阅读Flower联邦学习教程！"
 #, fuzzy
 msgid ""
 "In this notebook, we'll build a federated learning system using Flower, "
-"`Flower Datasets <https://flower.dev/docs/datasets/>`__ and PyTorch. In "
+"`Flower Datasets <https://flower.ai/docs/datasets/>`__ and PyTorch. In "
 "part 1, we use PyTorch for the model training pipeline and data loading. "
 "In part 2, we continue to federate the PyTorch-based pipeline using "
 "Flower."
@@ -19311,11 +19311,11 @@ msgstr "在下一个笔记中，我们将介绍一些更先进的概念。想定
 #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:733
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 2 "
-"<https://flower.dev/docs/framework/tutorial-use-a-federated-learning-"
+"<https://flower.ai/docs/framework/tutorial-use-a-federated-learning-"
 "strategy-pytorch.html>`__ goes into more depth about strategies and all "
 "the advanced things you can build with them."
 msgstr ""
-"`Flower 联邦学习教程 - 第 2 部分 <https://flower.dev/docs/framework/tutorial-use-a"
+"`Flower 联邦学习教程 - 第 2 部分 <https://flower.ai/docs/framework/tutorial-use-a"
 "-federated-learning-strategy-pytorch.html>`__ 更深入地介绍了策略以及可以使用策略构建的所有高级功能。"
 
 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:9
@@ -19326,20 +19326,20 @@ msgstr "使用联邦学习策略"
 msgid ""
 "Welcome to the next part of the federated learning tutorial. In previous "
 "parts of this tutorial, we introduced federated learning with PyTorch and"
-" Flower (`part 1 <https://flower.dev/docs/framework/tutorial-get-started-"
+" Flower (`part 1 <https://flower.ai/docs/framework/tutorial-get-started-"
 "with-flower-pytorch.html>`__)."
 msgstr ""
 "欢迎来到联邦学习教程的下一部分。在本教程的前几部分，我们介绍了使用 PyTorch 和 Flower 进行联邦学习（`第 1 部分 "
-"<https://flower.dev/docs/framework/tutorial-get-started-with-flower-"
+"<https://flower.ai/docs/framework/tutorial-get-started-with-flower-"
 "pytorch.html>`___）。"
 
 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:13
 msgid ""
 "In this notebook, we'll begin to customize the federated learning system "
 "we built in the introductory notebook (again, using `Flower "
-"<https://flower.dev/>`__ and `PyTorch <https://pytorch.org/>`__)."
+"<https://flower.ai/>`__ and `PyTorch <https://pytorch.org/>`__)."
 msgstr ""
-"在本笔记中，我们将开始定制在入门笔记中构建的联邦学习系统（再次使用 `Flower <https://flower.dev/>`__ 和 "
+"在本笔记中，我们将开始定制在入门笔记中构建的联邦学习系统（再次使用 `Flower <https://flower.ai/>`__ 和 "
 "`PyTorch <https://pytorch.org/>`__）。"
 
 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:17
@@ -19565,11 +19565,11 @@ msgstr ""
 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:719
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 3 "
-"<https://flower.dev/docs/framework/tutorial-build-a-strategy-from-"
+"<https://flower.ai/docs/framework/tutorial-build-a-strategy-from-"
 "scratch-pytorch.html>`__ shows how to build a fully custom ``Strategy`` "
 "from scratch."
 msgstr ""
-"`Flower 联邦学习教程 - 第 3 部分 <https://flower.dev/docs/framework/tutorial-"
+"`Flower 联邦学习教程 - 第 3 部分 <https://flower.ai/docs/framework/tutorial-"
 "build-a-strategy-from-scratch-pytorch.html>`__ 展示了如何从头开始构建完全自定义的 \"策略\"。"
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:9
@@ -19597,12 +19597,12 @@ msgstr "🧑‍🏫 本教程从零开始，不要求熟悉联邦学习。仅假
 msgid ""
 "`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ and join "
 "the open-source Flower community on Slack to connect, ask questions, and "
-"get help: `Join Slack <https://flower.dev/join-slack>`__ 🌼 We'd love to "
+"get help: `Join Slack <https://flower.ai/join-slack>`__ 🌼 We'd love to "
 "hear from you in the ``#introductions`` channel! And if anything is "
 "unclear, head over to the ``#questions`` channel."
 msgstr ""
 "`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ 并加入 Slack "
-"上的开源 Flower 社区，进行交流、提问并获得帮助： 加入 Slack <https://flower.dev/join-slack>`__ "
+"上的开源 Flower 社区，进行交流、提问并获得帮助： 加入 Slack <https://flower.ai/join-slack>`__ "
 "🌼 我们希望在 ``#introductions`` 频道听到您的声音！如果有任何不清楚的地方，请访问 ``#questions`` 频道。"
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:19
@@ -20149,11 +20149,11 @@ msgstr "在本教程的下一部分，我们将用 Flower 建立第一个联邦
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:373
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 1 "
-"<https://flower.dev/docs/framework/tutorial-get-started-with-flower-"
+"<https://flower.ai/docs/framework/tutorial-get-started-with-flower-"
 "pytorch.html>`__ shows how to build a simple federated learning system "
 "with PyTorch and Flower."
 msgstr ""
-"`Flower 联邦学习教程 - 第 1 部分 <https://flower.dev/docs/framework/tutorial-get-"
+"`Flower 联邦学习教程 - 第 1 部分 <https://flower.ai/docs/framework/tutorial-get-"
 "started-with-flower-pytorch.html>`__ 展示了如何使用 PyTorch 和 Flower "
 "构建一个简单的联邦学习系统。"
 
diff --git a/doc/source/_templates/base.html b/doc/source/_templates/base.html
index 0cbe6e9e4456..768c560f4f6a 100644
--- a/doc/source/_templates/base.html
+++ b/doc/source/_templates/base.html
@@ -6,9 +6,9 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark">
     {% if current_language != 'en' %}
-      <link rel="canonical" href="https://flower.dev/docs/framework/main/{{ current_language }}/{{ pagename }}.html">
+      <link rel="canonical" href="https://flower.ai/docs/framework/main/{{ current_language }}/{{ pagename }}.html">
     {% else %}
-      <link rel="canonical" href="https://flower.dev/docs/framework/{{ pagename }}.html">
+      <link rel="canonical" href="https://flower.ai/docs/framework/{{ pagename }}.html">
     {% endif %}
 
     {%- if metatags %}{{ metatags }}{% endif -%}
@@ -105,6 +105,6 @@
     {%- endblock -%}
 
     {%- endblock scripts -%}
-    <script defer data-domain="flower.dev" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
+    <script defer data-domain="flower.ai" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
   </body>
 </html>
diff --git a/doc/source/_templates/sidebar/lang.html b/doc/source/_templates/sidebar/lang.html
index 04b3af62c7ce..b377a53f9c40 100644
--- a/doc/source/_templates/sidebar/lang.html
+++ b/doc/source/_templates/sidebar/lang.html
@@ -2,8 +2,8 @@
   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/flag-icon-css/0.8.2/css/flag-icon.min.css">
 
   <div style="text-align: center; padding-bottom: 1rem;">
-    <a href="https://flower.dev/docs/framework/{{ current_version.url }}/en/{{ pagename }}.html" style="text-decoration:none; padding-right: 2rem; font-size: 150%;">&#127468;&#127463;</a>
-    <a href="https://flower.dev/docs/framework/{{ current_version.url }}/fr/{{ pagename }}.html" style="text-decoration:none; padding-right: 2rem; font-size: 150%;">&#127467;&#127479;</a>
-    <a href="https://flower.dev/docs/framework/{{ current_version.url }}/zh_Hans/{{ pagename }}.html" style="text-decoration:none; font-size: 150%;">&#x1F1E8;&#x1F1F3;</a>
+    <a href="https://flower.ai/docs/framework/{{ current_version.url }}/en/{{ pagename }}.html" style="text-decoration:none; padding-right: 2rem; font-size: 150%;">&#127468;&#127463;</a>
+    <a href="https://flower.ai/docs/framework/{{ current_version.url }}/fr/{{ pagename }}.html" style="text-decoration:none; padding-right: 2rem; font-size: 150%;">&#127467;&#127479;</a>
+    <a href="https://flower.ai/docs/framework/{{ current_version.url }}/zh_Hans/{{ pagename }}.html" style="text-decoration:none; font-size: 150%;">&#x1F1E8;&#x1F1F3;</a>
   </div>
 {% endif %}
diff --git a/doc/source/_templates/sidebar/versioning.html b/doc/source/_templates/sidebar/versioning.html
index dde7528d15e4..74f1cd8febb7 100644
--- a/doc/source/_templates/sidebar/versioning.html
+++ b/doc/source/_templates/sidebar/versioning.html
@@ -59,8 +59,8 @@
 
 <script>
 function checkPageExistence(versionName, current_language, pagename) {
-  var newUrl = `https://flower.dev/docs/framework/${versionName}/${current_language}/${pagename}.html`;
-  var fallbackUrl = `https://flower.dev/docs/framework/${versionName}/${current_language}/index.html#references`;
+  var newUrl = `https://flower.ai/docs/framework/${versionName}/${current_language}/${pagename}.html`;
+  var fallbackUrl = `https://flower.ai/docs/framework/${versionName}/${current_language}/index.html#references`;
 
   fetch(newUrl)
     .then(response => {
@@ -77,8 +77,8 @@
 }
 </script>
 
-<div class="wrap-collabsible"> 
-  <input id="collapsible" class="toggle" type="checkbox"> 
+<div class="wrap-collabsible">
+  <input id="collapsible" class="toggle" type="checkbox">
   <label for="collapsible" class="lbl-toggle">Versions</label>
   <div class="collapsible-content">
     <div class="content-inner">
diff --git a/doc/source/conf.py b/doc/source/conf.py
index 259d8a988841..88cb5c05b1d8 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -123,25 +123,27 @@
 # The full name is still at the top of the page
 add_module_names = False
 
+
 def find_test_modules(package_path):
     """Go through the python files and exclude every *_test.py file."""
     full_path_modules = []
     for root, dirs, files in os.walk(package_path):
         for file in files:
-            if file.endswith('_test.py'):
+            if file.endswith("_test.py"):
                 # Construct the module path relative to the package directory
                 full_path = os.path.join(root, file)
                 relative_path = os.path.relpath(full_path, package_path)
                 # Convert file path to dotted module path
-                module_path = os.path.splitext(relative_path)[0].replace(os.sep, '.')
+                module_path = os.path.splitext(relative_path)[0].replace(os.sep, ".")
                 full_path_modules.append(module_path)
     modules = []
     for full_path_module in full_path_modules:
-        parts = full_path_module.split('.')
+        parts = full_path_module.split(".")
         for i in range(len(parts)):
-            modules.append('.'.join(parts[i:]))
+            modules.append(".".join(parts[i:]))
     return modules
 
+
 # Stop from documenting the *_test.py files.
 # That's the only way to do that in autosummary (make the modules as mock_imports).
 autodoc_mock_imports = find_test_modules(os.path.abspath("../../src/py/flwr"))
@@ -249,7 +251,7 @@ def find_test_modules(package_path):
 html_title = f"Flower Framework"
 html_logo = "_static/flower-logo.png"
 html_favicon = "_static/favicon.ico"
-html_baseurl = "https://flower.dev/docs/framework/"
+html_baseurl = "https://flower.ai/docs/framework/"
 
 html_theme_options = {
     #
diff --git a/doc/source/contributor-explanation-architecture.rst b/doc/source/contributor-explanation-architecture.rst
index 0e2ea1f6e66b..a20a84313118 100644
--- a/doc/source/contributor-explanation-architecture.rst
+++ b/doc/source/contributor-explanation-architecture.rst
@@ -4,7 +4,7 @@ Flower Architecture
 Edge Client Engine
 ------------------
 
-`Flower <https://flower.dev>`_ core framework architecture with Edge Client Engine
+`Flower <https://flower.ai>`_ core framework architecture with Edge Client Engine
 
 .. figure:: _static/flower-architecture-ECE.png
      :width: 80 %
@@ -12,7 +12,7 @@ Edge Client Engine
 Virtual Client Engine
 ---------------------
 
-`Flower <https://flower.dev>`_ core framework architecture with Virtual Client Engine
+`Flower <https://flower.ai>`_ core framework architecture with Virtual Client Engine
 
 .. figure:: _static/flower-architecture-VCE.png
      :width: 80 %
@@ -20,7 +20,7 @@ Virtual Client Engine
 Virtual Client Engine and Edge Client Engine in the same workload
 -----------------------------------------------------------------
 
-`Flower <https://flower.dev>`_ core framework architecture with both Virtual Client Engine and Edge Client Engine
+`Flower <https://flower.ai>`_ core framework architecture with both Virtual Client Engine and Edge Client Engine
 
 .. figure:: _static/flower-architecture.drawio.png
      :width: 80 %
diff --git a/doc/source/contributor-how-to-build-docker-images.rst b/doc/source/contributor-how-to-build-docker-images.rst
index 2c6c7a7ab986..3beae7422bef 100644
--- a/doc/source/contributor-how-to-build-docker-images.rst
+++ b/doc/source/contributor-how-to-build-docker-images.rst
@@ -17,7 +17,7 @@ Before we can start, we need to meet a few prerequisites in our local developmen
 #. Verify the Docker daemon is running.
 
     Please follow the first section on
-    `Run Flower using Docker <https://flower.dev/docs/framework/how-to-run-flower-using-docker>`_
+    `Run Flower using Docker <https://flower.ai/docs/framework/how-to-run-flower-using-docker>`_
     which covers this step in more detail.
 
 Currently, Flower provides two images, a base image and a server image. There will also be a client
diff --git a/doc/source/contributor-how-to-contribute-translations.rst b/doc/source/contributor-how-to-contribute-translations.rst
index d97a2cb8c64f..1614b8e5a040 100644
--- a/doc/source/contributor-how-to-contribute-translations.rst
+++ b/doc/source/contributor-how-to-contribute-translations.rst
@@ -2,7 +2,7 @@ Contribute translations
 =======================
 
 Since `Flower 1.5
-<https://flower.dev/docs/framework/ref-changelog.html#v1-5-0-2023-08-31>`_ we
+<https://flower.ai/docs/framework/ref-changelog.html#v1-5-0-2023-08-31>`_ we
 have introduced translations to our doc pages, but, as you might have noticed,
 the translations are often imperfect. If you speak languages other than
 English, you might be able to help us in our effort to make Federated Learning
@@ -67,5 +67,5 @@ Add new languages
 -----------------
 
 If you want to add a new language, you will first have to contact us, either on
-`Slack <https://flower.dev/join-slack>`_, or by opening an issue on our `GitHub
+`Slack <https://flower.ai/join-slack>`_, or by opening an issue on our `GitHub
 repo <https://github.com/adap/flower>`_.
diff --git a/doc/source/contributor-ref-good-first-contributions.rst b/doc/source/contributor-ref-good-first-contributions.rst
index 523a4679c6ef..cbf21e2845bc 100644
--- a/doc/source/contributor-ref-good-first-contributions.rst
+++ b/doc/source/contributor-ref-good-first-contributions.rst
@@ -14,7 +14,7 @@ Until the Flower core library matures it will be easier to get PR's accepted if
 they only touch non-core areas of the codebase. Good candidates to get started
 are:
 
-- Documentation: What's missing? What could be expressed more clearly? 
+- Documentation: What's missing? What could be expressed more clearly?
 - Baselines: See below.
 - Examples: See below.
 
@@ -22,9 +22,9 @@ are:
 Request for Flower Baselines
 ----------------------------
 
-If you are not familiar with Flower Baselines, you should probably check-out our `contributing guide for baselines <https://flower.dev/docs/contributing-baselines.html>`_.
+If you are not familiar with Flower Baselines, you should probably check-out our `contributing guide for baselines <https://flower.ai/docs/contributing-baselines.html>`_.
 
-You should then check out the open 
+You should then check out the open
 `issues <https://github.com/adap/flower/issues?q=is%3Aopen+is%3Aissue+label%3A%22new+baseline%22>`_ for baseline requests.
 If you find a baseline that you'd like to work on and that has no assignes, feel free to assign it to yourself and start working on it!
 
diff --git a/doc/source/contributor-tutorial-contribute-on-github.rst b/doc/source/contributor-tutorial-contribute-on-github.rst
index d409802897e4..273b47a636cc 100644
--- a/doc/source/contributor-tutorial-contribute-on-github.rst
+++ b/doc/source/contributor-tutorial-contribute-on-github.rst
@@ -3,8 +3,8 @@ Contribute on GitHub
 
 This guide is for people who want to get involved with Flower, but who are not used to contributing to GitHub projects.
 
-If you're familiar with how contributing on GitHub works, you can directly checkout our 
-`getting started guide for contributors <https://flower.dev/docs/contributor-tutorial-get-started-as-a-contributor.html>`_.
+If you're familiar with how contributing on GitHub works, you can directly checkout our
+`getting started guide for contributors <https://flower.ai/docs/contributor-tutorial-get-started-as-a-contributor.html>`_.
 
 
 Setting up the repository
@@ -16,9 +16,9 @@ Setting up the repository
 
     GitHub, itself, is a code hosting platform for version control and collaboration. It allows for everyone to collaborate and work from anywhere on remote repositories.
 
-    If you haven't already, you will need to create an account on `GitHub <https://github.com/signup>`_. 
+    If you haven't already, you will need to create an account on `GitHub <https://github.com/signup>`_.
 
-    The idea behind the generic Git and GitHub workflow boils down to this: 
+    The idea behind the generic Git and GitHub workflow boils down to this:
     you download code from a remote repository on GitHub, make changes locally and keep track of them using Git and then you upload your new history back to GitHub.
 
 2. **Forking the Flower repository**
@@ -26,7 +26,7 @@ Setting up the repository
     and click the ``Fork`` button situated on the top right of the page.
 
     .. image:: _static/fork_button.png
-    
+
     You can change the name if you want, but this is not necessary as this version of Flower will be yours and will sit inside your own account (i.e., in your own list of repositories).
     Once created, you should see on the top left corner that you are looking at your own version of Flower.
 
@@ -34,14 +34,14 @@ Setting up the repository
 
 3. **Cloning your forked repository**
     The next step is to download the forked repository on your machine to be able to make changes to it.
-    On your forked repository page, you should first click on the ``Code`` button on the right, 
+    On your forked repository page, you should first click on the ``Code`` button on the right,
     this will give you the ability to copy the HTTPS link of the repository.
 
     .. image:: _static/cloning_fork.png
 
     Once you copied the \<URL\>, you can open a terminal on your machine, navigate to the place you want to download the repository to and type:
 
-    .. code-block:: shell 
+    .. code-block:: shell
 
         $ git clone <URL>
 
@@ -58,14 +58,14 @@ Setting up the repository
     To obtain it, we can do as previously mentioned by going to our fork repository on our GitHub account and copying the link.
 
     .. image:: _static/cloning_fork.png
-    
+
     Once the \<URL\> is copied, we can type the following command in our terminal:
 
     .. code-block:: shell
 
         $ git remote add origin <URL>
 
-    
+
 5. **Add upstream**
     Now we will add an upstream address to our repository.
     Still in the same directroy, we must run the following command:
@@ -76,10 +76,10 @@ Setting up the repository
 
     The following diagram visually explains what we did in the previous steps:
 
-    .. image:: _static/github_schema.png 
+    .. image:: _static/github_schema.png
 
-    The upstream is the GitHub remote address of the parent repository (in this case Flower), 
-    i.e. the one we eventually want to contribute to and therefore need an up-to-date history of. 
+    The upstream is the GitHub remote address of the parent repository (in this case Flower),
+    i.e. the one we eventually want to contribute to and therefore need an up-to-date history of.
     The origin is just the GitHub remote address of the forked repository we created, i.e. the copy (fork) in our own account.
 
     To make sure our local version of the fork is up-to-date with the latest changes from the Flower repository,
@@ -113,9 +113,9 @@ And with Flower's repository:
     $ git pull upstream main
 
 1. **Create a new branch**
-    To make the history cleaner and easier to work with, it is good practice to 
+    To make the history cleaner and easier to work with, it is good practice to
     create a new branch for each feature/project that needs to be implemented.
-    
+
     To do so, just run the following command inside the repository's directory:
 
     .. code-block:: shell
@@ -137,7 +137,7 @@ And with Flower's repository:
         $ ./dev/test.sh # to test that your code can be accepted
         $ ./baselines/dev/format.sh # same as above but for code added to baselines
         $ ./baselines/dev/test.sh # same as above but for code added to baselines
-    
+
 4. **Stage changes**
     Before creating a commit that will update your history, you must specify to Git which files it needs to take into account.
 
@@ -184,21 +184,21 @@ Creating and merging a pull request (PR)
     Once you click the ``Compare & pull request`` button, you should see something similar to this:
 
     .. image:: _static/creating_pr.png
-    
+
     At the top you have an explanation of which branch will be merged where:
 
     .. image:: _static/merging_branch.png
-    
+
     In this example you can see that the request is to merge the branch ``doc-fixes`` from my forked repository to branch ``main`` from the Flower repository.
 
-    The input box in the middle is there for you to describe what your PR does and to link it to existing issues. 
+    The input box in the middle is there for you to describe what your PR does and to link it to existing issues.
     We have placed comments (that won't be rendered once the PR is opened) to guide you through the process.
 
     It is important to follow the instructions described in comments. For instance, in order to not break how our changelog system works,
     you should read the information above the ``Changelog entry`` section carefully.
     You can also checkout some examples and details in the :ref:`changelogentry` appendix.
 
-    At the bottom you will find the button to open the PR. This will notify reviewers that a new PR has been opened and 
+    At the bottom you will find the button to open the PR. This will notify reviewers that a new PR has been opened and
     that they should look over it to merge or to request changes.
 
     If your PR is not yet ready for review, and you don't want to notify anyone, you have the option to create a draft pull request:
@@ -218,7 +218,7 @@ Creating and merging a pull request (PR)
     Merging will be blocked if there are ongoing requested changes.
 
     .. image:: _static/changes_requested.png
-    
+
     To resolve them, just push the necessary changes to the branch associated with the PR:
 
     .. image:: _static/make_changes.png
@@ -277,12 +277,12 @@ This is a tiny change, but it’ll allow us to test your end-to-end setup. After
 
 - Find the source file in ``doc/source``
 - Make the change in the ``.rst`` file (beware, the dashes under the title should be the same length as the title itself)
-- Build the docs and check the result: `<https://flower.dev/docs/writing-documentation.html#edit-an-existing-page>`_
+- Build the docs and check the result: `<https://flower.ai/docs/writing-documentation.html#edit-an-existing-page>`_
 
 Rename file
 :::::::::::
 
-You might have noticed that the file name still reflects the old wording. 
+You might have noticed that the file name still reflects the old wording.
 If we just change the file, then we break all existing links to it - it is **very important** to avoid that, breaking links can harm our search engine ranking.
 
 Here’s how to change the file name:
@@ -295,7 +295,7 @@ This will cause a redirect from ``saving-progress.html`` to ``save-progress.html
 Apply changes in the index file
 :::::::::::::::::::::::::::::::
 
-For the lateral navigation bar to work properly, it is very important to update the ``index.rst`` file as well. 
+For the lateral navigation bar to work properly, it is very important to update the ``index.rst`` file as well.
 This is where we define the whole arborescence of the navbar.
 
 - Find and modify the file name in ``index.rst``
@@ -343,7 +343,7 @@ Next steps
 
 Once you have made your first PR, and want to contribute more, be sure to check out the following :
 
-- `Good first contributions <https://flower.dev/docs/framework/contributor-ref-good-first-contributions.html>`_, where you should particularly look into the :code:`baselines` contributions.
+- `Good first contributions <https://flower.ai/docs/framework/contributor-ref-good-first-contributions.html>`_, where you should particularly look into the :code:`baselines` contributions.
 
 
 Appendix
@@ -358,10 +358,10 @@ When opening a new PR, inside its description, there should be a ``Changelog ent
 
 Above this header you should see the following comment that explains how to write your changelog entry:
 
-    Inside the following 'Changelog entry' section, 
+    Inside the following 'Changelog entry' section,
     you should put the description of your changes that will be added to the changelog alongside your PR title.
 
-    If the section is completely empty (without any token) or non-existant, 
+    If the section is completely empty (without any token) or non-existant,
     the changelog will just contain the title of the PR for the changelog entry, without any description.
 
     If the section contains some text other than tokens, it will use it to add a description to the change.
diff --git a/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst b/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst
index 5ebaa337dde8..5d4dac0c0cda 100644
--- a/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst
+++ b/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst
@@ -3,11 +3,11 @@ Example: FedBN in PyTorch - From Centralized To Federated
 
 This tutorial will show you how to use Flower to build a federated version of an existing machine learning workload with `FedBN <https://github.com/med-air/FedBN>`_, a federated training strategy designed for non-iid data.
 We are using PyTorch to train a Convolutional Neural Network(with Batch Normalization layers) on the CIFAR-10 dataset.
-When applying FedBN, only few changes needed compared to `Example: PyTorch - From Centralized To Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-federated.html>`_.
+When applying FedBN, only few changes needed compared to `Example: PyTorch - From Centralized To Federated <https://flower.ai/docs/examples/pytorch-from-centralized-to-federated.html>`_.
 
 Centralized Training
 --------------------
-All files are revised based on `Example: PyTorch - From Centralized To Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-federated.html>`_.
+All files are revised based on `Example: PyTorch - From Centralized To Federated <https://flower.ai/docs/examples/pytorch-from-centralized-to-federated.html>`_.
 The only thing to do is modifying the file called :code:`cifar.py`, revised part is shown below:
 
 The model architecture defined in class Net() is added with Batch Normalization layers accordingly.
@@ -50,8 +50,8 @@ Let's take the next step and use what we've built to create a federated learning
 Federated Training
 ------------------
 
-If you have read `Example: PyTorch - From Centralized To Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-federated.html>`_, the following parts are easy to follow, onyl :code:`get_parameters` and :code:`set_parameters` function in :code:`client.py` needed to revise.
-If not, please read the `Example: PyTorch - From Centralized To Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-federated.html>`_. first.
+If you have read `Example: PyTorch - From Centralized To Federated <https://flower.ai/docs/examples/pytorch-from-centralized-to-federated.html>`_, the following parts are easy to follow, onyl :code:`get_parameters` and :code:`set_parameters` function in :code:`client.py` needed to revise.
+If not, please read the `Example: PyTorch - From Centralized To Federated <https://flower.ai/docs/examples/pytorch-from-centralized-to-federated.html>`_. first.
 
 Our example consists of one *server* and two *clients*. In FedBN, :code:`server.py` keeps unchanged, we can start the server directly.
 
@@ -66,7 +66,7 @@ Finally, we will revise our *client* logic by changing :code:`get_parameters` an
     class CifarClient(fl.client.NumPyClient):
         """Flower client implementing CIFAR-10 image classification using
         PyTorch."""
-        
+
         ...
 
         def get_parameters(self, config) -> List[np.ndarray]:
@@ -79,7 +79,7 @@ Finally, we will revise our *client* logic by changing :code:`get_parameters` an
             params_dict = zip(keys, parameters)
             state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
             self.model.load_state_dict(state_dict, strict=False)
-        
+
         ...
 
 Now, you can now open two additional terminal windows and run
diff --git a/doc/source/how-to-configure-clients.rst b/doc/source/how-to-configure-clients.rst
index 26c132125ccf..bfb5a8f63761 100644
--- a/doc/source/how-to-configure-clients.rst
+++ b/doc/source/how-to-configure-clients.rst
@@ -13,7 +13,7 @@ Configuration values are represented as a dictionary with ``str`` keys and value
     config_dict = {
         "dropout": True,        # str key, bool value
         "learning_rate": 0.01,  # str key, float value
-        "batch_size": 32,       # str key, int value 
+        "batch_size": 32,       # str key, int value
         "optimizer": "sgd",     # str key, str value
     }
 
@@ -56,7 +56,7 @@ To make the built-in strategies use this function, we can pass it to ``FedAvg``
 One the client side, we receive the configuration dictionary in ``fit``:
 
 .. code-block:: python
-    
+
     class FlowerClient(flwr.client.NumPyClient):
         def fit(parameters, config):
             print(config["batch_size"])  # Prints `32`
@@ -86,7 +86,7 @@ Configuring individual clients
 
 In some cases, it is necessary to send different configuration values to different clients.
 
-This can be achieved by customizing an existing strategy or by `implementing a custom strategy from scratch <https://flower.dev/docs/framework/how-to-implement-strategies.html>`_. Here's a nonsensical example that customizes :code:`FedAvg` by adding a custom ``"hello": "world"`` configuration key/value pair to the config dict of a *single client* (only the first client in the list, the other clients in this round to not receive this "special" config value):
+This can be achieved by customizing an existing strategy or by `implementing a custom strategy from scratch <https://flower.ai/docs/framework/how-to-implement-strategies.html>`_. Here's a nonsensical example that customizes :code:`FedAvg` by adding a custom ``"hello": "world"`` configuration key/value pair to the config dict of a *single client* (only the first client in the list, the other clients in this round to not receive this "special" config value):
 
 .. code-block:: python
 
diff --git a/doc/source/how-to-install-flower.rst b/doc/source/how-to-install-flower.rst
index ff3dbb605846..dc88076424f8 100644
--- a/doc/source/how-to-install-flower.rst
+++ b/doc/source/how-to-install-flower.rst
@@ -57,7 +57,7 @@ Advanced installation options
 Install via Docker
 ~~~~~~~~~~~~~~~~~~
 
-`How to run Flower using Docker <https://flower.dev/docs/framework/how-to-run-flower-using-docker.html>`_
+`How to run Flower using Docker <https://flower.ai/docs/framework/how-to-run-flower-using-docker.html>`_
 
 Install pre-release
 ~~~~~~~~~~~~~~~~~~~
diff --git a/doc/source/how-to-run-flower-using-docker.rst b/doc/source/how-to-run-flower-using-docker.rst
index 40df1ffcb63c..ed034c820142 100644
--- a/doc/source/how-to-run-flower-using-docker.rst
+++ b/doc/source/how-to-run-flower-using-docker.rst
@@ -54,7 +54,7 @@ to the Flower server. Here, we are passing the flag ``--insecure``.
 
   The ``--insecure`` flag enables insecure communication (using HTTP, not HTTPS) and should only be used
   for testing purposes. We strongly recommend enabling
-  `SSL <https://flower.dev/docs/framework/how-to-run-flower-using-docker.html#enabling-ssl-for-secure-connections>`_
+  `SSL <https://flower.ai/docs/framework/how-to-run-flower-using-docker.html#enabling-ssl-for-secure-connections>`_
   when deploying to a production environment.
 
 You can use ``--help`` to view all available flags that the server supports:
@@ -90,7 +90,7 @@ To enable SSL, you will need a CA certificate, a server certificate and a server
 
 .. note::
   For testing purposes, you can generate your own self-signed certificates. The
-  `Enable SSL connections <https://flower.dev/docs/framework/how-to-enable-ssl-connections.html#certificates>`_
+  `Enable SSL connections <https://flower.ai/docs/framework/how-to-enable-ssl-connections.html#certificates>`_
   page contains a section that will guide you through the process.
 
 Assuming all files we need are in the local ``certificates`` directory, we can use the flag
diff --git a/doc/source/how-to-upgrade-to-flower-1.0.rst b/doc/source/how-to-upgrade-to-flower-1.0.rst
index fd380e95d69c..c4429d61d0a9 100644
--- a/doc/source/how-to-upgrade-to-flower-1.0.rst
+++ b/doc/source/how-to-upgrade-to-flower-1.0.rst
@@ -50,7 +50,7 @@ Strategies / ``start_server`` / ``start_simulation``
 - Replace ``num_rounds=1`` in ``start_simulation`` with the new ``config=ServerConfig(...)`` (see previous item)
 - Remove ``force_final_distributed_eval`` parameter from calls to ``start_server``. Distributed evaluation on all clients can be enabled by configuring the strategy to sample all clients for evaluation after the last round of training.
 - Rename parameter/ndarray conversion functions:
-  
+
   - ``parameters_to_weights`` --> ``parameters_to_ndarrays``
   - ``weights_to_parameters`` --> ``ndarrays_to_parameters``
 
@@ -88,4 +88,4 @@ Along with the necessary changes above, there are a number of potential improvem
 Further help
 ------------
 
-Most official `Flower code examples <https://github.com/adap/flower/tree/main/examples>`_ are already updated to Flower 1.0, they can serve as a reference for using the Flower 1.0 API. If there are further questionsm, `join the Flower Slack <https://flower.dev/join-slack/>`_ and use the channgel ``#questions``.
+Most official `Flower code examples <https://github.com/adap/flower/tree/main/examples>`_ are already updated to Flower 1.0, they can serve as a reference for using the Flower 1.0 API. If there are further questionsm, `join the Flower Slack <https://flower.ai/join-slack/>`_ and use the channgel ``#questions``.
diff --git a/doc/source/index.rst b/doc/source/index.rst
index 7e2b4052bee6..ea52a9421b61 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -4,7 +4,7 @@ Flower Framework Documentation
 .. meta::
    :description: Check out the documentation of the main Flower Framework enabling easy Python development for Federated Learning.
 
-Welcome to Flower's documentation. `Flower <https://flower.dev>`_ is a friendly federated learning framework.
+Welcome to Flower's documentation. `Flower <https://flower.ai>`_ is a friendly federated learning framework.
 
 
 Join the Flower Community
@@ -12,7 +12,7 @@ Join the Flower Community
 
 The Flower Community is growing quickly - we're a friendly group of researchers, engineers, students, professionals, academics, and other enthusiasts.
 
-.. button-link:: https://flower.dev/join-slack
+.. button-link:: https://flower.ai/join-slack
     :color: primary
     :shadow:
 
diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index e9282632410d..41dc91873c6c 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -42,7 +42,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Introduce Docker image for Flower server** ([#2700](https://github.com/adap/flower/pull/2700), [#2688](https://github.com/adap/flower/pull/2688), [#2705](https://github.com/adap/flower/pull/2705), [#2695](https://github.com/adap/flower/pull/2695), [#2747](https://github.com/adap/flower/pull/2747), [#2746](https://github.com/adap/flower/pull/2746), [#2680](https://github.com/adap/flower/pull/2680), [#2682](https://github.com/adap/flower/pull/2682), [#2701](https://github.com/adap/flower/pull/2701))
 
-  The Flower server can now be run using an official Docker image. A new how-to guide explains [how to run Flower using Docker](https://flower.dev/docs/framework/how-to-run-flower-using-docker.html). An official Flower client Docker image will follow.
+  The Flower server can now be run using an official Docker image. A new how-to guide explains [how to run Flower using Docker](https://flower.ai/docs/framework/how-to-run-flower-using-docker.html). An official Flower client Docker image will follow.
 
 - **Introduce** `flower-via-docker-compose` **example** ([#2626](https://github.com/adap/flower/pull/2626))
 
@@ -52,7 +52,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Update code examples to use Flower Datasets** ([#2450](https://github.com/adap/flower/pull/2450), [#2456](https://github.com/adap/flower/pull/2456), [#2318](https://github.com/adap/flower/pull/2318), [#2712](https://github.com/adap/flower/pull/2712))
 
-  Several code examples were updated to use [Flower Datasets](https://flower.dev/docs/datasets/).
+  Several code examples were updated to use [Flower Datasets](https://flower.ai/docs/datasets/).
 
 - **General updates to Flower Examples** ([#2381](https://github.com/adap/flower/pull/2381), [#2805](https://github.com/adap/flower/pull/2805), [#2782](https://github.com/adap/flower/pull/2782), [#2806](https://github.com/adap/flower/pull/2806), [#2829](https://github.com/adap/flower/pull/2829), [#2825](https://github.com/adap/flower/pull/2825), [#2816](https://github.com/adap/flower/pull/2816), [#2726](https://github.com/adap/flower/pull/2726), [#2659](https://github.com/adap/flower/pull/2659), [#2655](https://github.com/adap/flower/pull/2655))
 
@@ -213,11 +213,11 @@ We would like to give our special thanks to all the contributors who made the ne
 
   The new simulation engine has been rewritten from the ground up, yet it remains fully backwards compatible. It offers much improved stability and memory handling, especially when working with GPUs. Simulations transparently adapt to different settings to scale simulation in CPU-only, CPU+GPU, multi-GPU, or multi-node multi-GPU environments.
 
-  Comprehensive documentation includes a new [how-to run simulations](https://flower.dev/docs/framework/how-to-run-simulations.html) guide, new [simulation-pytorch](https://flower.dev/docs/examples/simulation-pytorch.html) and [simulation-tensorflow](https://flower.dev/docs/examples/simulation-tensorflow.html) notebooks, and a new [YouTube tutorial series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB).
+  Comprehensive documentation includes a new [how-to run simulations](https://flower.ai/docs/framework/how-to-run-simulations.html) guide, new [simulation-pytorch](https://flower.ai/docs/examples/simulation-pytorch.html) and [simulation-tensorflow](https://flower.ai/docs/examples/simulation-tensorflow.html) notebooks, and a new [YouTube tutorial series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB).
 
 - **Restructure Flower Docs** ([#1824](https://github.com/adap/flower/pull/1824), [#1865](https://github.com/adap/flower/pull/1865), [#1884](https://github.com/adap/flower/pull/1884), [#1887](https://github.com/adap/flower/pull/1887), [#1919](https://github.com/adap/flower/pull/1919), [#1922](https://github.com/adap/flower/pull/1922), [#1920](https://github.com/adap/flower/pull/1920), [#1923](https://github.com/adap/flower/pull/1923), [#1924](https://github.com/adap/flower/pull/1924), [#1962](https://github.com/adap/flower/pull/1962), [#2006](https://github.com/adap/flower/pull/2006), [#2133](https://github.com/adap/flower/pull/2133), [#2203](https://github.com/adap/flower/pull/2203), [#2215](https://github.com/adap/flower/pull/2215), [#2122](https://github.com/adap/flower/pull/2122), [#2223](https://github.com/adap/flower/pull/2223), [#2219](https://github.com/adap/flower/pull/2219), [#2232](https://github.com/adap/flower/pull/2232), [#2233](https://github.com/adap/flower/pull/2233), [#2234](https://github.com/adap/flower/pull/2234), [#2235](https://github.com/adap/flower/pull/2235), [#2237](https://github.com/adap/flower/pull/2237), [#2238](https://github.com/adap/flower/pull/2238), [#2242](https://github.com/adap/flower/pull/2242), [#2231](https://github.com/adap/flower/pull/2231), [#2243](https://github.com/adap/flower/pull/2243), [#2227](https://github.com/adap/flower/pull/2227))
 
-  Much effort went into a completely restructured Flower docs experience. The documentation on [flower.dev/docs](https://flower.dev/docs) is now divided into Flower Framework, Flower Baselines, Flower Android SDK, Flower iOS SDK, and code example projects.
+  Much effort went into a completely restructured Flower docs experience. The documentation on [flower.ai/docs](https://flower.ai/docs) is now divided into Flower Framework, Flower Baselines, Flower Android SDK, Flower iOS SDK, and code example projects.
 
 - **Introduce Flower Swift SDK** ([#1858](https://github.com/adap/flower/pull/1858), [#1897](https://github.com/adap/flower/pull/1897))
 
@@ -303,7 +303,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Introduce new "What is Federated Learning?" tutorial** ([#1657](https://github.com/adap/flower/pull/1657), [#1721](https://github.com/adap/flower/pull/1721))
 
-  A new [entry-level tutorial](https://flower.dev/docs/framework/tutorial-what-is-federated-learning.html) in our documentation explains the basics of Fedetated Learning. It enables anyone who's unfamiliar with Federated Learning to start their journey with Flower. Forward it to anyone who's interested in Federated Learning!
+  A new [entry-level tutorial](https://flower.ai/docs/framework/tutorial-what-is-federated-learning.html) in our documentation explains the basics of Fedetated Learning. It enables anyone who's unfamiliar with Federated Learning to start their journey with Flower. Forward it to anyone who's interested in Federated Learning!
 
 - **Introduce new Flower Baseline: FedProx MNIST** ([#1513](https://github.com/adap/flower/pull/1513), [#1680](https://github.com/adap/flower/pull/1680), [#1681](https://github.com/adap/flower/pull/1681), [#1679](https://github.com/adap/flower/pull/1679))
 
@@ -417,7 +417,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Introduce new Flower Baseline: FedAvg MNIST** ([#1497](https://github.com/adap/flower/pull/1497), [#1552](https://github.com/adap/flower/pull/1552))
 
-  Over the coming weeks, we will be releasing a number of new reference implementations useful especially to FL newcomers. They will typically revisit well known papers from the literature, and be suitable for integration in your own application or for experimentation, in order to deepen your knowledge of FL in general. Today's release is the first in this series. [Read more.](https://flower.dev/blog/2023-01-12-fl-starter-pack-fedavg-mnist-cnn/)
+  Over the coming weeks, we will be releasing a number of new reference implementations useful especially to FL newcomers. They will typically revisit well known papers from the literature, and be suitable for integration in your own application or for experimentation, in order to deepen your knowledge of FL in general. Today's release is the first in this series. [Read more.](https://flower.ai/blog/2023-01-12-fl-starter-pack-fedavg-mnist-cnn/)
 
 - **Improve GPU support in simulations** ([#1555](https://github.com/adap/flower/pull/1555))
 
@@ -427,16 +427,16 @@ We would like to give our special thanks to all the contributors who made the ne
 
   Some users reported that Jupyter Notebooks have not always been easy to use on GPU instances. We listened and made improvements to all of our Jupyter notebooks! Check out the updated notebooks here:
 
-  - [An Introduction to Federated Learning](https://flower.dev/docs/framework/tutorial-get-started-with-flower-pytorch.html)
-  - [Strategies in Federated Learning](https://flower.dev/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html)
-  - [Building a Strategy](https://flower.dev/docs/framework/tutorial-build-a-strategy-from-scratch-pytorch.html)
-  - [Client and NumPyClient](https://flower.dev/docs/framework/tutorial-customize-the-client-pytorch.html)
+  - [An Introduction to Federated Learning](https://flower.ai/docs/framework/tutorial-get-started-with-flower-pytorch.html)
+  - [Strategies in Federated Learning](https://flower.ai/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html)
+  - [Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a-strategy-from-scratch-pytorch.html)
+  - [Client and NumPyClient](https://flower.ai/docs/framework/tutorial-customize-the-client-pytorch.html)
 
 - **Introduce optional telemetry** ([#1533](https://github.com/adap/flower/pull/1533), [#1544](https://github.com/adap/flower/pull/1544), [#1584](https://github.com/adap/flower/pull/1584))
 
   After a [request for feedback](https://github.com/adap/flower/issues/1534) from the community, the Flower open-source project introduces optional collection of *anonymous* usage metrics to make well-informed decisions to improve Flower. Doing this enables the Flower team to understand how Flower is used and what challenges users might face.
 
-  **Flower is a friendly framework for collaborative AI and data science.** Staying true to this statement, Flower makes it easy to disable telemetry for users who do not want to share anonymous usage metrics. [Read more.](https://flower.dev/docs/telemetry.html).
+  **Flower is a friendly framework for collaborative AI and data science.** Staying true to this statement, Flower makes it easy to disable telemetry for users who do not want to share anonymous usage metrics. [Read more.](https://flower.ai/docs/telemetry.html).
 
 - **Introduce (experimental) Driver API** ([#1520](https://github.com/adap/flower/pull/1520), [#1525](https://github.com/adap/flower/pull/1525), [#1545](https://github.com/adap/flower/pull/1545), [#1546](https://github.com/adap/flower/pull/1546), [#1550](https://github.com/adap/flower/pull/1550), [#1551](https://github.com/adap/flower/pull/1551), [#1567](https://github.com/adap/flower/pull/1567))
 
@@ -468,7 +468,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
   As usual, the documentation has improved quite a bit. It is another step in our effort to make the Flower documentation the best documentation of any project. Stay tuned and as always, feel free to provide feedback!
 
-  One highlight is the new [first time contributor guide](https://flower.dev/docs/first-time-contributors.html): if you've never contributed on GitHub before, this is the perfect place to start!
+  One highlight is the new [first time contributor guide](https://flower.ai/docs/first-time-contributors.html): if you've never contributed on GitHub before, this is the perfect place to start!
 
 ### Incompatible changes
 
@@ -657,7 +657,7 @@ We would like to give our **special thanks** to all the contributors who made Fl
 
 - **Flower Baselines (preview): FedOpt, FedBN, FedAvgM** ([#919](https://github.com/adap/flower/pull/919), [#1127](https://github.com/adap/flower/pull/1127), [#914](https://github.com/adap/flower/pull/914))
 
-  The first preview release of Flower Baselines has arrived! We're kickstarting Flower Baselines with implementations of FedOpt (FedYogi, FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how to use [Flower Baselines](https://flower.dev/docs/using-baselines.html). With this first preview release we're also inviting the community to [contribute their own baselines](https://flower.dev/docs/contributing-baselines.html).
+  The first preview release of Flower Baselines has arrived! We're kickstarting Flower Baselines with implementations of FedOpt (FedYogi, FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how to use [Flower Baselines](https://flower.ai/docs/using-baselines.html). With this first preview release we're also inviting the community to [contribute their own baselines](https://flower.ai/docs/contributing-baselines.html).
 
 - **C++ client SDK (preview) and code example** ([#1111](https://github.com/adap/flower/pull/1111))
 
@@ -703,7 +703,7 @@ We would like to give our **special thanks** to all the contributors who made Fl
 
   - New option to keep Ray running if Ray was already initialized in `start_simulation` ([#1177](https://github.com/adap/flower/pull/1177))
   - Add support for custom `ClientManager` as a `start_simulation` parameter ([#1171](https://github.com/adap/flower/pull/1171))
-  - New documentation for [implementing strategies](https://flower.dev/docs/framework/how-to-implement-strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), [#1175](https://github.com/adap/flower/pull/1175))
+  - New documentation for [implementing strategies](https://flower.ai/docs/framework/how-to-implement-strategies.html) ([#1097](https://github.com/adap/flower/pull/1097), [#1175](https://github.com/adap/flower/pull/1175))
   - New mobile-friendly documentation theme ([#1174](https://github.com/adap/flower/pull/1174))
   - Limit version range for (optional) `ray` dependency to include only compatible releases (`>=1.9.2,<1.12.0`) ([#1205](https://github.com/adap/flower/pull/1205))
 
diff --git a/doc/source/ref-example-projects.rst b/doc/source/ref-example-projects.rst
index b47bd8e48997..8eb723000cac 100644
--- a/doc/source/ref-example-projects.rst
+++ b/doc/source/ref-example-projects.rst
@@ -23,8 +23,8 @@ The TensorFlow/Keras quickstart example shows CIFAR-10 image classification
 with MobileNetV2:
 
 - `Quickstart TensorFlow (Code) <https://github.com/adap/flower/tree/main/examples/quickstart-tensorflow>`_
-- `Quickstart TensorFlow (Tutorial) <https://flower.dev/docs/framework/tutorial-quickstart-tensorflow.html>`_
-- `Quickstart TensorFlow (Blog Post) <https://flower.dev/blog/2020-12-11-federated-learning-in-less-than-20-lines-of-code>`_
+- `Quickstart TensorFlow (Tutorial) <https://flower.ai/docs/framework/tutorial-quickstart-tensorflow.html>`_
+- `Quickstart TensorFlow (Blog Post) <https://flower.ai/blog/2020-12-11-federated-learning-in-less-than-20-lines-of-code>`_
 
 
 Quickstart PyTorch
@@ -34,7 +34,7 @@ The PyTorch quickstart example shows CIFAR-10 image classification
 with a simple Convolutional Neural Network:
 
 - `Quickstart PyTorch (Code) <https://github.com/adap/flower/tree/main/examples/quickstart-pytorch>`_
-- `Quickstart PyTorch (Tutorial) <https://flower.dev/docs/framework/tutorial-quickstart-pytorch.html>`_
+- `Quickstart PyTorch (Tutorial) <https://flower.ai/docs/framework/tutorial-quickstart-pytorch.html>`_
 
 
 PyTorch: From Centralized To Federated
@@ -43,7 +43,7 @@ PyTorch: From Centralized To Federated
 This example shows how a regular PyTorch project can be federated using Flower:
 
 - `PyTorch: From Centralized To Federated (Code) <https://github.com/adap/flower/tree/main/examples/pytorch-from-centralized-to-federated>`_
-- `PyTorch: From Centralized To Federated (Tutorial) <https://flower.dev/docs/framework/example-pytorch-from-centralized-to-federated.html>`_
+- `PyTorch: From Centralized To Federated (Tutorial) <https://flower.ai/docs/framework/example-pytorch-from-centralized-to-federated.html>`_
 
 
 Federated Learning on Raspberry Pi and Nvidia Jetson
@@ -52,7 +52,7 @@ Federated Learning on Raspberry Pi and Nvidia Jetson
 This example shows how Flower can be used to build a federated learning system that run across Raspberry Pi and Nvidia Jetson:
 
 - `Federated Learning on Raspberry Pi and Nvidia Jetson (Code) <https://github.com/adap/flower/tree/main/examples/embedded-devices>`_
-- `Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) <https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_
+- `Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) <https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_
 
 
 
diff --git a/doc/source/ref-faq.rst b/doc/source/ref-faq.rst
index 13c44bc64b0e..932396e3c583 100644
--- a/doc/source/ref-faq.rst
+++ b/doc/source/ref-faq.rst
@@ -6,20 +6,20 @@ This page collects answers to commonly asked questions about Federated Learning
 .. dropdown:: :fa:`eye,mr-1` Can Flower run on Juptyter Notebooks / Google Colab?
 
     Yes, it can! Flower even comes with a few under-the-hood optimizations to make it work even better on Colab. Here's a quickstart example:
-    
+
     * `Flower simulation PyTorch <https://colab.research.google.com/github/adap/flower/blob/main/examples/simulation-pytorch/sim.ipynb>`_
     * `Flower simulation TensorFlow/Keras <https://colab.research.google.com/github/adap/flower/blob/main/examples/simulation-tensorflow/sim.ipynb>`_
 
 .. dropdown:: :fa:`eye,mr-1` How can I run Federated Learning on a Raspberry Pi?
 
-    Find the `blog post about federated learning on embedded device here <https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_ and the corresponding `GitHub code example <https://github.com/adap/flower/tree/main/examples/embedded-devices>`_. 
+    Find the `blog post about federated learning on embedded device here <https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_ and the corresponding `GitHub code example <https://github.com/adap/flower/tree/main/examples/embedded-devices>`_.
 
 .. dropdown:: :fa:`eye,mr-1` Does Flower support federated learning on Android devices?
 
-    Yes, it does. Please take a look at our `blog post <https://flower.dev/blog/2021-12-15-federated-learning-on-android-devices-with-flower>`_ or check out the code examples:
+    Yes, it does. Please take a look at our `blog post <https://flower.ai/blog/2021-12-15-federated-learning-on-android-devices-with-flower>`_ or check out the code examples:
 
-    * `Android Kotlin example <https://flower.dev/docs/examples/android-kotlin.html>`_
-    * `Android Java example <https://flower.dev/docs/examples/android.html>`_
+    * `Android Kotlin example <https://flower.ai/docs/examples/android-kotlin.html>`_
+    * `Android Java example <https://flower.ai/docs/examples/android.html>`_
 
 .. dropdown:: :fa:`eye,mr-1` Can I combine federated learning with blockchain?
 
diff --git a/doc/source/ref-telemetry.md b/doc/source/ref-telemetry.md
index 206e641d8b41..49efef5c8559 100644
--- a/doc/source/ref-telemetry.md
+++ b/doc/source/ref-telemetry.md
@@ -41,7 +41,7 @@ Flower telemetry collects the following metrics:
 
 **Source.** Flower telemetry tries to store a random source ID in `~/.flwr/source` the first time a telemetry event is generated. The source ID is important to identify whether an issue is recurring or whether an issue is triggered by multiple clusters running concurrently (which often happens in simulation). For example, if a device runs multiple workloads at the same time, and this results in an issue, then, in order to reproduce the issue, multiple workloads must be started at the same time.
 
-You may delete the source ID at any time. If you wish for all events logged under a specific source ID to be deleted, you can send a deletion request mentioning the source ID to `telemetry@flower.dev`. All events related to that source ID will then be permanently deleted.
+You may delete the source ID at any time. If you wish for all events logged under a specific source ID to be deleted, you can send a deletion request mentioning the source ID to `telemetry@flower.ai`. All events related to that source ID will then be permanently deleted.
 
 We will not collect any personally identifiable information. If you think any of the metrics collected could be misused in any way, please [get in touch with us](#how-to-contact-us). We will update this page to reflect any changes to the metrics collected and publish changes in the changelog.
 
@@ -63,4 +63,4 @@ FLWR_TELEMETRY_ENABLED=0 FLWR_TELEMETRY_LOGGING=1 python server.py # or client.p
 
 ## How to contact us
 
-We want to hear from you. If you have any feedback or ideas on how to improve the way we handle anonymous usage metrics, reach out to us via [Slack](https://flower.dev/join-slack/) (channel `#telemetry`) or email (`telemetry@flower.dev`).
+We want to hear from you. If you have any feedback or ideas on how to improve the way we handle anonymous usage metrics, reach out to us via [Slack](https://flower.ai/join-slack/) (channel `#telemetry`) or email (`telemetry@flower.ai`).
diff --git a/doc/source/tutorial-quickstart-ios.rst b/doc/source/tutorial-quickstart-ios.rst
index 7c8007baaa75..aa94a72580c1 100644
--- a/doc/source/tutorial-quickstart-ios.rst
+++ b/doc/source/tutorial-quickstart-ios.rst
@@ -7,14 +7,14 @@ Quickstart iOS
 .. meta::
    :description: Read this Federated Learning quickstart tutorial for creating an iOS app using Flower to train a neural network on MNIST.
 
-In this tutorial we will learn how to train a Neural Network on MNIST using Flower and CoreML on iOS devices. 
+In this tutorial we will learn how to train a Neural Network on MNIST using Flower and CoreML on iOS devices.
 
-First of all, for running the Flower Python server, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_.
+First of all, for running the Flower Python server, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.ai/docs/recommended-env-setup.html>`_.
 For the Flower client implementation in iOS, it is recommended to use Xcode as our IDE.
 
-Our example consists of one Python *server* and two iPhone *clients* that all have the same model. 
+Our example consists of one Python *server* and two iPhone *clients* that all have the same model.
 
-*Clients* are responsible for generating individual weight updates for the model based on their local datasets. 
+*Clients* are responsible for generating individual weight updates for the model based on their local datasets.
 These updates are then sent to the *server* which will aggregate them to produce a better model. Finally, the *server* sends this improved version of the model back to each *client*.
 A complete cycle of weight updates is called a *round*.
 
@@ -44,10 +44,10 @@ For simplicity reasons we will use the complete Flower client with CoreML, that
   public func getParameters() -> GetParametersRes {
     let parameters = parameters.weightsToParameters()
     let status = Status(code: .ok, message: String())
-        
+
     return GetParametersRes(parameters: parameters, status: status)
   }
-    
+
   /// Calls the routine to fit the local model
   ///
   /// - Returns: The result from the local training, e.g., updated parameters
@@ -55,17 +55,17 @@ For simplicity reasons we will use the complete Flower client with CoreML, that
     let status = Status(code: .ok, message: String())
     let result = runMLTask(configuration: parameters.parametersToWeights(parameters: ins.parameters), task: .train)
     let parameters = parameters.weightsToParameters()
-        
+
     return FitRes(parameters: parameters, numExamples: result.numSamples, status: status)
     }
-    
+
   /// Calls the routine to evaluate the local model
   ///
   /// - Returns: The result from the evaluation, e.g., loss
   public func evaluate(ins: EvaluateIns) -> EvaluateRes {
     let status = Status(code: .ok, message: String())
     let result = runMLTask(configuration: parameters.parametersToWeights(parameters: ins.parameters), task: .test)
-        
+
     return EvaluateRes(loss: Float(result.loss), numExamples: result.numSamples, status: status)
   }
 
@@ -88,12 +88,12 @@ For the MNIST dataset, we need to preprocess it into :code:`MLBatchProvider` obj
 
   // prepare train dataset
   let trainBatchProvider = DataLoader.trainBatchProvider() { _ in }
-            
+
   // prepare test dataset
   let testBatchProvider = DataLoader.testBatchProvider() { _ in }
-            
+
   // load them together
-  let dataLoader = MLDataLoader(trainBatchProvider: trainBatchProvider, 
+  let dataLoader = MLDataLoader(trainBatchProvider: trainBatchProvider,
                                 testBatchProvider: testBatchProvider)
 
 Since CoreML does not allow the model parameters to be seen before training, and accessing the model parameters during or after the training can only be done by specifying the layer name,
@@ -122,7 +122,7 @@ Then start the Flower gRPC client and start communicating to the server by passi
   self.flwrGRPC.startFlwrGRPC(client: self.mlFlwrClient)
 
 That's it for the client. We only have to implement :code:`Client` or call the provided
-:code:`MLFlwrClient` and call :code:`startFlwrGRPC()`. The attribute :code:`hostname` and :code:`port` tells the client which server to connect to. 
+:code:`MLFlwrClient` and call :code:`startFlwrGRPC()`. The attribute :code:`hostname` and :code:`port` tells the client which server to connect to.
 This can be done by entering the hostname and port in the application before clicking the start button to start the federated learning process.
 
 Flower Server
diff --git a/doc/source/tutorial-quickstart-mxnet.rst b/doc/source/tutorial-quickstart-mxnet.rst
index ff8d4b2087dd..08304483af86 100644
--- a/doc/source/tutorial-quickstart-mxnet.rst
+++ b/doc/source/tutorial-quickstart-mxnet.rst
@@ -9,13 +9,13 @@ Quickstart MXNet
 .. meta::
    :description: Check out this Federated Learning quickstart tutorial for using Flower with MXNet to train a Sequential model on MNIST.
 
-In this tutorial, we will learn how to train a :code:`Sequential` model on MNIST using Flower and MXNet. 
+In this tutorial, we will learn how to train a :code:`Sequential` model on MNIST using Flower and MXNet.
 
-It is recommended to create a virtual environment and run everything within this `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_. 
+It is recommended to create a virtual environment and run everything within this `virtualenv <https://flower.ai/docs/recommended-env-setup.html>`_.
 
-Our example consists of one *server* and two *clients* all having the same model. 
+Our example consists of one *server* and two *clients* all having the same model.
 
-*Clients* are responsible for generating individual model parameter updates for the model based on their local datasets. 
+*Clients* are responsible for generating individual model parameter updates for the model based on their local datasets.
 These updates are then sent to the *server* which will aggregate them to produce an updated global model. Finally, the *server* sends this improved version of the model back to each *client*.
 A complete cycle of parameters updates is called a *round*.
 
@@ -35,12 +35,12 @@ Since we want to use MXNet, let's go ahead and install it:
 Flower Client
 -------------
 
-Now that we have all our dependencies installed, let's run a simple distributed training with two clients and one server. Our training procedure and network architecture are based on MXNet´s `Hand-written Digit Recognition tutorial <https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_. 
+Now that we have all our dependencies installed, let's run a simple distributed training with two clients and one server. Our training procedure and network architecture are based on MXNet´s `Hand-written Digit Recognition tutorial <https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_.
 
 In a file called :code:`client.py`, import Flower and MXNet related packages:
 
 .. code-block:: python
-      
+
     import flwr as fl
 
     import numpy as np
@@ -58,7 +58,7 @@ In addition, define the device allocation in MXNet with:
 
     DEVICE = [mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()]
 
-We use MXNet to load MNIST, a popular image classification dataset of handwritten digits for machine learning. The MXNet utility :code:`mx.test_utils.get_mnist()` downloads the training and test data. 
+We use MXNet to load MNIST, a popular image classification dataset of handwritten digits for machine learning. The MXNet utility :code:`mx.test_utils.get_mnist()` downloads the training and test data.
 
 .. code-block:: python
 
@@ -72,7 +72,7 @@ We use MXNet to load MNIST, a popular image classification dataset of handwritte
         val_data = mx.io.NDArrayIter(mnist["test_data"], mnist["test_label"], batch_size)
         return train_data, val_data
 
-Define the training and loss with MXNet. We train the model by looping over the dataset, measure the corresponding loss, and optimize it. 
+Define the training and loss with MXNet. We train the model by looping over the dataset, measure the corresponding loss, and optimize it.
 
 .. code-block:: python
 
@@ -110,7 +110,7 @@ Define the training and loss with MXNet. We train the model by looping over the
         return trainings_metric, num_examples
 
 
-Next, we define the validation of our machine learning model. We loop over the test set and measure both loss and accuracy on the test set. 
+Next, we define the validation of our machine learning model. We loop over the test set and measure both loss and accuracy on the test set.
 
 .. code-block:: python
 
@@ -155,7 +155,7 @@ Our Flower clients will use a simple :code:`Sequential` model:
         init = nd.random.uniform(shape=(2, 784))
         model(init)
 
-After loading the dataset with :code:`load_data()` we perform one forward propagation to initialize the model and model parameters with :code:`model(init)`. Next, we implement a Flower client. 
+After loading the dataset with :code:`load_data()` we perform one forward propagation to initialize the model and model parameters with :code:`model(init)`. Next, we implement a Flower client.
 
 The Flower server interacts with clients through an interface called
 :code:`Client`. When the server selects a particular client for training, it
@@ -207,7 +207,7 @@ They can be implemented in the following way:
             [accuracy, loss], num_examples = test(model, val_data)
             print("Evaluation accuracy & loss", accuracy, loss)
             return float(loss[1]), val_data.batch_size, {"accuracy": float(accuracy[1])}
-    
+
 
 We can now create an instance of our class :code:`MNISTClient` and add one line
 to actually run this client:
diff --git a/doc/source/tutorial-quickstart-pytorch.rst b/doc/source/tutorial-quickstart-pytorch.rst
index f15a4a93114e..32f9c5ebb3a1 100644
--- a/doc/source/tutorial-quickstart-pytorch.rst
+++ b/doc/source/tutorial-quickstart-pytorch.rst
@@ -10,13 +10,13 @@ Quickstart PyTorch
 ..  youtube:: jOmmuzMIQ4c
    :width: 100%
 
-In this tutorial we will learn how to train a Convolutional Neural Network on CIFAR10 using Flower and PyTorch. 
+In this tutorial we will learn how to train a Convolutional Neural Network on CIFAR10 using Flower and PyTorch.
 
-First of all, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_. 
+First of all, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.ai/docs/recommended-env-setup.html>`_.
 
-Our example consists of one *server* and two *clients* all having the same model. 
+Our example consists of one *server* and two *clients* all having the same model.
 
-*Clients* are responsible for generating individual weight-updates for the model based on their local datasets. 
+*Clients* are responsible for generating individual weight-updates for the model based on their local datasets.
 These updates are then sent to the *server* which will aggregate them to produce a better model. Finally, the *server* sends this improved version of the model back to each *client*.
 A complete cycle of weight updates is called a *round*.
 
@@ -26,7 +26,7 @@ Now that we have a rough idea of what is going on, let's get started. We first n
 
   $ pip install flwr
 
-Since we want to use PyTorch to solve a computer vision task, let's go ahead and install PyTorch and the **torchvision** library: 
+Since we want to use PyTorch to solve a computer vision task, let's go ahead and install PyTorch and the **torchvision** library:
 
 .. code-block:: shell
 
@@ -36,12 +36,12 @@ Since we want to use PyTorch to solve a computer vision task, let's go ahead and
 Flower Client
 -------------
 
-Now that we have all our dependencies installed, let's run a simple distributed training with two clients and one server. Our training procedure and network architecture are based on PyTorch's `Deep Learning with PyTorch <https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_. 
+Now that we have all our dependencies installed, let's run a simple distributed training with two clients and one server. Our training procedure and network architecture are based on PyTorch's `Deep Learning with PyTorch <https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_.
 
 In a file called :code:`client.py`, import Flower and PyTorch related packages:
 
 .. code-block:: python
-      
+
     from collections import OrderedDict
 
     import torch
@@ -59,7 +59,7 @@ In addition, we define the device allocation in PyTorch with:
 
     DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-We use PyTorch to load CIFAR10, a popular colored image classification dataset for machine learning. The PyTorch :code:`DataLoader()` downloads the training and test data that are then normalized. 
+We use PyTorch to load CIFAR10, a popular colored image classification dataset for machine learning. The PyTorch :code:`DataLoader()` downloads the training and test data that are then normalized.
 
 .. code-block:: python
 
@@ -75,7 +75,7 @@ We use PyTorch to load CIFAR10, a popular colored image classification dataset f
         num_examples = {"trainset" : len(trainset), "testset" : len(testset)}
         return trainloader, testloader, num_examples
 
-Define the loss and optimizer with PyTorch. The training of the dataset is done by looping over the dataset, measure the corresponding loss and optimize it. 
+Define the loss and optimizer with PyTorch. The training of the dataset is done by looping over the dataset, measure the corresponding loss and optimize it.
 
 .. code-block:: python
 
@@ -91,7 +91,7 @@ Define the loss and optimizer with PyTorch. The training of the dataset is done
                 loss.backward()
                 optimizer.step()
 
-Define then the validation of the  machine learning network. We loop over the test set and measure the loss and accuracy of the test set. 
+Define then the validation of the  machine learning network. We loop over the test set and measure the loss and accuracy of the test set.
 
 .. code-block:: python
 
@@ -139,7 +139,7 @@ The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 Minute Blit
     net = Net().to(DEVICE)
     trainloader, testloader, num_examples = load_data()
 
-After loading the data set with :code:`load_data()` we define the Flower interface. 
+After loading the data set with :code:`load_data()` we define the Flower interface.
 
 The Flower server interacts with clients through an interface called
 :code:`Client`. When the server selects a particular client for training, it
diff --git a/doc/source/tutorial-quickstart-scikitlearn.rst b/doc/source/tutorial-quickstart-scikitlearn.rst
index 4921f63bab2c..b95118aa091f 100644
--- a/doc/source/tutorial-quickstart-scikitlearn.rst
+++ b/doc/source/tutorial-quickstart-scikitlearn.rst
@@ -7,13 +7,13 @@ Quickstart scikit-learn
 .. meta::
    :description: Check out this Federated Learning quickstart tutorial for using Flower with scikit-learn to train a linear regression model.
 
-In this tutorial, we will learn how to train a :code:`Logistic Regression` model on MNIST using Flower and scikit-learn. 
+In this tutorial, we will learn how to train a :code:`Logistic Regression` model on MNIST using Flower and scikit-learn.
 
-It is recommended to create a virtual environment and run everything within this `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_. 
+It is recommended to create a virtual environment and run everything within this `virtualenv <https://flower.ai/docs/recommended-env-setup.html>`_.
 
-Our example consists of one *server* and two *clients* all having the same model. 
+Our example consists of one *server* and two *clients* all having the same model.
 
-*Clients* are responsible for generating individual model parameter updates for the model based on their local datasets. 
+*Clients* are responsible for generating individual model parameter updates for the model based on their local datasets.
 These updates are then sent to the *server* which will aggregate them to produce an updated global model. Finally, the *server* sends this improved version of the model back to each *client*.
 A complete cycle of parameters updates is called a *round*.
 
@@ -59,7 +59,7 @@ Please check out :code:`utils.py` `here <https://github.com/adap/flower/blob/mai
 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 warnings
   import flwr as fl
   import numpy as np
@@ -70,7 +70,7 @@ The pre-defined functions are used in the :code:`client.py` and imported. The :c
   import utils
 
 
-We load the MNIST dataset from `OpenML <https://www.openml.org/d/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()`. 
+We load the MNIST dataset from `OpenML <https://www.openml.org/d/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()`.
 
 .. code-block:: python
 
diff --git a/doc/source/tutorial-quickstart-xgboost.rst b/doc/source/tutorial-quickstart-xgboost.rst
index 3a7b356c4d2a..ec9101f4b3fd 100644
--- a/doc/source/tutorial-quickstart-xgboost.rst
+++ b/doc/source/tutorial-quickstart-xgboost.rst
@@ -36,7 +36,7 @@ and then we dive into a more complex example (`full code xgboost-comprehensive <
 Environment Setup
 --------------------
 
-First of all, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_.
+First of all, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.ai/docs/recommended-env-setup.html>`_.
 
 We first need to install Flower and Flower Datasets. You can do this by running :
 
@@ -596,7 +596,7 @@ Comprehensive Federated XGBoost
 Now that you have known how federated XGBoost work with Flower, it's time to run some more comprehensive experiments by customising the experimental settings.
 In the xgboost-comprehensive example (`full code <https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive>`_),
 we provide more options to define various experimental setups, including aggregation strategies, data partitioning and centralised/distributed evaluation.
-We also support `Flower simulation <https://flower.dev/docs/framework/how-to-run-simulations.html>`_ making it easy to simulate large client cohorts in a resource-aware manner.
+We also support `Flower simulation <https://flower.ai/docs/framework/how-to-run-simulations.html>`_ making it easy to simulate large client cohorts in a resource-aware manner.
 Let's take a look!
 
 Cyclic training
diff --git a/doc/source/tutorial-series-customize-the-client-pytorch.ipynb b/doc/source/tutorial-series-customize-the-client-pytorch.ipynb
index 0ff67de6f51d..bcfdeb30d3c7 100644
--- a/doc/source/tutorial-series-customize-the-client-pytorch.ipynb
+++ b/doc/source/tutorial-series-customize-the-client-pytorch.ipynb
@@ -7,11 +7,11 @@
    "source": [
     "# Customize the client\n",
     "\n",
-    "Welcome to the fourth part of the Flower federated learning tutorial. In the previous parts of this tutorial, we introduced federated learning with PyTorch and Flower ([part 1](https://flower.dev/docs/framework/tutorial-get-started-with-flower-pytorch.html)), we learned how strategies can be used to customize the execution on both the server and the clients ([part 2](https://flower.dev/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html)), and we built our own custom strategy from scratch ([part 3](https://flower.dev/docs/framework/tutorial-build-a-strategy-from-scratch-pytorch.html)).\n",
+    "Welcome to the fourth part of the Flower federated learning tutorial. In the previous parts of this tutorial, we introduced federated learning with PyTorch and Flower ([part 1](https://flower.ai/docs/framework/tutorial-get-started-with-flower-pytorch.html)), we learned how strategies can be used to customize the execution on both the server and the clients ([part 2](https://flower.ai/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html)), and we built our own custom strategy from scratch ([part 3](https://flower.ai/docs/framework/tutorial-build-a-strategy-from-scratch-pytorch.html)).\n",
     "\n",
     "In this notebook, we revisit `NumPyClient` and introduce a new baseclass for building clients, simply named `Client`. In previous parts of this tutorial, we've based our client on `NumPyClient`, a convenience class which makes it easy to work with machine learning libraries that have good NumPy interoperability. With `Client`, we gain a lot of flexibility that we didn't have before, but we'll also have to do a few things the we didn't have to do before.\n",
     "\n",
-    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.dev/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
+    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.ai/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
     "\n",
     "Let's go deeper and see what it takes to move from `NumPyClient` to `Client`!"
    ]
diff --git a/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb b/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb
index 704ed520bf3e..f4b8acaa5bb8 100644
--- a/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb
+++ b/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb
@@ -9,9 +9,9 @@
     "\n",
     "Welcome to the Flower federated learning tutorial!\n",
     "\n",
-    "In this notebook, we'll build a federated learning system using Flower, [Flower Datasets](https://flower.dev/docs/datasets/) and PyTorch. In part 1, we use PyTorch for the model training pipeline and data loading. In part 2, we continue to federate the PyTorch-based pipeline using Flower.\n",
+    "In this notebook, we'll build a federated learning system using Flower, [Flower Datasets](https://flower.ai/docs/datasets/) and PyTorch. In part 1, we use PyTorch for the model training pipeline and data loading. In part 2, we continue to federate the PyTorch-based pipeline using Flower.\n",
     "\n",
-    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.dev/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
+    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.ai/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
     "\n",
     "Let's get stated!"
    ]
diff --git a/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb b/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb
index 06f53cd8e1b1..c758b8f637b0 100644
--- a/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb
+++ b/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb
@@ -7,11 +7,11 @@
    "source": [
     "# Use a federated learning strategy\n",
     "\n",
-    "Welcome to the next part of the federated learning tutorial. In previous parts of this tutorial, we introduced federated learning with PyTorch and Flower ([part 1](https://flower.dev/docs/framework/tutorial-get-started-with-flower-pytorch.html)).\n",
+    "Welcome to the next part of the federated learning tutorial. In previous parts of this tutorial, we introduced federated learning with PyTorch and Flower ([part 1](https://flower.ai/docs/framework/tutorial-get-started-with-flower-pytorch.html)).\n",
     "\n",
-    "In this notebook, we'll begin to customize the federated learning system we built in the introductory notebook (again, using [Flower](https://flower.dev/) and [PyTorch](https://pytorch.org/)).\n",
+    "In this notebook, we'll begin to customize the federated learning system we built in the introductory notebook (again, using [Flower](https://flower.ai/) and [PyTorch](https://pytorch.org/)).\n",
     "\n",
-    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.dev/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
+    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.ai/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
     "\n",
     "Let's move beyond FedAvg with Flower strategies!"
    ]
diff --git a/examples/doc/source/_templates/base.html b/examples/doc/source/_templates/base.html
index e4fe80720b74..08030fb08c15 100644
--- a/examples/doc/source/_templates/base.html
+++ b/examples/doc/source/_templates/base.html
@@ -5,7 +5,7 @@
     <meta charset="utf-8"/>
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark">
-    <link rel="canonical" href="https://flower.dev/docs/examples/{{ pagename }}.html">
+    <link rel="canonical" href="https://flower.ai/docs/examples/{{ pagename }}.html">
 
     {%- if metatags %}{{ metatags }}{% endif -%}
 
@@ -99,6 +99,6 @@
     {%- endblock -%}
 
     {%- endblock scripts -%}
-    <script defer data-domain="flower.dev" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
+    <script defer data-domain="flower.ai" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
   </body>
 </html>
diff --git a/examples/doc/source/conf.py b/examples/doc/source/conf.py
index 608aaeaeed6b..bf177aa5ae24 100644
--- a/examples/doc/source/conf.py
+++ b/examples/doc/source/conf.py
@@ -76,7 +76,7 @@
 html_title = f"Flower Examples {release}"
 html_logo = "_static/flower-logo.png"
 html_favicon = "_static/favicon.ico"
-html_baseurl = "https://flower.dev/docs/examples/"
+html_baseurl = "https://flower.ai/docs/examples/"
 
 html_theme_options = {
     #

From 6da77b1ee5061e681db68c0b9ae575448d2c7ac2 Mon Sep 17 00:00:00 2001
From: Gustavo Bertoli <gubertoli@gmail.com>
Date: Fri, 16 Feb 2024 17:35:23 +0100
Subject: [PATCH 015/102] Fix a typo in the datasets documentation (#2808)

---
 datasets/doc/source/tutorial-quickstart.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/datasets/doc/source/tutorial-quickstart.rst b/datasets/doc/source/tutorial-quickstart.rst
index b93a08f234f2..bd4f336d618d 100644
--- a/datasets/doc/source/tutorial-quickstart.rst
+++ b/datasets/doc/source/tutorial-quickstart.rst
@@ -40,7 +40,7 @@ To iid partition your dataset, choose the split you want to partition and the nu
   partition = fds.load_partition(0, "train")
   centralized_dataset = fds.load_full("test")
 
-Now you're ready to go. You have ten partitions created from the train split of the MNIST dataset and the test split
+Now you're ready to go. You have ten partitions created from the train split of the CIFAR10 dataset and the test split
 for the centralized evaluation. We will convert the type of the dataset from Hugging Face's `Dataset` type to the one
 supported by your framework.
 

From 17fb28f27ed1be4e1d9ebba8f4438b72d36c78e2 Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.ai>
Date: Fri, 16 Feb 2024 18:04:17 +0100
Subject: [PATCH 016/102] Introduce ServerApp compatibility mode (#2958)

---
 doc/source/ref-api-cli.rst                    | 28 +++++------
 .../{mt-pytorch => app-pytorch}/README.md     |  0
 .../{mt-pytorch => app-pytorch}/client.py     | 11 +----
 .../{mt-pytorch => app-pytorch}/driver.py     |  0
 .../pyproject.toml                            |  2 +-
 .../requirements.txt                          |  0
 .../start_driver.py => app-pytorch/server.py} | 13 +++--
 examples/{mt-pytorch => app-pytorch}/task.py  |  0
 src/py/flwr/server/compat/app.py              | 48 ++++++++++++-------
 src/py/flwr/server/run_serverapp.py           | 20 +++++++-
 src/py/flwr/server/serverapp.py               |  9 ++++
 11 files changed, 82 insertions(+), 49 deletions(-)
 rename examples/{mt-pytorch => app-pytorch}/README.md (100%)
 rename examples/{mt-pytorch => app-pytorch}/client.py (78%)
 rename examples/{mt-pytorch => app-pytorch}/driver.py (100%)
 rename examples/{mt-pytorch => app-pytorch}/pyproject.toml (83%)
 rename examples/{mt-pytorch => app-pytorch}/requirements.txt (100%)
 rename examples/{mt-pytorch/start_driver.py => app-pytorch/server.py} (85%)
 rename examples/{mt-pytorch => app-pytorch}/task.py (100%)

diff --git a/doc/source/ref-api-cli.rst b/doc/source/ref-api-cli.rst
index c0e8940061fc..63579143755d 100644
--- a/doc/source/ref-api-cli.rst
+++ b/doc/source/ref-api-cli.rst
@@ -31,22 +31,22 @@ flower-fleet-api
    :func:  _parse_args_run_fleet_api
    :prog: flower-fleet-api
 
-.. .. _flower-client-app-apiref:
+.. _flower-client-app-apiref:
 
-.. flower-client-app
-.. ~~~~~~~~~~~~~~~~~
+flower-client-app
+~~~~~~~~~~~~~~~~~
 
-.. .. argparse::
-..    :filename: flwr.client
-..    :func: _parse_args_run_client_app
-..    :prog: flower-client-app
+.. argparse::
+   :module: flwr.client.app
+   :func: _parse_args_run_client_app
+   :prog: flower-client-app
 
-.. .. _flower-server-app-apiref:
+.. _flower-server-app-apiref:
 
-.. flower-server-app
-.. ~~~~~~~~~~~~~~~~~
+flower-server-app
+~~~~~~~~~~~~~~~~~
 
-.. .. argparse::
-..    :filename: flwr.server
-..    :func: _parse_args_run_server_app
-..    :prog: flower-server-app
+.. argparse::
+   :module: flwr.server.run_serverapp
+   :func: _parse_args_run_server_app
+   :prog: flower-server-app
diff --git a/examples/mt-pytorch/README.md b/examples/app-pytorch/README.md
similarity index 100%
rename from examples/mt-pytorch/README.md
rename to examples/app-pytorch/README.md
diff --git a/examples/mt-pytorch/client.py b/examples/app-pytorch/client.py
similarity index 78%
rename from examples/mt-pytorch/client.py
rename to examples/app-pytorch/client.py
index 1f2db323ac34..8095a2d7aa93 100644
--- a/examples/mt-pytorch/client.py
+++ b/examples/app-pytorch/client.py
@@ -38,16 +38,7 @@ def client_fn(cid: str):
     return FlowerClient().to_client()
 
 
-# To run this: `flower-client client:app`
+# Run via `flower-client-app client:app`
 app = fl.client.ClientApp(
     client_fn=client_fn,
 )
-
-
-if __name__ == "__main__":
-    # Start Flower client
-    fl.client.start_client(
-        server_address="0.0.0.0:9092",  # "0.0.0.0:9093" for REST
-        client_fn=client_fn,
-        transport="grpc-rere",  # "rest" for REST
-    )
diff --git a/examples/mt-pytorch/driver.py b/examples/app-pytorch/driver.py
similarity index 100%
rename from examples/mt-pytorch/driver.py
rename to examples/app-pytorch/driver.py
diff --git a/examples/mt-pytorch/pyproject.toml b/examples/app-pytorch/pyproject.toml
similarity index 83%
rename from examples/mt-pytorch/pyproject.toml
rename to examples/app-pytorch/pyproject.toml
index 4978035495ea..5603b0f03480 100644
--- a/examples/mt-pytorch/pyproject.toml
+++ b/examples/app-pytorch/pyproject.toml
@@ -10,7 +10,7 @@ authors = ["The Flower Authors <hello@flower.dev>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
-flwr-nightly = {version = ">=1.0,<2.0", extras = ["rest", "simulation"]}
+flwr = { path = "../../", develop = true, extras = ["simulation"] }
 torch = "1.13.1"
 torchvision = "0.14.1"
 tqdm = "4.65.0"
diff --git a/examples/mt-pytorch/requirements.txt b/examples/app-pytorch/requirements.txt
similarity index 100%
rename from examples/mt-pytorch/requirements.txt
rename to examples/app-pytorch/requirements.txt
diff --git a/examples/mt-pytorch/start_driver.py b/examples/app-pytorch/server.py
similarity index 85%
rename from examples/mt-pytorch/start_driver.py
rename to examples/app-pytorch/server.py
index 307f4ebd1a3b..fbf3f24a133d 100644
--- a/examples/mt-pytorch/start_driver.py
+++ b/examples/app-pytorch/server.py
@@ -33,10 +33,9 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
     fit_metrics_aggregation_fn=weighted_average,
 )
 
-if __name__ == "__main__":
-    # Start Flower server
-    fl.server.driver.start_driver(
-        server_address="0.0.0.0:9091",
-        config=fl.server.ServerConfig(num_rounds=3),
-        strategy=strategy,
-    )
+
+# Run via `flower-server-app server:app`
+app = fl.server.ServerApp(
+    config=fl.server.ServerConfig(num_rounds=3),
+    strategy=strategy,
+)
diff --git a/examples/mt-pytorch/task.py b/examples/app-pytorch/task.py
similarity index 100%
rename from examples/mt-pytorch/task.py
rename to examples/app-pytorch/task.py
diff --git a/src/py/flwr/server/compat/app.py b/src/py/flwr/server/compat/app.py
index 06debb858c38..c0255391b885 100644
--- a/src/py/flwr/server/compat/app.py
+++ b/src/py/flwr/server/compat/app.py
@@ -24,7 +24,7 @@
 
 from flwr.common import EventType, event
 from flwr.common.address import parse_address
-from flwr.common.logger import log
+from flwr.common.logger import log, warn_deprecated_feature
 from flwr.proto import driver_pb2  # pylint: disable=E0611
 from flwr.server.app import init_defaults, run_fl
 from flwr.server.client_manager import ClientManager
@@ -33,6 +33,7 @@
 from flwr.server.server_config import ServerConfig
 from flwr.server.strategy import Strategy
 
+from ..driver import Driver
 from ..driver.grpc_driver import GrpcDriver
 from .driver_client_proxy import DriverClientProxy
 
@@ -54,6 +55,7 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
     strategy: Optional[Strategy] = None,
     client_manager: Optional[ClientManager] = None,
     root_certificates: Optional[Union[bytes, str]] = None,
+    driver: Optional[Driver] = None,
 ) -> History:
     """Start a Flower Driver API server.
 
@@ -81,6 +83,8 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
         The PEM-encoded root certificates as a byte string or a path string.
         If provided, a secure connection using the certificates will be
         established to an SSL-enabled Flower server.
+    driver : Optional[Driver] (default: None)
+        The Driver object to use.
 
     Returns
     -------
@@ -101,21 +105,29 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
     """
     event(EventType.START_DRIVER_ENTER)
 
-    # Parse IP address
-    parsed_address = parse_address(server_address)
-    if not parsed_address:
-        sys.exit(f"Server IP address ({server_address}) cannot be parsed.")
-    host, port, is_v6 = parsed_address
-    address = f"[{host}]:{port}" if is_v6 else f"{host}:{port}"
-
-    # Create the Driver
-    if isinstance(root_certificates, str):
-        root_certificates = Path(root_certificates).read_bytes()
-    grpc_driver = GrpcDriver(
-        driver_service_address=address, root_certificates=root_certificates
-    )
+    if driver:
+        # pylint: disable=protected-access
+        grpc_driver, _ = driver._get_grpc_driver_and_run_id()
+        # pylint: enable=protected-access
+    else:
+        # Not passing a `Driver` object is deprecated
+        warn_deprecated_feature("start_driver")
+
+        # Parse IP address
+        parsed_address = parse_address(server_address)
+        if not parsed_address:
+            sys.exit(f"Server IP address ({server_address}) cannot be parsed.")
+        host, port, is_v6 = parsed_address
+        address = f"[{host}]:{port}" if is_v6 else f"{host}:{port}"
+
+        # Create the Driver
+        if isinstance(root_certificates, str):
+            root_certificates = Path(root_certificates).read_bytes()
+        grpc_driver = GrpcDriver(
+            driver_service_address=address, root_certificates=root_certificates
+        )
+        grpc_driver.connect()
 
-    grpc_driver.connect()
     lock = threading.Lock()
 
     # Initialize the Driver API server and config
@@ -150,7 +162,11 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
 
     # Stop the Driver API server and the thread
     with lock:
-        grpc_driver.disconnect()
+        if driver:
+            del driver
+        else:
+            grpc_driver.disconnect()
+
     thread.join()
 
     event(EventType.START_SERVER_LEAVE)
diff --git a/src/py/flwr/server/run_serverapp.py b/src/py/flwr/server/run_serverapp.py
index 35fffcf2d7ba..685675b72e75 100644
--- a/src/py/flwr/server/run_serverapp.py
+++ b/src/py/flwr/server/run_serverapp.py
@@ -21,8 +21,11 @@
 from pathlib import Path
 
 from flwr.common import EventType, event
+from flwr.common.context import Context
 from flwr.common.logger import log
+from flwr.common.recordset import RecordSet
 
+from .driver.driver import Driver
 from .serverapp import ServerApp, load_server_app
 
 
@@ -89,6 +92,21 @@ def _load() -> ServerApp:
 
     log(DEBUG, "server_app: `%s`", server_app)
 
+    # Initialize Context
+    context = Context(state=RecordSet())
+
+    # Initialize Driver
+    driver = Driver(
+        driver_service_address=args.server,
+        root_certificates=root_certificates,
+    )
+
+    # Call ServerApp
+    server_app(driver=driver, context=context)
+
+    # Clean up
+    del driver
+
     event(EventType.RUN_SERVER_APP_LEAVE)
 
 
@@ -117,7 +135,7 @@ def _parse_args_run_server_app() -> argparse.ArgumentParser:
     )
     parser.add_argument(
         "--server",
-        default="0.0.0.0:9092",
+        default="0.0.0.0:9091",
         help="Server address",
     )
     parser.add_argument(
diff --git a/src/py/flwr/server/serverapp.py b/src/py/flwr/server/serverapp.py
index 1ffa087719dc..39c4f3f1e87f 100644
--- a/src/py/flwr/server/serverapp.py
+++ b/src/py/flwr/server/serverapp.py
@@ -23,6 +23,7 @@
 from flwr.server.strategy import Strategy
 
 from .client_manager import ClientManager
+from .compat import start_driver
 from .server import Server
 from .server_config import ServerConfig
 
@@ -44,6 +45,14 @@ def __init__(
 
     def __call__(self, driver: Driver, context: Context) -> None:
         """Execute `ServerApp`."""
+        # Compatibility mode
+        start_driver(
+            server=self.server,
+            config=self.config,
+            strategy=self.strategy,
+            client_manager=self.client_manager,
+            driver=driver,
+        )
 
 
 class LoadServerAppError(Exception):

From d76a6f27426650673cc9ac2c0f836ee977a86b11 Mon Sep 17 00:00:00 2001
From: Daniel Nata Nugraha <daniel.nata@flower.dev>
Date: Fri, 16 Feb 2024 23:10:51 +0000
Subject: [PATCH 017/102] Update quickstart-fastai requirements (#2467)

Co-authored-by: Taner Topal <taner@flower.dev>
Co-authored-by: jafermarq <javier@flower.dev>
---
 examples/quickstart-fastai/pyproject.toml   | 8 +++++---
 examples/quickstart-fastai/requirements.txt | 7 ++++---
 2 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/examples/quickstart-fastai/pyproject.toml b/examples/quickstart-fastai/pyproject.toml
index ffaa97267493..7f458ef3431c 100644
--- a/examples/quickstart-fastai/pyproject.toml
+++ b/examples/quickstart-fastai/pyproject.toml
@@ -9,6 +9,8 @@ description = "Fastai Federated Learning Quickstart with Flower"
 authors = ["The Flower Authors <hello@flower.dev>"]
 
 [tool.poetry.dependencies]
-python = ">=3.8,<3.10"
-flwr = "^1.0.0"
-fastai = "^2.7.10"
+python = ">=3.8,<3.11"
+flwr = ">=1.0,<2.0"
+fastai = "2.7.14"
+torch = "2.2.0"
+torchvision = "0.17.0"
diff --git a/examples/quickstart-fastai/requirements.txt b/examples/quickstart-fastai/requirements.txt
index 0a7f315018a2..9c6e8d77293a 100644
--- a/examples/quickstart-fastai/requirements.txt
+++ b/examples/quickstart-fastai/requirements.txt
@@ -1,3 +1,4 @@
-fastai~=2.7.12
-flwr~=1.4.0
-torch~=2.0.1
+flwr>=1.0, <2.0
+fastai==2.7.14
+torch==2.2.0
+torchvision==0.17.0

From c4bc415ea50341dfd0f1bab26d3b7294cb2754a0 Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.ai>
Date: Sat, 17 Feb 2024 12:27:18 +0100
Subject: [PATCH 018/102] Update README in app-pytorch (#2970)

---
 examples/app-pytorch/README.md | 25 +++++++++++++------------
 1 file changed, 13 insertions(+), 12 deletions(-)

diff --git a/examples/app-pytorch/README.md b/examples/app-pytorch/README.md
index 0f676044ee90..73d19ff11258 100644
--- a/examples/app-pytorch/README.md
+++ b/examples/app-pytorch/README.md
@@ -1,10 +1,9 @@
 # Flower App (PyTorch) 🧪
 
-🧪 = This example covers experimental features that might change in future versions of Flower
+> 🧪 = This example covers experimental features that might change in future versions of Flower
+> Please consult the regular PyTorch code examples ([quickstart](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch), [advanced](https://github.com/adap/flower/tree/main/examples/advanced-pytorch)) to learn how to use Flower with PyTorch.
 
-Please consult the regular PyTorch code examples ([quickstart](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch), [advanced](https://github.com/adap/flower/tree/main/examples/advanced-pytorch)) to learn how to use Flower with PyTorch.
-
-This how-to guide describes the deployment of a long-running Flower server.
+The following steps describe how to start a long-running Flower server (SuperLink) and then run a Flower App (consisting of a `ClientApp` and a `ServerApp`).
 
 ## Preconditions
 
@@ -13,10 +12,10 @@ Let's assume the following project structure:
 ```bash
 $ tree .
 .
-├── client.py
-├── server.py
-├── task.py
-└── requirements.txt
+├── client.py         # <-- contains `ClientApp`
+├── server.py         # <-- contains `ServerApp`
+├── task.py           # <-- task-specific code (model, data)
+└── requirements.txt  # <-- dependencies
 ```
 
 ## Install dependencies
@@ -25,13 +24,13 @@ $ tree .
 pip install -r requirements.txt
 ```
 
-## Start the SuperLink
+## Start the long-running Flower server (SuperLink)
 
 ```bash
 flower-superlink --insecure
 ```
 
-## Start the long-running Flower client
+## Start the long-running Flower client (SuperNode)
 
 In a new terminal window, start the first long-running Flower client:
 
@@ -45,8 +44,10 @@ In yet another new terminal window, start the second long-running Flower client:
 flower-client-app client:app --insecure
 ```
 
-## Start the driver
+## Run the Flower App
+
+With both the long-running server (SuperLink) and two clients (SuperNode) up and running, we can now run the actual Flower App:
 
 ```bash
-python start_driver.py
+flower-server-app server:app --insecure
 ```

From b731913a4864e90b47a45e8cbfaf12289d56a459 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Sat, 17 Feb 2024 12:39:44 +0100
Subject: [PATCH 019/102] Update `app-pytorch` example (#2967)

---
 examples/app-pytorch/pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/app-pytorch/pyproject.toml b/examples/app-pytorch/pyproject.toml
index 5603b0f03480..3fd8d3170f5e 100644
--- a/examples/app-pytorch/pyproject.toml
+++ b/examples/app-pytorch/pyproject.toml
@@ -3,7 +3,7 @@ requires = ["poetry-core>=1.4.0"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
-name = "mt-pytorch"
+name = "app-pytorch"
 version = "0.1.0"
 description = "Multi-Tenant Federated Learning with Flower and PyTorch"
 authors = ["The Flower Authors <hello@flower.dev>"]

From ce2d97019b10e6db9e226919158a7cda574875c0 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Sun, 18 Feb 2024 16:02:51 +0100
Subject: [PATCH 020/102] Make VCE use `Message` and `Flower` callable (#2783)

---
 doc/source/how-to-run-simulations.rst         |   2 +-
 e2e/pandas/simulation.py                      |   6 +-
 .../client/message_handler/message_handler.py |   2 +-
 src/py/flwr/simulation/app.py                 |   8 +-
 .../simulation/ray_transport/ray_actor.py     |  64 ++--
 .../ray_transport/ray_client_proxy.py         | 291 ++++++------------
 .../ray_transport/ray_client_proxy_test.py    | 103 ++++---
 src/py/flwr/simulation/ray_transport/utils.py |  23 --
 8 files changed, 189 insertions(+), 310 deletions(-)

diff --git a/doc/source/how-to-run-simulations.rst b/doc/source/how-to-run-simulations.rst
index 6e0520a79bf5..d1dcb511ed51 100644
--- a/doc/source/how-to-run-simulations.rst
+++ b/doc/source/how-to-run-simulations.rst
@@ -29,7 +29,7 @@ Running Flower simulations still require you to define your client class, a stra
     
     def client_fn(cid: str):
         # Return a standard Flower client
-        return MyFlowerClient()
+        return MyFlowerClient().to_client()
 
     # Launch the simulation
     hist = fl.simulation.start_simulation(
diff --git a/e2e/pandas/simulation.py b/e2e/pandas/simulation.py
index 91af84062712..b548b5ebb760 100644
--- a/e2e/pandas/simulation.py
+++ b/e2e/pandas/simulation.py
@@ -1,12 +1,8 @@
 import flwr as fl
 
-from client import FlowerClient
+from client import client_fn
 from strategy import FedAnalytics
 
-def client_fn(cid):
-    _ = cid
-    return FlowerClient()
-
 hist = fl.simulation.start_simulation(
     client_fn=client_fn,
     num_clients=2,
diff --git a/src/py/flwr/client/message_handler/message_handler.py b/src/py/flwr/client/message_handler/message_handler.py
index 93de7d7d8821..f8c8a725aec7 100644
--- a/src/py/flwr/client/message_handler/message_handler.py
+++ b/src/py/flwr/client/message_handler/message_handler.py
@@ -109,7 +109,7 @@ def handle_legacy_message_from_msgtype(
     client_fn: ClientFn, message: Message, context: Context
 ) -> Message:
     """Handle legacy message in the inner most mod."""
-    client = client_fn("-1")
+    client = client_fn(str(message.metadata.node_id))
 
     client.set_context(context)
 
diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py
index b159042588c9..9ee230942890 100644
--- a/src/py/flwr/simulation/app.py
+++ b/src/py/flwr/simulation/app.py
@@ -35,7 +35,7 @@
 from flwr.server.server_config import ServerConfig
 from flwr.server.strategy import Strategy
 from flwr.simulation.ray_transport.ray_actor import (
-    DefaultActor,
+    ClientAppActor,
     VirtualClientEngineActor,
     VirtualClientEngineActorPool,
     pool_size_from_resources,
@@ -83,7 +83,7 @@ def start_simulation(
     client_manager: Optional[ClientManager] = None,
     ray_init_args: Optional[Dict[str, Any]] = None,
     keep_initialised: Optional[bool] = False,
-    actor_type: Type[VirtualClientEngineActor] = DefaultActor,
+    actor_type: Type[VirtualClientEngineActor] = ClientAppActor,
     actor_kwargs: Optional[Dict[str, Any]] = None,
     actor_scheduling: Union[str, NodeAffinitySchedulingStrategy] = "DEFAULT",
 ) -> History:
@@ -139,10 +139,10 @@ def start_simulation(
     keep_initialised: Optional[bool] (default: False)
         Set to True to prevent `ray.shutdown()` in case `ray.is_initialized()=True`.
 
-    actor_type: VirtualClientEngineActor (default: DefaultActor)
+    actor_type: VirtualClientEngineActor (default: ClientAppActor)
         Optionally specify the type of actor to use. The actor object, which
         persists throughout the simulation, will be the process in charge of
-        running the clients' jobs (i.e. their `fit()` method).
+        executing a ClientApp wrapping input argument `client_fn`.
 
     actor_kwargs: Optional[Dict[str, Any]] (default: None)
         If you want to create your own Actor classes, you might need to pass
diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py
index 853566a4cbeb..974773a3f577 100644
--- a/src/py/flwr/simulation/ray_transport/ray_actor.py
+++ b/src/py/flwr/simulation/ray_transport/ray_actor.py
@@ -25,18 +25,12 @@
 from ray import ObjectRef
 from ray.util.actor_pool import ActorPool
 
-from flwr import common
-from flwr.client import Client, ClientFn
+from flwr.client.clientapp import ClientApp
 from flwr.common.context import Context
 from flwr.common.logger import log
-from flwr.simulation.ray_transport.utils import check_clientfn_returns_client
+from flwr.common.message import Message
 
-# All possible returns by a client
-ClientRes = Union[
-    common.GetPropertiesRes, common.GetParametersRes, common.FitRes, common.EvaluateRes
-]
-# A function to be executed by a client to obtain some results
-JobFn = Callable[[Client], ClientRes]
+ClientAppFn = Callable[[], ClientApp]
 
 
 class ClientException(Exception):
@@ -58,27 +52,25 @@ def terminate(self) -> None:
 
     def run(
         self,
-        client_fn: ClientFn,
-        job_fn: JobFn,
+        client_app_fn: ClientAppFn,
+        message: Message,
         cid: str,
         context: Context,
-    ) -> Tuple[str, ClientRes, Context]:
+    ) -> Tuple[str, Message, Context]:
         """Run a client run."""
-        # Execute tasks and return result
+        # Pass message through ClientApp and return a message
         # return also cid which is needed to ensure results
         # from the pool are correctly assigned to each ClientProxy
         try:
-            # Instantiate client (check 'Client' type is returned)
-            client = check_clientfn_returns_client(client_fn(cid))
-            # Inject context
-            client.set_context(context)
-            # Run client job
-            job_results = job_fn(client)
-            # Retrieve context (potentially updated)
-            updated_context = client.get_context()
+            # Load app
+            app: ClientApp = client_app_fn()
+
+            # Handle task message
+            out_message = app(message=message, context=context)
+
         except Exception as ex:
             client_trace = traceback.format_exc()
-            message = (
+            mssg = (
                 "\n\tSomething went wrong when running your client run."
                 "\n\tClient "
                 + cid
@@ -87,13 +79,13 @@ def run(
                 + " was running its run."
                 "\n\tException triggered on the client side: " + client_trace,
             )
-            raise ClientException(str(message)) from ex
+            raise ClientException(str(mssg)) from ex
 
-        return cid, job_results, updated_context
+        return cid, out_message, context
 
 
 @ray.remote
-class DefaultActor(VirtualClientEngineActor):
+class ClientAppActor(VirtualClientEngineActor):
     """A Ray Actor class that runs client runs.
 
     Parameters
@@ -237,16 +229,16 @@ def add_actors_to_pool(self, num_actors: int) -> None:
             self._idle_actors.extend(new_actors)
             self.num_actors += num_actors
 
-    def submit(self, fn: Any, value: Tuple[ClientFn, JobFn, str, Context]) -> None:
-        """Take idle actor and assign it a client run.
+    def submit(self, fn: Any, value: Tuple[ClientAppFn, Message, str, Context]) -> None:
+        """Take an idle actor and assign it to run a client app and Message.
 
         Submit a job to an actor by first removing it from the list of idle actors, then
-        check if this actor was flagged to be removed from the pool
+        check if this actor was flagged to be removed from the pool.
         """
-        client_fn, job_fn, cid, context = value
+        app_fn, mssg, cid, context = value
         actor = self._idle_actors.pop()
         if self._check_and_remove_actor_from_pool(actor):
-            future = fn(actor, client_fn, job_fn, cid, context)
+            future = fn(actor, app_fn, mssg, cid, context)
             future_key = tuple(future) if isinstance(future, List) else future
             self._future_to_actor[future_key] = (self._next_task_index, actor, cid)
             self._next_task_index += 1
@@ -255,7 +247,7 @@ def submit(self, fn: Any, value: Tuple[ClientFn, JobFn, str, Context]) -> None:
             self._cid_to_future[cid]["future"] = future_key
 
     def submit_client_job(
-        self, actor_fn: Any, job: Tuple[ClientFn, JobFn, str, Context]
+        self, actor_fn: Any, job: Tuple[ClientAppFn, Message, str, Context]
     ) -> None:
         """Submit a job while tracking client ids."""
         _, _, cid, _ = job
@@ -295,7 +287,7 @@ def _is_future_ready(self, cid: str) -> bool:
 
         return self._cid_to_future[cid]["ready"]  # type: ignore
 
-    def _fetch_future_result(self, cid: str) -> Tuple[ClientRes, Context]:
+    def _fetch_future_result(self, cid: str) -> Tuple[Message, Context]:
         """Fetch result and updated context for a VirtualClient from Object Store.
 
         The job submitted by the ClientProxy interfacing with client with cid=cid is
@@ -303,9 +295,9 @@ def _fetch_future_result(self, cid: str) -> Tuple[ClientRes, Context]:
         """
         try:
             future: ObjectRef[Any] = self._cid_to_future[cid]["future"]  # type: ignore
-            res_cid, res, updated_context = ray.get(
+            res_cid, out_mssg, updated_context = ray.get(
                 future
-            )  # type: (str, ClientRes, Context)
+            )  # type: (str, Message, Context)
         except ray.exceptions.RayActorError as ex:
             log(ERROR, ex)
             if hasattr(ex, "actor_id"):
@@ -322,7 +314,7 @@ def _fetch_future_result(self, cid: str) -> Tuple[ClientRes, Context]:
         # Reset mapping
         self._reset_cid_to_future_dict(cid)
 
-        return res, updated_context
+        return out_mssg, updated_context
 
     def _flag_actor_for_removal(self, actor_id_hex: str) -> None:
         """Flag actor that should be removed from pool."""
@@ -409,7 +401,7 @@ def process_unordered_future(self, timeout: Optional[float] = None) -> None:
 
     def get_client_result(
         self, cid: str, timeout: Optional[float]
-    ) -> Tuple[ClientRes, Context]:
+    ) -> Tuple[Message, Context]:
         """Get result from VirtualClient with specific cid."""
         # Loop until all jobs submitted to the pool are completed. Break early
         # if the result for the ClientProxy calling this method is ready
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
index 894012dc6d70..ddac030b2ef0 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
@@ -17,107 +17,33 @@
 
 import traceback
 from logging import ERROR
-from typing import Dict, Optional, cast
-
-import ray
+from typing import Optional
 
 from flwr import common
-from flwr.client import Client, ClientFn
-from flwr.client.client import (
-    maybe_call_evaluate,
-    maybe_call_fit,
-    maybe_call_get_parameters,
-    maybe_call_get_properties,
-)
+from flwr.client import ClientFn
+from flwr.client.clientapp import ClientApp
 from flwr.client.node_state import NodeState
+from flwr.common.constant import (
+    MESSAGE_TYPE_EVALUATE,
+    MESSAGE_TYPE_FIT,
+    MESSAGE_TYPE_GET_PARAMETERS,
+    MESSAGE_TYPE_GET_PROPERTIES,
+)
 from flwr.common.logger import log
-from flwr.server.client_proxy import ClientProxy
-from flwr.simulation.ray_transport.ray_actor import (
-    ClientRes,
-    JobFn,
-    VirtualClientEngineActorPool,
+from flwr.common.message import Message, Metadata
+from flwr.common.recordset import RecordSet
+from flwr.common.recordset_compat import (
+    evaluateins_to_recordset,
+    fitins_to_recordset,
+    getparametersins_to_recordset,
+    getpropertiesins_to_recordset,
+    recordset_to_evaluateres,
+    recordset_to_fitres,
+    recordset_to_getparametersres,
+    recordset_to_getpropertiesres,
 )
-
-
-class RayClientProxy(ClientProxy):
-    """Flower client proxy which delegates work using Ray."""
-
-    def __init__(self, client_fn: ClientFn, cid: str, resources: Dict[str, float]):
-        super().__init__(cid)
-        self.client_fn = client_fn
-        self.resources = resources
-
-    def get_properties(
-        self, ins: common.GetPropertiesIns, timeout: Optional[float]
-    ) -> common.GetPropertiesRes:
-        """Return client's properties."""
-        future_get_properties_res = launch_and_get_properties.options(  # type: ignore
-            **self.resources,
-        ).remote(self.client_fn, self.cid, ins)
-        try:
-            res = ray.get(future_get_properties_res, timeout=timeout)
-        except Exception as ex:
-            log(ERROR, ex)
-            raise ex
-        return cast(
-            common.GetPropertiesRes,
-            res,
-        )
-
-    def get_parameters(
-        self, ins: common.GetParametersIns, timeout: Optional[float]
-    ) -> common.GetParametersRes:
-        """Return the current local model parameters."""
-        future_paramseters_res = launch_and_get_parameters.options(  # type: ignore
-            **self.resources,
-        ).remote(self.client_fn, self.cid, ins)
-        try:
-            res = ray.get(future_paramseters_res, timeout=timeout)
-        except Exception as ex:
-            log(ERROR, ex)
-            raise ex
-        return cast(
-            common.GetParametersRes,
-            res,
-        )
-
-    def fit(self, ins: common.FitIns, timeout: Optional[float]) -> common.FitRes:
-        """Train model parameters on the locally held dataset."""
-        future_fit_res = launch_and_fit.options(  # type: ignore
-            **self.resources,
-        ).remote(self.client_fn, self.cid, ins)
-        try:
-            res = ray.get(future_fit_res, timeout=timeout)
-        except Exception as ex:
-            log(ERROR, ex)
-            raise ex
-        return cast(
-            common.FitRes,
-            res,
-        )
-
-    def evaluate(
-        self, ins: common.EvaluateIns, timeout: Optional[float]
-    ) -> common.EvaluateRes:
-        """Evaluate model parameters on the locally held dataset."""
-        future_evaluate_res = launch_and_evaluate.options(  # type: ignore
-            **self.resources,
-        ).remote(self.client_fn, self.cid, ins)
-        try:
-            res = ray.get(future_evaluate_res, timeout=timeout)
-        except Exception as ex:
-            log(ERROR, ex)
-            raise ex
-        return cast(
-            common.EvaluateRes,
-            res,
-        )
-
-    def reconnect(
-        self, ins: common.ReconnectIns, timeout: Optional[float]
-    ) -> common.DisconnectRes:
-        """Disconnect and (optionally) reconnect later."""
-        return common.DisconnectRes(reason="")  # Nothing to do here (yet)
+from flwr.server.client_proxy import ClientProxy
+from flwr.simulation.ray_transport.ray_actor import VirtualClientEngineActorPool
 
 
 class RayActorClientProxy(ClientProxy):
@@ -127,15 +53,17 @@ def __init__(
         self, client_fn: ClientFn, cid: str, actor_pool: VirtualClientEngineActorPool
     ):
         super().__init__(cid)
-        self.client_fn = client_fn
+
+        def _load_app() -> ClientApp:
+            return ClientApp(client_fn=client_fn)
+
+        self.app_fn = _load_app
         self.actor_pool = actor_pool
         self.proxy_state = NodeState()
 
-    def _submit_job(self, job_fn: JobFn, timeout: Optional[float]) -> ClientRes:
-        # The VCE is not exposed to TaskIns, it won't handle multilple runs
-        # For the time being, fixing run_id is a small compromise
-        # This will be one of the first points to address integrating VCE + DriverAPI
-        run_id = 0
+    def _submit_job(self, message: Message, timeout: Optional[float]) -> Message:
+        """Sumbit a message to the ActorPool."""
+        run_id = message.metadata.run_id
 
         # Register state
         self.proxy_state.register_context(run_id=run_id)
@@ -145,10 +73,12 @@ def _submit_job(self, job_fn: JobFn, timeout: Optional[float]) -> ClientRes:
 
         try:
             self.actor_pool.submit_client_job(
-                lambda a, c_fn, j_fn, cid, state: a.run.remote(c_fn, j_fn, cid, state),
-                (self.client_fn, job_fn, self.cid, state),
+                lambda a, a_fn, mssg, cid, state: a.run.remote(a_fn, mssg, cid, state),
+                (self.app_fn, message, self.cid, state),
+            )
+            out_mssg, updated_context = self.actor_pool.get_client_result(
+                self.cid, timeout
             )
-            res, updated_context = self.actor_pool.get_client_result(self.cid, timeout)
 
             # Update state
             self.proxy_state.update_context(run_id=run_id, context=updated_context)
@@ -162,134 +92,87 @@ def _submit_job(self, job_fn: JobFn, timeout: Optional[float]) -> ClientRes:
             log(ERROR, ex)
             raise ex
 
-        return res
+        return out_mssg
+
+    def _wrap_recordset_in_message(
+        self,
+        recordset: RecordSet,
+        message_type: str,
+        timeout: Optional[float],
+    ) -> Message:
+        """Wrap a RecordSet inside a Message."""
+        return Message(
+            content=recordset,
+            metadata=Metadata(
+                run_id=0,
+                message_id="",
+                group_id="",
+                node_id=int(self.cid),
+                ttl=str(timeout) if timeout else "",
+                message_type=message_type,
+            ),
+        )
 
     def get_properties(
         self, ins: common.GetPropertiesIns, timeout: Optional[float]
     ) -> common.GetPropertiesRes:
         """Return client's properties."""
+        recordset = getpropertiesins_to_recordset(ins)
+        message = self._wrap_recordset_in_message(
+            recordset,
+            message_type=MESSAGE_TYPE_GET_PROPERTIES,
+            timeout=timeout,
+        )
 
-        def get_properties(client: Client) -> common.GetPropertiesRes:
-            return maybe_call_get_properties(
-                client=client,
-                get_properties_ins=ins,
-            )
+        message_out = self._submit_job(message, timeout)
 
-        res = self._submit_job(get_properties, timeout)
-
-        return cast(
-            common.GetPropertiesRes,
-            res,
-        )
+        return recordset_to_getpropertiesres(message_out.content)
 
     def get_parameters(
         self, ins: common.GetParametersIns, timeout: Optional[float]
     ) -> common.GetParametersRes:
         """Return the current local model parameters."""
+        recordset = getparametersins_to_recordset(ins)
+        message = self._wrap_recordset_in_message(
+            recordset,
+            message_type=MESSAGE_TYPE_GET_PARAMETERS,
+            timeout=timeout,
+        )
 
-        def get_parameters(client: Client) -> common.GetParametersRes:
-            return maybe_call_get_parameters(
-                client=client,
-                get_parameters_ins=ins,
-            )
-
-        res = self._submit_job(get_parameters, timeout)
+        message_out = self._submit_job(message, timeout)
 
-        return cast(
-            common.GetParametersRes,
-            res,
-        )
+        return recordset_to_getparametersres(message_out.content, keep_input=False)
 
     def fit(self, ins: common.FitIns, timeout: Optional[float]) -> common.FitRes:
         """Train model parameters on the locally held dataset."""
+        recordset = fitins_to_recordset(
+            ins, keep_input=True
+        )  # This must stay TRUE since ins are in-memory
+        message = self._wrap_recordset_in_message(
+            recordset, message_type=MESSAGE_TYPE_FIT, timeout=timeout
+        )
 
-        def fit(client: Client) -> common.FitRes:
-            return maybe_call_fit(
-                client=client,
-                fit_ins=ins,
-            )
+        message_out = self._submit_job(message, timeout)
 
-        res = self._submit_job(fit, timeout)
-
-        return cast(
-            common.FitRes,
-            res,
-        )
+        return recordset_to_fitres(message_out.content, keep_input=False)
 
     def evaluate(
         self, ins: common.EvaluateIns, timeout: Optional[float]
     ) -> common.EvaluateRes:
         """Evaluate model parameters on the locally held dataset."""
+        recordset = evaluateins_to_recordset(
+            ins, keep_input=True
+        )  # This must stay TRUE since ins are in-memory
+        message = self._wrap_recordset_in_message(
+            recordset, message_type=MESSAGE_TYPE_EVALUATE, timeout=timeout
+        )
 
-        def evaluate(client: Client) -> common.EvaluateRes:
-            return maybe_call_evaluate(
-                client=client,
-                evaluate_ins=ins,
-            )
-
-        res = self._submit_job(evaluate, timeout)
+        message_out = self._submit_job(message, timeout)
 
-        return cast(
-            common.EvaluateRes,
-            res,
-        )
+        return recordset_to_evaluateres(message_out.content)
 
     def reconnect(
         self, ins: common.ReconnectIns, timeout: Optional[float]
     ) -> common.DisconnectRes:
         """Disconnect and (optionally) reconnect later."""
         return common.DisconnectRes(reason="")  # Nothing to do here (yet)
-
-
-@ray.remote
-def launch_and_get_properties(
-    client_fn: ClientFn, cid: str, get_properties_ins: common.GetPropertiesIns
-) -> common.GetPropertiesRes:
-    """Exectue get_properties remotely."""
-    client: Client = _create_client(client_fn, cid)
-    return maybe_call_get_properties(
-        client=client,
-        get_properties_ins=get_properties_ins,
-    )
-
-
-@ray.remote
-def launch_and_get_parameters(
-    client_fn: ClientFn, cid: str, get_parameters_ins: common.GetParametersIns
-) -> common.GetParametersRes:
-    """Exectue get_parameters remotely."""
-    client: Client = _create_client(client_fn, cid)
-    return maybe_call_get_parameters(
-        client=client,
-        get_parameters_ins=get_parameters_ins,
-    )
-
-
-@ray.remote
-def launch_and_fit(
-    client_fn: ClientFn, cid: str, fit_ins: common.FitIns
-) -> common.FitRes:
-    """Exectue fit remotely."""
-    client: Client = _create_client(client_fn, cid)
-    return maybe_call_fit(
-        client=client,
-        fit_ins=fit_ins,
-    )
-
-
-@ray.remote
-def launch_and_evaluate(
-    client_fn: ClientFn, cid: str, evaluate_ins: common.EvaluateIns
-) -> common.EvaluateRes:
-    """Exectue evaluate remotely."""
-    client: Client = _create_client(client_fn, cid)
-    return maybe_call_evaluate(
-        client=client,
-        evaluate_ins=evaluate_ins,
-    )
-
-
-def _create_client(client_fn: ClientFn, cid: str) -> Client:
-    """Create a client instance."""
-    # Materialize client
-    return client_fn(cid)
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
index b380d37d01c8..9ade31c323d8 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
@@ -17,19 +17,25 @@
 
 from math import pi
 from random import shuffle
-from typing import List, Tuple, Type, cast
+from typing import Dict, List, Tuple, Type
 
 import ray
 
 from flwr.client import Client, NumPyClient
-from flwr.common import Code, GetPropertiesRes, Status
+from flwr.client.clientapp import ClientApp
+from flwr.common import Config, Scalar
 from flwr.common.configsrecord import ConfigsRecord
+from flwr.common.constant import MESSAGE_TYPE_GET_PROPERTIES
 from flwr.common.context import Context
+from flwr.common.message import Message, Metadata
 from flwr.common.recordset import RecordSet
+from flwr.common.recordset_compat import (
+    getpropertiesins_to_recordset,
+    recordset_to_getpropertiesres,
+)
+from flwr.common.recordset_compat_test import _get_valid_getpropertiesins
 from flwr.simulation.ray_transport.ray_actor import (
-    ClientRes,
-    DefaultActor,
-    JobFn,
+    ClientAppActor,
     VirtualClientEngineActor,
     VirtualClientEngineActorPool,
 )
@@ -42,34 +48,24 @@ class DummyClient(NumPyClient):
     def __init__(self, cid: str) -> None:
         self.cid = int(cid)
 
-
-def get_dummy_client(cid: str) -> Client:
-    """Return a DummyClient converted to Client type."""
-    return DummyClient(cid).to_client()
-
-
-# A dummy run
-def job_fn(cid: str) -> JobFn:  # pragma: no cover
-    """Construct a simple job with cid dependency."""
-
-    def cid_times_pi(client: Client) -> ClientRes:  # pylint: disable=unused-argument
-        result = int(cid) * pi
+    def get_properties(self, config: Config) -> Dict[str, Scalar]:
+        """Return properties by doing a simple calculation."""
+        result = int(self.cid) * pi
 
         # store something in context
-        client.numpy_client.context.state.set_configs(  # type: ignore
+        self.context.state.set_configs(
             "result", record=ConfigsRecord({"result": str(result)})
         )
+        return {"result": result}
 
-        # now let's convert it to a GetPropertiesRes response
-        return GetPropertiesRes(
-            status=Status(Code(0), message="test"), properties={"result": result}
-        )
 
-    return cid_times_pi
+def get_dummy_client(cid: str) -> Client:
+    """Return a DummyClient converted to Client type."""
+    return DummyClient(cid).to_client()
 
 
 def prep(
-    actor_type: Type[VirtualClientEngineActor] = DefaultActor,
+    actor_type: Type[VirtualClientEngineActor] = ClientAppActor,
 ) -> Tuple[List[RayActorClientProxy], VirtualClientEngineActorPool]:  # pragma: no cover
     """Prepare ClientProxies and pool for tests."""
     client_resources = {"num_cpus": 1, "num_gpus": 0.0}
@@ -104,13 +100,23 @@ def test_cid_consistency_one_at_a_time() -> None:
     Submit one job and waits for completion. Then submits the next and so on
     """
     proxies, _ = prep()
+
+    getproperties_ins = _get_valid_getpropertiesins()
+    recordset = getpropertiesins_to_recordset(getproperties_ins)
+
     # submit jobs one at a time
     for prox in proxies:
-        res = prox._submit_job(  # pylint: disable=protected-access
-            job_fn=job_fn(prox.cid), timeout=None
+        message = prox._wrap_recordset_in_message(  # pylint: disable=protected-access
+            recordset,
+            MESSAGE_TYPE_GET_PROPERTIES,
+            timeout=None,
         )
+        message_out = prox._submit_job(  # pylint: disable=protected-access
+            message=message, timeout=None
+        )
+
+        res = recordset_to_getpropertiesres(message_out.content)
 
-        res = cast(GetPropertiesRes, res)
         assert int(prox.cid) * pi == res.properties["result"]
 
     ray.shutdown()
@@ -125,6 +131,9 @@ def test_cid_consistency_all_submit_first_run_consistency() -> None:
     proxies, _ = prep()
     run_id = 0
 
+    getproperties_ins = _get_valid_getpropertiesins()
+    recordset = getpropertiesins_to_recordset(getproperties_ins)
+
     # submit all jobs (collect later)
     shuffle(proxies)
     for prox in proxies:
@@ -133,18 +142,24 @@ def test_cid_consistency_all_submit_first_run_consistency() -> None:
         # Retrieve state
         state = prox.proxy_state.retrieve_context(run_id=run_id)
 
-        job = job_fn(prox.cid)
+        message = prox._wrap_recordset_in_message(  # pylint: disable=protected-access
+            recordset,
+            message_type=MESSAGE_TYPE_GET_PROPERTIES,
+            timeout=None,
+        )
         prox.actor_pool.submit_client_job(
-            lambda a, c_fn, j_fn, cid, state: a.run.remote(c_fn, j_fn, cid, state),
-            (prox.client_fn, job, prox.cid, state),
+            lambda a, a_fn, mssg, cid, state: a.run.remote(a_fn, mssg, cid, state),
+            (prox.app_fn, message, prox.cid, state),
         )
 
     # fetch results one at a time
     shuffle(proxies)
     for prox in proxies:
-        res, updated_context = prox.actor_pool.get_client_result(prox.cid, timeout=None)
+        message_out, updated_context = prox.actor_pool.get_client_result(
+            prox.cid, timeout=None
+        )
         prox.proxy_state.update_context(run_id, context=updated_context)
-        res = cast(GetPropertiesRes, res)
+        res = recordset_to_getpropertiesres(message_out.content)
 
         assert int(prox.cid) * pi == res.properties["result"]
         assert (
@@ -163,20 +178,36 @@ def test_cid_consistency_without_proxies() -> None:
     num_clients = len(proxies)
     cids = [str(cid) for cid in range(num_clients)]
 
+    getproperties_ins = _get_valid_getpropertiesins()
+    recordset = getpropertiesins_to_recordset(getproperties_ins)
+
+    def _load_app() -> ClientApp:
+        return ClientApp(client_fn=get_dummy_client)
+
     # submit all jobs (collect later)
     shuffle(cids)
     for cid in cids:
-        job = job_fn(cid)
+        message = Message(
+            content=recordset,
+            metadata=Metadata(
+                run_id=0,
+                message_id="",
+                group_id="",
+                ttl="",
+                node_id=int(cid),
+                message_type=MESSAGE_TYPE_GET_PROPERTIES,
+            ),
+        )
         pool.submit_client_job(
             lambda a, c_fn, j_fn, cid_, state: a.run.remote(c_fn, j_fn, cid_, state),
-            (get_dummy_client, job, cid, Context(state=RecordSet())),
+            (_load_app, message, cid, Context(state=RecordSet())),
         )
 
     # fetch results one at a time
     shuffle(cids)
     for cid in cids:
-        res, _ = pool.get_client_result(cid, timeout=None)
-        res = cast(GetPropertiesRes, res)
+        message_out, _ = pool.get_client_result(cid, timeout=None)
+        res = recordset_to_getpropertiesres(message_out.content)
         assert int(cid) * pi == res.properties["result"]
 
     ray.shutdown()
diff --git a/src/py/flwr/simulation/ray_transport/utils.py b/src/py/flwr/simulation/ray_transport/utils.py
index dd9fb6b2aa85..3861164998a4 100644
--- a/src/py/flwr/simulation/ray_transport/utils.py
+++ b/src/py/flwr/simulation/ray_transport/utils.py
@@ -18,7 +18,6 @@
 import warnings
 from logging import ERROR
 
-from flwr.client import Client
 from flwr.common.logger import log
 
 try:
@@ -60,25 +59,3 @@ def enable_tf_gpu_growth() -> None:
             log(ERROR, traceback.format_exc())
             log(ERROR, ex)
             raise ex
-
-
-def check_clientfn_returns_client(client: Client) -> Client:
-    """Warn once that clients returned in `clinet_fn` should be of type Client.
-
-    This is here for backwards compatibility. If a ClientFn is provided returning
-    a different type of client (e.g. NumPyClient) we'll warn the user but convert
-    the client internally to `Client` by calling `.to_client()`.
-    """
-    if not isinstance(client, Client):
-        mssg = (
-            " Ensure your client is of type `flwr.client.Client`. Please convert it"
-            " using the `.to_client()` method before returning it"
-            " in the `client_fn` you pass to `start_simulation`."
-            " We have applied this conversion on your behalf."
-            " Not returning a `Client` might trigger an error in future"
-            " versions of Flower."
-        )
-
-        warnings.warn(mssg, DeprecationWarning, stacklevel=2)
-        client = client.to_client()
-    return client

From e6fe6f48ae3ca391182e928ac6673ef18906be8f Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.ai>
Date: Sun, 18 Feb 2024 16:42:17 +0100
Subject: [PATCH 021/102] Introduce custom ServerApp functions (#2972)

---
 src/py/flwr/server/__init__.py                |   2 +-
 src/py/flwr/server/run_serverapp.py           |   6 +-
 .../server/{serverapp.py => server_app.py}    | 102 +++++++++++++++---
 src/py/flwr/server/server_app_test.py         |  63 +++++++++++
 4 files changed, 154 insertions(+), 19 deletions(-)
 rename src/py/flwr/server/{serverapp.py => server_app.py} (51%)
 create mode 100644 src/py/flwr/server/server_app_test.py

diff --git a/src/py/flwr/server/__init__.py b/src/py/flwr/server/__init__.py
index 09372e258861..cad054fb86ab 100644
--- a/src/py/flwr/server/__init__.py
+++ b/src/py/flwr/server/__init__.py
@@ -26,8 +26,8 @@
 from .history import History as History
 from .run_serverapp import run_server_app as run_server_app
 from .server import Server as Server
+from .server_app import ServerApp as ServerApp
 from .server_config import ServerConfig as ServerConfig
-from .serverapp import ServerApp as ServerApp
 
 __all__ = [
     "ClientManager",
diff --git a/src/py/flwr/server/run_serverapp.py b/src/py/flwr/server/run_serverapp.py
index 685675b72e75..283348f05457 100644
--- a/src/py/flwr/server/run_serverapp.py
+++ b/src/py/flwr/server/run_serverapp.py
@@ -26,7 +26,7 @@
 from flwr.common.recordset import RecordSet
 
 from .driver.driver import Driver
-from .serverapp import ServerApp, load_server_app
+from .server_app import ServerApp, load_server_app
 
 
 def run_server_app() -> None:
@@ -78,8 +78,6 @@ def run_server_app() -> None:
         root_certificates,
     )
 
-    log(WARN, "Not implemented: run_server_app")
-
     server_app_dir = args.dir
     if server_app_dir is not None:
         sys.path.insert(0, server_app_dir)
@@ -90,8 +88,6 @@ def _load() -> ServerApp:
 
     server_app = _load()
 
-    log(DEBUG, "server_app: `%s`", server_app)
-
     # Initialize Context
     context = Context(state=RecordSet())
 
diff --git a/src/py/flwr/server/serverapp.py b/src/py/flwr/server/server_app.py
similarity index 51%
rename from src/py/flwr/server/serverapp.py
rename to src/py/flwr/server/server_app.py
index 39c4f3f1e87f..027818019786 100644
--- a/src/py/flwr/server/serverapp.py
+++ b/src/py/flwr/server/server_app.py
@@ -16,9 +16,10 @@
 
 
 import importlib
-from typing import Optional, cast
+from typing import Callable, Optional, cast
 
 from flwr.common.context import Context
+from flwr.common.recordset import RecordSet
 from flwr.server.driver.driver import Driver
 from flwr.server.strategy import Strategy
 
@@ -26,10 +27,32 @@
 from .compat import start_driver
 from .server import Server
 from .server_config import ServerConfig
+from .typing import ServerAppCallable
 
 
 class ServerApp:
-    """Flower ServerApp."""
+    """Flower ServerApp.
+
+    Examples
+    --------
+    Use the `ServerApp` with an existing `Strategy`:
+
+    >>> server_config = ServerConfig(num_rounds=3)
+    >>> strategy = FedAvg()
+    >>>
+    >>> app = ServerApp()
+    >>>     server_config=server_config,
+    >>>     strategy=strategy,
+    >>> )
+
+    Use the `ServerApp` with a custom main function:
+
+    >>> app = ServerApp()
+    >>>
+    >>> @app.main()
+    >>> def main(driver: Driver, context: Context) -> None:
+    >>>    print("ServerApp running")
+    """
 
     def __init__(
         self,
@@ -38,21 +61,74 @@ def __init__(
         strategy: Optional[Strategy] = None,
         client_manager: Optional[ClientManager] = None,
     ) -> None:
-        self.server = server
-        self.config = config
-        self.strategy = strategy
-        self.client_manager = client_manager
+        self._server = server
+        self._config = config
+        self._strategy = strategy
+        self._client_manager = client_manager
+        self._main: Optional[ServerAppCallable] = None
 
     def __call__(self, driver: Driver, context: Context) -> None:
         """Execute `ServerApp`."""
         # Compatibility mode
-        start_driver(
-            server=self.server,
-            config=self.config,
-            strategy=self.strategy,
-            client_manager=self.client_manager,
-            driver=driver,
-        )
+        if not self._main:
+            start_driver(
+                server=self._server,
+                config=self._config,
+                strategy=self._strategy,
+                client_manager=self._client_manager,
+                driver=driver,
+            )
+            return
+
+        # New execution mode
+        context = Context(state=RecordSet())
+        self._main(driver, context)
+
+    def main(self) -> Callable[[ServerAppCallable], ServerAppCallable]:
+        """Return a decorator that registers the main fn with the server app.
+
+        Examples
+        --------
+        >>> app = ServerApp()
+        >>>
+        >>> @app.main()
+        >>> def main(driver: Driver, context: Context) -> None:
+        >>>    print("ServerApp running")
+        """
+
+        def main_decorator(main_fn: ServerAppCallable) -> ServerAppCallable:
+            """Register the main fn with the ServerApp object."""
+            if self._server or self._config or self._strategy or self._client_manager:
+                raise ValueError(
+                    """Use either a custom main function or a `Strategy`, but not both.
+
+                    Use the `ServerApp` with an existing `Strategy`:
+
+                    >>> server_config = ServerConfig(num_rounds=3)
+                    >>> strategy = FedAvg()
+                    >>>
+                    >>> app = ServerApp()
+                    >>>     server_config=server_config,
+                    >>>     strategy=strategy,
+                    >>> )
+
+                    Use the `ServerApp` with a custom main function:
+
+                    >>> app = ServerApp()
+                    >>>
+                    >>> @app.main()
+                    >>> def main(driver: Driver, context: Context) -> None:
+                    >>>    print("ServerApp running")
+                    """,
+                )
+
+            # Register provided function with the ServerApp object
+            self._main = main_fn
+
+            # Return provided function unmodified
+            return main_fn
+
+        return main_decorator
 
 
 class LoadServerAppError(Exception):
diff --git a/src/py/flwr/server/server_app_test.py b/src/py/flwr/server/server_app_test.py
new file mode 100644
index 000000000000..011b22b0d60d
--- /dev/null
+++ b/src/py/flwr/server/server_app_test.py
@@ -0,0 +1,63 @@
+# Copyright 2020 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for ServerApp."""
+
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from flwr.common.context import Context
+from flwr.common.recordset import RecordSet
+from flwr.server import ServerApp, ServerConfig
+from flwr.server.driver import Driver
+
+
+def test_server_app_custom_mode() -> None:
+    """Test sampling w/o criterion."""
+    # Prepare
+    app = ServerApp()
+    driver = MagicMock()
+    context = Context(state=RecordSet())
+
+    called = {"called": False}
+
+    # pylint: disable=unused-argument
+    @app.main()
+    def custom_main(driver: Driver, context: Context) -> None:
+        called["called"] = True
+
+    # pylint: enable=unused-argument
+
+    # Execute
+    app(driver, context)
+
+    # Assert
+    assert called["called"]
+
+
+def test_server_app_exception_when_both_modes() -> None:
+    """Test ServerApp error when both compat mode and custom fns are used."""
+    # Prepare
+    app = ServerApp(config=ServerConfig(num_rounds=3))
+
+    # Execute and assert
+    with pytest.raises(ValueError):
+        # pylint: disable=unused-argument
+        @app.main()
+        def custom_main(driver: Driver, context: Context) -> None:
+            pass
+
+        # pylint: enable=unused-argument

From 155a58f15e324ffd2c399ca9a82f0874071e81b1 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Sun, 18 Feb 2024 17:13:41 +0100
Subject: [PATCH 022/102] Split `run_server_app` (#2974)

---
 src/py/flwr/server/run_serverapp.py | 34 ++++++++++++++++++-----------
 1 file changed, 21 insertions(+), 13 deletions(-)

diff --git a/src/py/flwr/server/run_serverapp.py b/src/py/flwr/server/run_serverapp.py
index 283348f05457..26b8243b1f88 100644
--- a/src/py/flwr/server/run_serverapp.py
+++ b/src/py/flwr/server/run_serverapp.py
@@ -29,6 +29,24 @@
 from .server_app import ServerApp, load_server_app
 
 
+def run(server_app_attr: str, driver: Driver, server_app_dir: str) -> None:
+    """Run ServerApp with a given Driver."""
+    if server_app_dir is not None:
+        sys.path.insert(0, server_app_dir)
+
+    def _load() -> ServerApp:
+        server_app: ServerApp = load_server_app(server_app_attr)
+        return server_app
+
+    server_app = _load()
+
+    # Initialize Context
+    context = Context(state=RecordSet())
+
+    # Call ServerApp
+    server_app(driver=driver, context=context)
+
+
 def run_server_app() -> None:
     """Run Flower server app."""
     event(EventType.RUN_SERVER_APP_ENTER)
@@ -79,17 +97,7 @@ def run_server_app() -> None:
     )
 
     server_app_dir = args.dir
-    if server_app_dir is not None:
-        sys.path.insert(0, server_app_dir)
-
-    def _load() -> ServerApp:
-        server_app: ServerApp = load_server_app(getattr(args, "server-app"))
-        return server_app
-
-    server_app = _load()
-
-    # Initialize Context
-    context = Context(state=RecordSet())
+    server_app_attr = getattr(args, "server-app")
 
     # Initialize Driver
     driver = Driver(
@@ -97,8 +105,8 @@ def _load() -> ServerApp:
         root_certificates=root_certificates,
     )
 
-    # Call ServerApp
-    server_app(driver=driver, context=context)
+    # Run the Server App with the Driver
+    run(server_app_attr, driver, server_app_dir)
 
     # Clean up
     del driver

From 461abc8b9e86cf551cb885bc53c33398b9fc52f8 Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.ai>
Date: Sun, 18 Feb 2024 17:27:47 +0100
Subject: [PATCH 023/102] Export Context, Message and RecordSet from
 flwr.common (#2975)

---
 examples/secaggplus-mt/workflows.py                |  2 +-
 src/py/flwr/client/app.py                          |  3 +--
 src/py/flwr/client/client.py                       |  2 +-
 src/py/flwr/client/clientapp.py                    |  3 +--
 src/py/flwr/client/grpc_client/connection.py       |  4 +---
 src/py/flwr/client/grpc_client/connection_test.py  |  3 +--
 src/py/flwr/client/grpc_rere_client/connection.py  |  3 +--
 .../flwr/client/message_handler/message_handler.py |  4 +---
 .../client/message_handler/message_handler_test.py |  7 ++++---
 .../client/message_handler/task_handler_test.py    |  3 +--
 .../mod/secure_aggregation/secaggplus_mod.py       | 12 ++++++++----
 .../mod/secure_aggregation/secaggplus_mod_test.py  |  4 +---
 src/py/flwr/client/mod/utils.py                    |  3 +--
 src/py/flwr/client/mod/utils_test.py               | 13 ++++++++-----
 src/py/flwr/client/node_state.py                   |  3 +--
 src/py/flwr/client/node_state_tests.py             |  2 +-
 src/py/flwr/client/numpy_client.py                 |  2 +-
 src/py/flwr/client/rest_client/connection.py       |  3 +--
 src/py/flwr/client/typing.py                       |  3 +--
 src/py/flwr/common/__init__.py                     | 14 ++++++++++++++
 src/py/flwr/server/compat/driver_client_proxy.py   |  2 +-
 src/py/flwr/server/run_serverapp.py                |  4 +---
 src/py/flwr/server/server_app.py                   |  3 +--
 src/py/flwr/server/typing.py                       |  2 +-
 src/py/flwr/simulation/ray_transport/ray_actor.py  |  3 +--
 .../simulation/ray_transport/ray_client_proxy.py   |  3 +--
 .../ray_transport/ray_client_proxy_test.py         |  5 +----
 27 files changed, 57 insertions(+), 58 deletions(-)

diff --git a/examples/secaggplus-mt/workflows.py b/examples/secaggplus-mt/workflows.py
index b98de883b8f7..c9d0190555eb 100644
--- a/examples/secaggplus-mt/workflows.py
+++ b/examples/secaggplus-mt/workflows.py
@@ -58,7 +58,7 @@
 from flwr.proto.task_pb2 import Task
 from flwr.common import serde
 from flwr.common.constant import TASK_TYPE_FIT
-from flwr.common.recordset import RecordSet
+from flwr.common import RecordSet
 from flwr.common import recordset_compat as compat
 from flwr.common.configsrecord import ConfigsRecord
 
diff --git a/src/py/flwr/client/app.py b/src/py/flwr/client/app.py
index 15f7c5057a20..86e792d784e7 100644
--- a/src/py/flwr/client/app.py
+++ b/src/py/flwr/client/app.py
@@ -25,7 +25,7 @@
 from flwr.client.client import Client
 from flwr.client.clientapp import ClientApp
 from flwr.client.typing import ClientFn
-from flwr.common import GRPC_MAX_MESSAGE_LENGTH, EventType, event
+from flwr.common import GRPC_MAX_MESSAGE_LENGTH, EventType, Message, event
 from flwr.common.address import parse_address
 from flwr.common.constant import (
     MISSING_EXTRA_REST,
@@ -35,7 +35,6 @@
     TRANSPORT_TYPES,
 )
 from flwr.common.logger import log, warn_deprecated_feature, warn_experimental_feature
-from flwr.common.message import Message
 
 from .clientapp import load_client_app
 from .grpc_client.connection import grpc_connection
diff --git a/src/py/flwr/client/client.py b/src/py/flwr/client/client.py
index 6d982ecc9a9e..23a3755f3efe 100644
--- a/src/py/flwr/client/client.py
+++ b/src/py/flwr/client/client.py
@@ -21,6 +21,7 @@
 
 from flwr.common import (
     Code,
+    Context,
     EvaluateIns,
     EvaluateRes,
     FitIns,
@@ -32,7 +33,6 @@
     Parameters,
     Status,
 )
-from flwr.common.context import Context
 
 
 class Client(ABC):
diff --git a/src/py/flwr/client/clientapp.py b/src/py/flwr/client/clientapp.py
index cfc59c9298ed..9de6516c7a39 100644
--- a/src/py/flwr/client/clientapp.py
+++ b/src/py/flwr/client/clientapp.py
@@ -23,8 +23,7 @@
 )
 from flwr.client.mod.utils import make_ffn
 from flwr.client.typing import ClientFn, Mod
-from flwr.common.context import Context
-from flwr.common.message import Message
+from flwr.common import Context, Message
 
 
 class ClientApp:
diff --git a/src/py/flwr/client/grpc_client/connection.py b/src/py/flwr/client/grpc_client/connection.py
index e6d21963fcbf..e04846985845 100644
--- a/src/py/flwr/client/grpc_client/connection.py
+++ b/src/py/flwr/client/grpc_client/connection.py
@@ -22,7 +22,7 @@
 from queue import Queue
 from typing import Callable, Iterator, Optional, Tuple, Union, cast
 
-from flwr.common import GRPC_MAX_MESSAGE_LENGTH
+from flwr.common import GRPC_MAX_MESSAGE_LENGTH, Message, Metadata, RecordSet
 from flwr.common import recordset_compat as compat
 from flwr.common import serde
 from flwr.common.configsrecord import ConfigsRecord
@@ -34,8 +34,6 @@
 )
 from flwr.common.grpc import create_channel
 from flwr.common.logger import log
-from flwr.common.message import Message, Metadata
-from flwr.common.recordset import RecordSet
 from flwr.proto.transport_pb2 import (  # pylint: disable=E0611
     ClientMessage,
     Reason,
diff --git a/src/py/flwr/client/grpc_client/connection_test.py b/src/py/flwr/client/grpc_client/connection_test.py
index 127e27356f64..4fa289d8bd46 100644
--- a/src/py/flwr/client/grpc_client/connection_test.py
+++ b/src/py/flwr/client/grpc_client/connection_test.py
@@ -23,11 +23,10 @@
 
 import grpc
 
+from flwr.common import Message, Metadata, RecordSet
 from flwr.common import recordset_compat as compat
 from flwr.common.configsrecord import ConfigsRecord
 from flwr.common.constant import MESSAGE_TYPE_GET_PROPERTIES
-from flwr.common.message import Message, Metadata
-from flwr.common.recordset import RecordSet
 from flwr.common.typing import Code, GetPropertiesRes, Status
 from flwr.proto.transport_pb2 import (  # pylint: disable=E0611
     ClientMessage,
diff --git a/src/py/flwr/client/grpc_rere_client/connection.py b/src/py/flwr/client/grpc_rere_client/connection.py
index 07635d002721..04f03299f320 100644
--- a/src/py/flwr/client/grpc_rere_client/connection.py
+++ b/src/py/flwr/client/grpc_rere_client/connection.py
@@ -26,10 +26,9 @@
     validate_task_ins,
     validate_task_res,
 )
-from flwr.common import GRPC_MAX_MESSAGE_LENGTH
+from flwr.common import GRPC_MAX_MESSAGE_LENGTH, Message
 from flwr.common.grpc import create_channel
 from flwr.common.logger import log, warn_experimental_feature
-from flwr.common.message import Message
 from flwr.common.serde import message_from_taskins, message_to_taskres
 from flwr.proto.fleet_pb2 import (  # pylint: disable=E0611
     CreateNodeRequest,
diff --git a/src/py/flwr/client/message_handler/message_handler.py b/src/py/flwr/client/message_handler/message_handler.py
index f8c8a725aec7..c5bc91969291 100644
--- a/src/py/flwr/client/message_handler/message_handler.py
+++ b/src/py/flwr/client/message_handler/message_handler.py
@@ -24,6 +24,7 @@
     maybe_call_get_properties,
 )
 from flwr.client.typing import ClientFn
+from flwr.common import Context, Message, Metadata, RecordSet
 from flwr.common.configsrecord import ConfigsRecord
 from flwr.common.constant import (
     MESSAGE_TYPE_EVALUATE,
@@ -31,9 +32,6 @@
     MESSAGE_TYPE_GET_PARAMETERS,
     MESSAGE_TYPE_GET_PROPERTIES,
 )
-from flwr.common.context import Context
-from flwr.common.message import Message, Metadata
-from flwr.common.recordset import RecordSet
 from flwr.common.recordset_compat import (
     evaluateres_to_recordset,
     fitres_to_recordset,
diff --git a/src/py/flwr/client/message_handler/message_handler_test.py b/src/py/flwr/client/message_handler/message_handler_test.py
index c4c65d98b833..361d301bc8fc 100644
--- a/src/py/flwr/client/message_handler/message_handler_test.py
+++ b/src/py/flwr/client/message_handler/message_handler_test.py
@@ -21,6 +21,7 @@
 from flwr.client.typing import ClientFn
 from flwr.common import (
     Code,
+    Context,
     EvaluateIns,
     EvaluateRes,
     FitIns,
@@ -29,15 +30,15 @@
     GetParametersRes,
     GetPropertiesIns,
     GetPropertiesRes,
+    Message,
+    Metadata,
     Parameters,
+    RecordSet,
     Status,
 )
 from flwr.common import recordset_compat as compat
 from flwr.common import typing
 from flwr.common.constant import MESSAGE_TYPE_GET_PROPERTIES
-from flwr.common.context import Context
-from flwr.common.message import Message, Metadata
-from flwr.common.recordset import RecordSet
 
 from .message_handler import handle_legacy_message_from_msgtype
 
diff --git a/src/py/flwr/client/message_handler/task_handler_test.py b/src/py/flwr/client/message_handler/task_handler_test.py
index 9a668231d509..65ad23630ec2 100644
--- a/src/py/flwr/client/message_handler/task_handler_test.py
+++ b/src/py/flwr/client/message_handler/task_handler_test.py
@@ -20,8 +20,7 @@
     validate_task_ins,
     validate_task_res,
 )
-from flwr.common import serde
-from flwr.common.recordset import RecordSet
+from flwr.common import RecordSet, serde
 from flwr.proto.fleet_pb2 import PullTaskInsResponse  # pylint: disable=E0611
 from flwr.proto.task_pb2 import Task, TaskIns, TaskRes  # pylint: disable=E0611
 
diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
index fa5a9fd24109..ce8aab523787 100644
--- a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
@@ -21,14 +21,18 @@
 from typing import Any, Callable, Dict, List, Tuple, cast
 
 from flwr.client.typing import ClientAppCallable
-from flwr.common import ndarray_to_bytes, parameters_to_ndarrays
+from flwr.common import (
+    Context,
+    Message,
+    Metadata,
+    RecordSet,
+    ndarray_to_bytes,
+    parameters_to_ndarrays,
+)
 from flwr.common import recordset_compat as compat
 from flwr.common.configsrecord import ConfigsRecord
 from flwr.common.constant import MESSAGE_TYPE_FIT
-from flwr.common.context import Context
 from flwr.common.logger import log
-from flwr.common.message import Message, Metadata
-from flwr.common.recordset import RecordSet
 from flwr.common.secure_aggregation.crypto.shamir import create_shares
 from flwr.common.secure_aggregation.crypto.symmetric_encryption import (
     bytes_to_private_key,
diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
index 4033306d0845..760d1a26984c 100644
--- a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
@@ -19,11 +19,9 @@
 from typing import Callable, Dict, List
 
 from flwr.client.mod import make_ffn
+from flwr.common import Context, Message, Metadata, RecordSet
 from flwr.common.configsrecord import ConfigsRecord
 from flwr.common.constant import MESSAGE_TYPE_FIT
-from flwr.common.context import Context
-from flwr.common.message import Message, Metadata
-from flwr.common.recordset import RecordSet
 from flwr.common.secure_aggregation.secaggplus_constants import (
     KEY_ACTIVE_SECURE_ID_LIST,
     KEY_CIPHERTEXT_LIST,
diff --git a/src/py/flwr/client/mod/utils.py b/src/py/flwr/client/mod/utils.py
index 3db5da563c23..4c3c32944f01 100644
--- a/src/py/flwr/client/mod/utils.py
+++ b/src/py/flwr/client/mod/utils.py
@@ -18,8 +18,7 @@
 from typing import List
 
 from flwr.client.typing import ClientAppCallable, Mod
-from flwr.common.context import Context
-from flwr.common.message import Message
+from flwr.common import Context, Message
 
 
 def make_ffn(ffn: ClientAppCallable, mods: List[Mod]) -> ClientAppCallable:
diff --git a/src/py/flwr/client/mod/utils_test.py b/src/py/flwr/client/mod/utils_test.py
index 4a086d9ae3f7..bb3db6e1d6ce 100644
--- a/src/py/flwr/client/mod/utils_test.py
+++ b/src/py/flwr/client/mod/utils_test.py
@@ -19,11 +19,14 @@
 from typing import List
 
 from flwr.client.typing import ClientAppCallable, Mod
-from flwr.common.configsrecord import ConfigsRecord
-from flwr.common.context import Context
-from flwr.common.message import Message, Metadata
-from flwr.common.metricsrecord import MetricsRecord
-from flwr.common.recordset import RecordSet
+from flwr.common import (
+    ConfigsRecord,
+    Context,
+    Message,
+    Metadata,
+    MetricsRecord,
+    RecordSet,
+)
 
 from .utils import make_ffn
 
diff --git a/src/py/flwr/client/node_state.py b/src/py/flwr/client/node_state.py
index 465bbd356c1c..71681b783419 100644
--- a/src/py/flwr/client/node_state.py
+++ b/src/py/flwr/client/node_state.py
@@ -17,8 +17,7 @@
 
 from typing import Any, Dict
 
-from flwr.common.context import Context
-from flwr.common.recordset import RecordSet
+from flwr.common import Context, RecordSet
 
 
 class NodeState:
diff --git a/src/py/flwr/client/node_state_tests.py b/src/py/flwr/client/node_state_tests.py
index 11e5e74a31ec..20885880983f 100644
--- a/src/py/flwr/client/node_state_tests.py
+++ b/src/py/flwr/client/node_state_tests.py
@@ -16,8 +16,8 @@
 
 
 from flwr.client.node_state import NodeState
+from flwr.common import Context
 from flwr.common.configsrecord import ConfigsRecord
-from flwr.common.context import Context
 from flwr.proto.task_pb2 import TaskIns  # pylint: disable=E0611
 
 
diff --git a/src/py/flwr/client/numpy_client.py b/src/py/flwr/client/numpy_client.py
index a77889912a09..0247958d88a9 100644
--- a/src/py/flwr/client/numpy_client.py
+++ b/src/py/flwr/client/numpy_client.py
@@ -21,12 +21,12 @@
 from flwr.client.client import Client
 from flwr.common import (
     Config,
+    Context,
     NDArrays,
     Scalar,
     ndarrays_to_parameters,
     parameters_to_ndarrays,
 )
-from flwr.common.context import Context
 from flwr.common.typing import (
     Code,
     EvaluateIns,
diff --git a/src/py/flwr/client/rest_client/connection.py b/src/py/flwr/client/rest_client/connection.py
index a5c8ea0957d2..cee1529ac285 100644
--- a/src/py/flwr/client/rest_client/connection.py
+++ b/src/py/flwr/client/rest_client/connection.py
@@ -26,10 +26,9 @@
     validate_task_ins,
     validate_task_res,
 )
-from flwr.common import GRPC_MAX_MESSAGE_LENGTH
+from flwr.common import GRPC_MAX_MESSAGE_LENGTH, Message
 from flwr.common.constant import MISSING_EXTRA_REST
 from flwr.common.logger import log
-from flwr.common.message import Message
 from flwr.common.serde import message_from_taskins, message_to_taskres
 from flwr.proto.fleet_pb2 import (  # pylint: disable=E0611
     CreateNodeRequest,
diff --git a/src/py/flwr/client/typing.py b/src/py/flwr/client/typing.py
index 7aef2b30e0fc..956ac7a15c05 100644
--- a/src/py/flwr/client/typing.py
+++ b/src/py/flwr/client/typing.py
@@ -17,8 +17,7 @@
 
 from typing import Callable
 
-from flwr.common.context import Context
-from flwr.common.message import Message
+from flwr.common import Context, Message
 
 from .client import Client as Client
 
diff --git a/src/py/flwr/common/__init__.py b/src/py/flwr/common/__init__.py
index 2f45de45dfc3..3ee5c1b14500 100644
--- a/src/py/flwr/common/__init__.py
+++ b/src/py/flwr/common/__init__.py
@@ -15,14 +15,21 @@
 """Common components shared between server and client."""
 
 
+from .configsrecord import ConfigsRecord as ConfigsRecord
+from .context import Context as Context
 from .date import now as now
 from .grpc import GRPC_MAX_MESSAGE_LENGTH
 from .logger import configure as configure
 from .logger import log as log
+from .message import Message as Message
+from .message import Metadata as Metadata
+from .metricsrecord import MetricsRecord as MetricsRecord
 from .parameter import bytes_to_ndarray as bytes_to_ndarray
 from .parameter import ndarray_to_bytes as ndarray_to_bytes
 from .parameter import ndarrays_to_parameters as ndarrays_to_parameters
 from .parameter import parameters_to_ndarrays as parameters_to_ndarrays
+from .parametersrecord import ParametersRecord as ParametersRecord
+from .recordset import RecordSet as RecordSet
 from .telemetry import EventType as EventType
 from .telemetry import event as event
 from .typing import ClientMessage as ClientMessage
@@ -53,7 +60,9 @@
     "ClientMessage",
     "Code",
     "Config",
+    "ConfigsRecord",
     "configure",
+    "Context",
     "DisconnectRes",
     "EvaluateIns",
     "EvaluateRes",
@@ -67,8 +76,11 @@
     "GetPropertiesRes",
     "GRPC_MAX_MESSAGE_LENGTH",
     "log",
+    "Message",
+    "Metadata",
     "Metrics",
     "MetricsAggregationFn",
+    "MetricsRecord",
     "ndarray_to_bytes",
     "now",
     "NDArray",
@@ -76,8 +88,10 @@
     "ndarrays_to_parameters",
     "Parameters",
     "parameters_to_ndarrays",
+    "ParametersRecord",
     "Properties",
     "ReconnectIns",
+    "RecordSet",
     "Scalar",
     "ServerMessage",
     "Status",
diff --git a/src/py/flwr/server/compat/driver_client_proxy.py b/src/py/flwr/server/compat/driver_client_proxy.py
index 1dc992106f60..a52bd229892d 100644
--- a/src/py/flwr/server/compat/driver_client_proxy.py
+++ b/src/py/flwr/server/compat/driver_client_proxy.py
@@ -19,6 +19,7 @@
 from typing import List, Optional
 
 from flwr import common
+from flwr.common import RecordSet
 from flwr.common import recordset_compat as compat
 from flwr.common import serde
 from flwr.common.constant import (
@@ -27,7 +28,6 @@
     MESSAGE_TYPE_GET_PARAMETERS,
     MESSAGE_TYPE_GET_PROPERTIES,
 )
-from flwr.common.recordset import RecordSet
 from flwr.proto import driver_pb2, node_pb2, task_pb2  # pylint: disable=E0611
 from flwr.server.client_proxy import ClientProxy
 
diff --git a/src/py/flwr/server/run_serverapp.py b/src/py/flwr/server/run_serverapp.py
index 26b8243b1f88..e7205ebd1444 100644
--- a/src/py/flwr/server/run_serverapp.py
+++ b/src/py/flwr/server/run_serverapp.py
@@ -20,10 +20,8 @@
 from logging import DEBUG, WARN
 from pathlib import Path
 
-from flwr.common import EventType, event
-from flwr.common.context import Context
+from flwr.common import Context, EventType, RecordSet, event
 from flwr.common.logger import log
-from flwr.common.recordset import RecordSet
 
 from .driver.driver import Driver
 from .server_app import ServerApp, load_server_app
diff --git a/src/py/flwr/server/server_app.py b/src/py/flwr/server/server_app.py
index 027818019786..1d775878bbd9 100644
--- a/src/py/flwr/server/server_app.py
+++ b/src/py/flwr/server/server_app.py
@@ -18,8 +18,7 @@
 import importlib
 from typing import Callable, Optional, cast
 
-from flwr.common.context import Context
-from flwr.common.recordset import RecordSet
+from flwr.common import Context, RecordSet
 from flwr.server.driver.driver import Driver
 from flwr.server.strategy import Strategy
 
diff --git a/src/py/flwr/server/typing.py b/src/py/flwr/server/typing.py
index 728121c2eddf..dd2463c8e939 100644
--- a/src/py/flwr/server/typing.py
+++ b/src/py/flwr/server/typing.py
@@ -17,7 +17,7 @@
 
 from typing import Callable
 
-from flwr.common.context import Context
+from flwr.common import Context
 from flwr.server.driver import Driver
 
 ServerAppCallable = Callable[[Driver, Context], None]
diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py
index 974773a3f577..70a220dc2a19 100644
--- a/src/py/flwr/simulation/ray_transport/ray_actor.py
+++ b/src/py/flwr/simulation/ray_transport/ray_actor.py
@@ -26,9 +26,8 @@
 from ray.util.actor_pool import ActorPool
 
 from flwr.client.clientapp import ClientApp
-from flwr.common.context import Context
+from flwr.common import Context, Message
 from flwr.common.logger import log
-from flwr.common.message import Message
 
 ClientAppFn = Callable[[], ClientApp]
 
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
index ddac030b2ef0..10fe0f41dd28 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
@@ -23,6 +23,7 @@
 from flwr.client import ClientFn
 from flwr.client.clientapp import ClientApp
 from flwr.client.node_state import NodeState
+from flwr.common import Message, Metadata, RecordSet
 from flwr.common.constant import (
     MESSAGE_TYPE_EVALUATE,
     MESSAGE_TYPE_FIT,
@@ -30,8 +31,6 @@
     MESSAGE_TYPE_GET_PROPERTIES,
 )
 from flwr.common.logger import log
-from flwr.common.message import Message, Metadata
-from flwr.common.recordset import RecordSet
 from flwr.common.recordset_compat import (
     evaluateins_to_recordset,
     fitins_to_recordset,
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
index 9ade31c323d8..5910607e2012 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
@@ -23,12 +23,9 @@
 
 from flwr.client import Client, NumPyClient
 from flwr.client.clientapp import ClientApp
-from flwr.common import Config, Scalar
+from flwr.common import Config, Context, Message, Metadata, RecordSet, Scalar
 from flwr.common.configsrecord import ConfigsRecord
 from flwr.common.constant import MESSAGE_TYPE_GET_PROPERTIES
-from flwr.common.context import Context
-from flwr.common.message import Message, Metadata
-from flwr.common.recordset import RecordSet
 from flwr.common.recordset_compat import (
     getpropertiesins_to_recordset,
     recordset_to_getpropertiesres,

From 1884222dd7f97f421193a36aa9d32254cc27a1be Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.ai>
Date: Sun, 18 Feb 2024 18:01:22 +0100
Subject: [PATCH 024/102] Export Driver from flwr.server (#2973)

---
 src/py/flwr/server/__init__.py           | 5 +++--
 src/py/flwr/server/driver/driver.py      | 3 ++-
 src/py/flwr/server/driver/driver_test.py | 3 ++-
 src/py/flwr/server/server_app.py         | 2 +-
 src/py/flwr/server/typing.py             | 3 ++-
 5 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/src/py/flwr/server/__init__.py b/src/py/flwr/server/__init__.py
index cad054fb86ab..969bea96d1fe 100644
--- a/src/py/flwr/server/__init__.py
+++ b/src/py/flwr/server/__init__.py
@@ -15,7 +15,7 @@
 """Flower server."""
 
 
-from . import driver, strategy
+from . import strategy
 from .app import run_driver_api as run_driver_api
 from .app import run_fleet_api as run_fleet_api
 from .app import run_superlink as run_superlink
@@ -23,6 +23,7 @@
 from .client_manager import ClientManager as ClientManager
 from .client_manager import SimpleClientManager as SimpleClientManager
 from .compat import start_driver as start_driver
+from .driver import Driver as Driver
 from .history import History as History
 from .run_serverapp import run_server_app as run_server_app
 from .server import Server as Server
@@ -31,7 +32,7 @@
 
 __all__ = [
     "ClientManager",
-    "driver",
+    "Driver",
     "History",
     "run_driver_api",
     "run_fleet_api",
diff --git a/src/py/flwr/server/driver/driver.py b/src/py/flwr/server/driver/driver.py
index 0a7cb36f8847..8b8b637e47cb 100644
--- a/src/py/flwr/server/driver/driver.py
+++ b/src/py/flwr/server/driver/driver.py
@@ -25,7 +25,8 @@
 )
 from flwr.proto.node_pb2 import Node  # pylint: disable=E0611
 from flwr.proto.task_pb2 import TaskIns, TaskRes  # pylint: disable=E0611
-from flwr.server.driver.grpc_driver import DEFAULT_SERVER_ADDRESS_DRIVER, GrpcDriver
+
+from .grpc_driver import DEFAULT_SERVER_ADDRESS_DRIVER, GrpcDriver
 
 
 class Driver:
diff --git a/src/py/flwr/server/driver/driver_test.py b/src/py/flwr/server/driver/driver_test.py
index 0ee7fbfec37e..b0d548af53a5 100644
--- a/src/py/flwr/server/driver/driver_test.py
+++ b/src/py/flwr/server/driver/driver_test.py
@@ -24,7 +24,8 @@
     PushTaskInsRequest,
 )
 from flwr.proto.task_pb2 import Task, TaskIns, TaskRes  # pylint: disable=E0611
-from flwr.server.driver.driver import Driver
+
+from .driver import Driver
 
 
 class TestDriver(unittest.TestCase):
diff --git a/src/py/flwr/server/server_app.py b/src/py/flwr/server/server_app.py
index 1d775878bbd9..7b5630b1bad2 100644
--- a/src/py/flwr/server/server_app.py
+++ b/src/py/flwr/server/server_app.py
@@ -19,11 +19,11 @@
 from typing import Callable, Optional, cast
 
 from flwr.common import Context, RecordSet
-from flwr.server.driver.driver import Driver
 from flwr.server.strategy import Strategy
 
 from .client_manager import ClientManager
 from .compat import start_driver
+from .driver import Driver
 from .server import Server
 from .server_config import ServerConfig
 from .typing import ServerAppCallable
diff --git a/src/py/flwr/server/typing.py b/src/py/flwr/server/typing.py
index dd2463c8e939..fa84322bc785 100644
--- a/src/py/flwr/server/typing.py
+++ b/src/py/flwr/server/typing.py
@@ -18,6 +18,7 @@
 from typing import Callable
 
 from flwr.common import Context
-from flwr.server.driver import Driver
+
+from .driver import Driver
 
 ServerAppCallable = Callable[[Driver, Context], None]

From da682a1709185f359bbba68e34df5d86f5077853 Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Sun, 18 Feb 2024 20:45:55 +0000
Subject: [PATCH 025/102] Add new fields to `Metadata` class (#2961)

---
 src/py/flwr/client/grpc_client/connection.py  |   6 +-
 .../client/grpc_client/connection_test.py     |   8 +-
 .../client/grpc_rere_client/connection.py     |  56 +++---
 .../client/message_handler/message_handler.py |  46 +++--
 .../message_handler/message_handler_test.py   | 117 +++++++++++--
 .../client/message_handler/task_handler.py    |  69 +-------
 .../message_handler/task_handler_test.py      |  43 +----
 .../mod/secure_aggregation/secaggplus_mod.py  |  14 +-
 .../secure_aggregation/secaggplus_mod_test.py |  18 +-
 src/py/flwr/client/mod/utils_test.py          |   9 +-
 src/py/flwr/client/rest_client/connection.py  |  50 +++---
 src/py/flwr/common/message.py                 | 164 ++++++++++++++++--
 src/py/flwr/common/serde.py                   |  30 +++-
 src/py/flwr/common/serde_test.py              |  31 +---
 .../ray_transport/ray_client_proxy.py         |   4 +-
 .../ray_transport/ray_client_proxy_test.py    |   4 +-
 16 files changed, 398 insertions(+), 271 deletions(-)

diff --git a/src/py/flwr/client/grpc_client/connection.py b/src/py/flwr/client/grpc_client/connection.py
index e04846985845..c59c33d7ae8e 100644
--- a/src/py/flwr/client/grpc_client/connection.py
+++ b/src/py/flwr/client/grpc_client/connection.py
@@ -169,9 +169,11 @@ def receive() -> Message:
             metadata=Metadata(
                 run_id=0,
                 message_id=str(uuid.uuid4()),
+                src_node_id=0,
+                dst_node_id=0,
+                reply_to_message="",
                 group_id="",
                 ttl="",
-                node_id=0,
                 message_type=message_type,
             ),
             content=recordset,
@@ -205,7 +207,7 @@ def send(message: Message) -> None:
                 disconnect_res=ClientMessage.DisconnectRes(reason=reason)
             )
         else:
-            raise ValueError(f"Invalid task type: {message_type}")
+            raise ValueError(f"Invalid message type: {message_type}")
 
         # Send ClientMessage proto
         return queue.put(msg_proto, block=False)
diff --git a/src/py/flwr/client/grpc_client/connection_test.py b/src/py/flwr/client/grpc_client/connection_test.py
index 4fa289d8bd46..9f0aa3b8980c 100644
--- a/src/py/flwr/client/grpc_client/connection_test.py
+++ b/src/py/flwr/client/grpc_client/connection_test.py
@@ -46,8 +46,10 @@
     metadata=Metadata(
         run_id=0,
         message_id="",
+        src_node_id=0,
+        dst_node_id=0,
+        reply_to_message="",
         group_id="",
-        node_id=0,
         ttl="",
         message_type=MESSAGE_TYPE_GET_PROPERTIES,
     ),
@@ -59,8 +61,10 @@
     metadata=Metadata(
         run_id=0,
         message_id="",
+        src_node_id=0,
+        dst_node_id=0,
+        reply_to_message="",
         group_id="",
-        node_id=0,
         ttl="",
         message_type="reconnect",
     ),
diff --git a/src/py/flwr/client/grpc_rere_client/connection.py b/src/py/flwr/client/grpc_rere_client/connection.py
index 04f03299f320..00b7a864c5d6 100644
--- a/src/py/flwr/client/grpc_rere_client/connection.py
+++ b/src/py/flwr/client/grpc_rere_client/connection.py
@@ -16,19 +16,17 @@
 
 
 from contextlib import contextmanager
+from copy import copy
 from logging import DEBUG, ERROR
 from pathlib import Path
 from typing import Callable, Dict, Iterator, Optional, Tuple, Union, cast
 
-from flwr.client.message_handler.task_handler import (
-    configure_task_res,
-    get_task_ins,
-    validate_task_ins,
-    validate_task_res,
-)
-from flwr.common import GRPC_MAX_MESSAGE_LENGTH, Message
+from flwr.client.message_handler.message_handler import validate_out_message
+from flwr.client.message_handler.task_handler import get_task_ins, validate_task_ins
+from flwr.common import GRPC_MAX_MESSAGE_LENGTH
 from flwr.common.grpc import create_channel
 from flwr.common.logger import log, warn_experimental_feature
+from flwr.common.message import Message, Metadata
 from flwr.common.serde import message_from_taskins, message_to_taskres
 from flwr.proto.fleet_pb2 import (  # pylint: disable=E0611
     CreateNodeRequest,
@@ -41,7 +39,7 @@
 from flwr.proto.task_pb2 import TaskIns  # pylint: disable=E0611
 
 KEY_NODE = "node"
-KEY_TASK_INS = "current_task_ins"
+KEY_METADATA = "in_message_metadata"
 
 
 def on_channel_state_change(channel_connectivity: str) -> None:
@@ -102,8 +100,8 @@ def grpc_request_response(
     channel.subscribe(on_channel_state_change)
     stub = FleetStub(channel)
 
-    # Necessary state to link TaskRes to TaskIns
-    state: Dict[str, Optional[TaskIns]] = {KEY_TASK_INS: None}
+    # Necessary state to validate messages to be sent
+    state: Dict[str, Optional[Metadata]] = {KEY_METADATA: None}
 
     # Enable create_node and delete_node to store node
     node_store: Dict[str, Optional[Node]] = {KEY_NODE: None}
@@ -149,14 +147,20 @@ def receive() -> Optional[Message]:
         task_ins: Optional[TaskIns] = get_task_ins(response)
 
         # Discard the current TaskIns if not valid
-        if task_ins is not None and not validate_task_ins(task_ins):
+        if task_ins is not None and not (
+            task_ins.task.consumer.node_id == node.node_id
+            and validate_task_ins(task_ins)
+        ):
             task_ins = None
 
-        # Remember `task_ins` until `task_res` is available
-        state[KEY_TASK_INS] = task_ins
+        # Construct the Message
+        in_message = message_from_taskins(task_ins) if task_ins else None
+
+        # Remember `metadata` of the in message
+        state[KEY_METADATA] = copy(in_message.metadata) if in_message else None
 
         # Return the message if available
-        return message_from_taskins(task_ins) if task_ins is not None else None
+        return in_message
 
     def send(message: Message) -> None:
         """Send task result back to server."""
@@ -164,30 +168,26 @@ def send(message: Message) -> None:
         if node_store[KEY_NODE] is None:
             log(ERROR, "Node instance missing")
             return
-        node: Node = cast(Node, node_store[KEY_NODE])
 
-        # Get incoming TaskIns
-        if state[KEY_TASK_INS] is None:
-            log(ERROR, "No current TaskIns")
+        # Get incoming message
+        in_metadata = state[KEY_METADATA]
+        if in_metadata is None:
+            log(ERROR, "No current message")
+            return
+
+        # Validate out message
+        if not validate_out_message(message, in_metadata):
+            log(ERROR, "Invalid out message")
             return
-        task_ins: TaskIns = cast(TaskIns, state[KEY_TASK_INS])
 
         # Construct TaskRes
         task_res = message_to_taskres(message)
 
-        # Check if fields to be set are not initialized
-        if not validate_task_res(task_res):
-            state[KEY_TASK_INS] = None
-            log(ERROR, "TaskRes has been initialized accidentally")
-
-        # Configure TaskRes
-        task_res = configure_task_res(task_res, task_ins, node)
-
         # Serialize ProtoBuf to bytes
         request = PushTaskResRequest(task_res_list=[task_res])
         _ = stub.PushTaskRes(request)
 
-        state[KEY_TASK_INS] = None
+        state[KEY_METADATA] = None
 
     try:
         # Yield methods
diff --git a/src/py/flwr/client/message_handler/message_handler.py b/src/py/flwr/client/message_handler/message_handler.py
index c5bc91969291..643128f31061 100644
--- a/src/py/flwr/client/message_handler/message_handler.py
+++ b/src/py/flwr/client/message_handler/message_handler.py
@@ -85,17 +85,7 @@ def handle_control_message(message: Message) -> Tuple[Optional[Message], int]:
         reason = cast(int, disconnect_msg.disconnect_res.reason)
         recordset = RecordSet()
         recordset.set_configs("config", ConfigsRecord({"reason": reason}))
-        out_message = Message(
-            metadata=Metadata(
-                run_id=0,
-                message_id="",
-                group_id="",
-                node_id=0,
-                ttl="",
-                message_type="reconnect",
-            ),
-            content=recordset,
-        )
+        out_message = message.create_reply(recordset, ttl="")
         # Return TaskRes and sleep duration
         return out_message, sleep_duration
 
@@ -107,7 +97,7 @@ def handle_legacy_message_from_msgtype(
     client_fn: ClientFn, message: Message, context: Context
 ) -> Message:
     """Handle legacy message in the inner most mod."""
-    client = client_fn(str(message.metadata.node_id))
+    client = client_fn(str(message.metadata.dst_node_id))
 
     client.set_context(context)
 
@@ -144,21 +134,10 @@ def handle_legacy_message_from_msgtype(
         )
         out_recordset = evaluateres_to_recordset(evaluate_res)
     else:
-        raise ValueError(f"Invalid task type: {message_type}")
+        raise ValueError(f"Invalid message type: {message_type}")
 
     # Return Message
-    out_message = Message(
-        metadata=Metadata(
-            run_id=0,
-            message_id="",
-            group_id="",
-            node_id=0,
-            ttl="",
-            message_type=message_type,
-        ),
-        content=out_recordset,
-    )
-    return out_message
+    return message.create_reply(out_recordset, ttl="")
 
 
 def _reconnect(
@@ -173,3 +152,20 @@ def _reconnect(
     # Build DisconnectRes message
     disconnect_res = ClientMessage.DisconnectRes(reason=reason)
     return ClientMessage(disconnect_res=disconnect_res), sleep_duration
+
+
+def validate_out_message(out_message: Message, in_message_metadata: Metadata) -> bool:
+    """Validate the out message."""
+    out_meta = out_message.metadata
+    in_meta = in_message_metadata
+    if (  # pylint: disable-next=too-many-boolean-expressions
+        out_meta.run_id == in_meta.run_id
+        and out_meta.message_id == ""  # This will be generated by the server
+        and out_meta.src_node_id == in_meta.dst_node_id
+        and out_meta.dst_node_id == in_meta.src_node_id
+        and out_meta.reply_to_message == in_meta.message_id
+        and out_meta.group_id == in_meta.group_id
+        and out_meta.message_type == in_meta.message_type
+    ):
+        return True
+    return False
diff --git a/src/py/flwr/client/message_handler/message_handler_test.py b/src/py/flwr/client/message_handler/message_handler_test.py
index 361d301bc8fc..9fc126f27923 100644
--- a/src/py/flwr/client/message_handler/message_handler_test.py
+++ b/src/py/flwr/client/message_handler/message_handler_test.py
@@ -15,7 +15,10 @@
 """Client-side message handler tests."""
 
 
+import unittest
 import uuid
+from copy import copy
+from typing import List
 
 from flwr.client import Client
 from flwr.client.typing import ClientFn
@@ -40,7 +43,7 @@
 from flwr.common import typing
 from flwr.common.constant import MESSAGE_TYPE_GET_PROPERTIES
 
-from .message_handler import handle_legacy_message_from_msgtype
+from .message_handler import handle_legacy_message_from_msgtype, validate_out_message
 
 
 class ClientWithoutProps(Client):
@@ -122,10 +125,12 @@ def test_client_without_get_properties() -> None:
     recordset = compat.getpropertiesins_to_recordset(GetPropertiesIns({}))
     message = Message(
         metadata=Metadata(
-            run_id=0,
+            run_id=123,
             message_id=str(uuid.uuid4()),
-            group_id="",
-            node_id=0,
+            group_id="some group ID",
+            src_node_id=0,
+            dst_node_id=1123,
+            reply_to_message="",
             ttl="",
             message_type=MESSAGE_TYPE_GET_PROPERTIES,
         ),
@@ -148,10 +153,22 @@ def test_client_without_get_properties() -> None:
         properties={},
     )
     expected_rs = compat.getpropertiesres_to_recordset(expected_get_properties_res)
-    expected_msg = Message(message.metadata, expected_rs)
+    expected_msg = Message(
+        metadata=Metadata(
+            run_id=123,
+            message_id="",
+            group_id="some group ID",
+            src_node_id=1123,
+            dst_node_id=0,
+            reply_to_message=message.metadata.message_id,
+            ttl="",
+            message_type=MESSAGE_TYPE_GET_PROPERTIES,
+        ),
+        content=expected_rs,
+    )
 
     assert actual_msg.content == expected_msg.content
-    assert actual_msg.metadata.message_type == expected_msg.metadata.message_type
+    assert actual_msg.metadata == expected_msg.metadata
 
 
 def test_client_with_get_properties() -> None:
@@ -161,10 +178,12 @@ def test_client_with_get_properties() -> None:
     recordset = compat.getpropertiesins_to_recordset(GetPropertiesIns({}))
     message = Message(
         metadata=Metadata(
-            run_id=0,
+            run_id=123,
             message_id=str(uuid.uuid4()),
-            group_id="",
-            node_id=0,
+            group_id="some group ID",
+            src_node_id=0,
+            dst_node_id=1123,
+            reply_to_message="",
             ttl="",
             message_type=MESSAGE_TYPE_GET_PROPERTIES,
         ),
@@ -187,7 +206,83 @@ def test_client_with_get_properties() -> None:
         properties={"str_prop": "val", "int_prop": 1},
     )
     expected_rs = compat.getpropertiesres_to_recordset(expected_get_properties_res)
-    expected_msg = Message(message.metadata, expected_rs)
+    expected_msg = Message(
+        metadata=Metadata(
+            run_id=123,
+            message_id="",
+            group_id="some group ID",
+            src_node_id=1123,
+            dst_node_id=0,
+            reply_to_message=message.metadata.message_id,
+            ttl="",
+            message_type=MESSAGE_TYPE_GET_PROPERTIES,
+        ),
+        content=expected_rs,
+    )
 
     assert actual_msg.content == expected_msg.content
-    assert actual_msg.metadata.message_type == expected_msg.metadata.message_type
+    assert actual_msg.metadata == expected_msg.metadata
+
+
+class TestMessageValidation(unittest.TestCase):
+    """Test message validation."""
+
+    def setUp(self) -> None:
+        """Set up the message validation."""
+        # Common setup for tests
+        self.in_metadata = Metadata(
+            run_id=123,
+            message_id="qwerty",
+            src_node_id=10,
+            dst_node_id=20,
+            reply_to_message="",
+            group_id="group1",
+            ttl="60",
+            message_type="mock",
+        )
+        self.valid_out_metadata = Metadata(
+            run_id=123,
+            message_id="",
+            src_node_id=20,
+            dst_node_id=10,
+            reply_to_message="qwerty",
+            group_id="group1",
+            ttl="60",
+            message_type="mock",
+        )
+        self.common_content = RecordSet()
+
+    def test_valid_message(self) -> None:
+        """Test a valid message."""
+        # Prepare
+        valid_message = Message(metadata=self.valid_out_metadata, content=RecordSet())
+
+        # Assert
+        self.assertTrue(validate_out_message(valid_message, self.in_metadata))
+
+    def test_invalid_message_run_id(self) -> None:
+        """Test invalid messages."""
+        # Prepare
+        msg = Message(metadata=self.valid_out_metadata, content=RecordSet())
+
+        # Execute
+        invalid_metadata_list: List[Metadata] = []
+        attrs = list(vars(self.valid_out_metadata).keys())
+        for attr in attrs:
+            if attr == "_ttl":  # Skip configurable ttl
+                continue
+            # Make an invalid metadata
+            invalid_metadata = copy(self.valid_out_metadata)
+            value = getattr(invalid_metadata, attr)
+            if isinstance(value, int):
+                value = 999
+            elif isinstance(value, str):
+                value = "999"
+            setattr(invalid_metadata, attr, value)
+            # Add to list
+            invalid_metadata_list.append(invalid_metadata)
+
+        # Assert
+        for invalid_metadata in invalid_metadata_list:
+            msg._metadata = invalid_metadata  # pylint: disable=protected-access
+            self.assertFalse(validate_out_message(msg, self.in_metadata))
diff --git a/src/py/flwr/client/message_handler/task_handler.py b/src/py/flwr/client/message_handler/task_handler.py
index daac1be77138..7f515a30fe5a 100644
--- a/src/py/flwr/client/message_handler/task_handler.py
+++ b/src/py/flwr/client/message_handler/task_handler.py
@@ -18,8 +18,7 @@
 from typing import Optional
 
 from flwr.proto.fleet_pb2 import PullTaskInsResponse  # pylint: disable=E0611
-from flwr.proto.node_pb2 import Node  # pylint: disable=E0611
-from flwr.proto.task_pb2 import Task, TaskIns, TaskRes  # pylint: disable=E0611
+from flwr.proto.task_pb2 import TaskIns  # pylint: disable=E0611
 
 
 def validate_task_ins(task_ins: TaskIns) -> bool:
@@ -41,40 +40,6 @@ def validate_task_ins(task_ins: TaskIns) -> bool:
     return True
 
 
-def validate_task_res(task_res: TaskRes) -> bool:
-    """Validate a TaskRes before filling its fields in the `send()` function.
-
-    Parameters
-    ----------
-    task_res: TaskRes
-        The task response to be sent to the server.
-
-    Returns
-    -------
-    is_valid: bool
-        True if the `task_id`, `group_id`, and `run_id` fields in TaskRes
-        and the `producer`, `consumer`, and `ancestry` fields in its sub-message Task
-        are not initialized accidentally elsewhere,
-        False otherwise.
-    """
-    # Retrieve initialized fields in TaskRes and Task
-    initialized_fields_in_task_res = {field.name for field, _ in task_res.ListFields()}
-    initialized_fields_in_task = {field.name for field, _ in task_res.task.ListFields()}
-
-    # Check if certain fields are already initialized
-    if (  # pylint: disable-next=too-many-boolean-expressions
-        "task_id" in initialized_fields_in_task_res
-        or "group_id" in initialized_fields_in_task_res
-        or "run_id" in initialized_fields_in_task_res
-        or "producer" in initialized_fields_in_task
-        or "consumer" in initialized_fields_in_task
-        or "ancestry" in initialized_fields_in_task
-    ):
-        return False
-
-    return True
-
-
 def get_task_ins(
     pull_task_ins_response: PullTaskInsResponse,
 ) -> Optional[TaskIns]:
@@ -87,35 +52,3 @@ def get_task_ins(
     task_ins: TaskIns = pull_task_ins_response.task_ins_list[0]
 
     return task_ins
-
-
-def configure_task_res(
-    task_res: TaskRes, ref_task_ins: TaskIns, producer: Node
-) -> TaskRes:
-    """Set the metadata of a TaskRes.
-
-    Fill `group_id` and `run_id` in TaskRes
-    and `producer`, `consumer`, and `ancestry` in Task in TaskRes.
-
-    `producer` in Task in TaskRes will remain unchanged/unset.
-
-    Note that protobuf API `protobuf.message.MergeFrom(other_msg)`
-    does NOT always overwrite fields that are set in `other_msg`.
-    Please refer to:
-    https://googleapis.dev/python/protobuf/latest/google/protobuf/message.html
-    """
-    task_res = TaskRes(
-        task_id="",  # This will be generated by the server
-        group_id=ref_task_ins.group_id,
-        run_id=ref_task_ins.run_id,
-        task=task_res.task,
-    )
-    # pylint: disable-next=no-member
-    task_res.task.MergeFrom(
-        Task(
-            producer=producer,
-            consumer=ref_task_ins.task.producer,
-            ancestry=[ref_task_ins.task_id],
-        )
-    )
-    return task_res
diff --git a/src/py/flwr/client/message_handler/task_handler_test.py b/src/py/flwr/client/message_handler/task_handler_test.py
index 65ad23630ec2..fa204198e6b6 100644
--- a/src/py/flwr/client/message_handler/task_handler_test.py
+++ b/src/py/flwr/client/message_handler/task_handler_test.py
@@ -15,14 +15,11 @@
 """Tests for module task_handler."""
 
 
-from flwr.client.message_handler.task_handler import (
-    get_task_ins,
-    validate_task_ins,
-    validate_task_res,
-)
-from flwr.common import RecordSet, serde
+from flwr.client.message_handler.task_handler import get_task_ins, validate_task_ins
+from flwr.common import serde
+from flwr.common.recordset import RecordSet
 from flwr.proto.fleet_pb2 import PullTaskInsResponse  # pylint: disable=E0611
-from flwr.proto.task_pb2 import Task, TaskIns, TaskRes  # pylint: disable=E0611
+from flwr.proto.task_pb2 import Task, TaskIns  # pylint: disable=E0611
 
 
 def test_validate_task_ins_no_task() -> None:
@@ -46,38 +43,6 @@ def test_validate_task_ins_valid() -> None:
     assert validate_task_ins(task_ins)
 
 
-def test_validate_task_res() -> None:
-    """Test validate_task_res."""
-    task_res = TaskRes(task=Task())
-    assert validate_task_res(task_res)
-
-    task_res.task_id = "123"
-    assert not validate_task_res(task_res)
-
-    task_res.Clear()
-    task_res.group_id = "123"
-    assert not validate_task_res(task_res)
-
-    task_res.Clear()
-    task_res.run_id = 61016
-    assert not validate_task_res(task_res)
-
-    task_res.Clear()
-    # pylint: disable-next=no-member
-    task_res.task.producer.node_id = 0
-    assert not validate_task_res(task_res)
-
-    task_res.Clear()
-    # pylint: disable-next=no-member
-    task_res.task.consumer.node_id = 0
-    assert not validate_task_res(task_res)
-
-    task_res.Clear()
-    # pylint: disable-next=no-member
-    task_res.task.ancestry.append("123")
-    assert not validate_task_res(task_res)
-
-
 def test_get_task_ins_empty_response() -> None:
     """Test get_task_ins."""
     res = PullTaskInsResponse(reconnect=None, task_ins_list=[])
diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
index ce8aab523787..68cafb3e9825 100644
--- a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
@@ -24,7 +24,6 @@
 from flwr.common import (
     Context,
     Message,
-    Metadata,
     RecordSet,
     ndarray_to_bytes,
     parameters_to_ndarrays,
@@ -210,17 +209,8 @@ def secaggplus_mod(
     ctxt.state.set_configs(RECORD_KEY_STATE, ConfigsRecord(state.to_dict()))
 
     # Return message
-    return Message(
-        metadata=Metadata(
-            run_id=0,
-            message_id="",
-            group_id="",
-            node_id=0,
-            ttl="",
-            message_type=MESSAGE_TYPE_FIT,
-        ),
-        content=RecordSet(configs={RECORD_KEY_CONFIGS: ConfigsRecord(res, False)}),
-    )
+    content = RecordSet(configs={RECORD_KEY_CONFIGS: ConfigsRecord(res, False)})
+    return msg.create_reply(content, ttl="")
 
 
 def check_stage(current_stage: str, configs: Dict[str, ConfigsRecordValues]) -> None:
diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
index 760d1a26984c..bbb7df1eb7f0 100644
--- a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
@@ -53,18 +53,8 @@ def get_test_handler(
 ) -> Callable[[Dict[str, ConfigsRecordValues]], Dict[str, ConfigsRecordValues]]:
     """."""
 
-    def empty_ffn(_: Message, _2: Context) -> Message:
-        return Message(
-            metadata=Metadata(
-                run_id=0,
-                message_id="",
-                group_id="",
-                node_id=0,
-                ttl="",
-                message_type=MESSAGE_TYPE_FIT,
-            ),
-            content=RecordSet(),
-        )
+    def empty_ffn(_msg: Message, _2: Context) -> Message:
+        return _msg.create_reply(RecordSet(), ttl="")
 
     app = make_ffn(empty_ffn, [secaggplus_mod])
 
@@ -73,8 +63,10 @@ def func(configs: Dict[str, ConfigsRecordValues]) -> Dict[str, ConfigsRecordValu
             metadata=Metadata(
                 run_id=0,
                 message_id="",
+                src_node_id=0,
+                dst_node_id=123,
+                reply_to_message="",
                 group_id="",
-                node_id=0,
                 ttl="",
                 message_type=MESSAGE_TYPE_FIT,
             ),
diff --git a/src/py/flwr/client/mod/utils_test.py b/src/py/flwr/client/mod/utils_test.py
index bb3db6e1d6ce..4915f0710a0f 100644
--- a/src/py/flwr/client/mod/utils_test.py
+++ b/src/py/flwr/client/mod/utils_test.py
@@ -78,7 +78,14 @@ def _get_dummy_flower_message() -> Message:
     return Message(
         content=RecordSet(),
         metadata=Metadata(
-            run_id=0, message_id="", group_id="", node_id=0, ttl="", message_type="mock"
+            run_id=0,
+            message_id="",
+            group_id="",
+            src_node_id=0,
+            dst_node_id=0,
+            reply_to_message="",
+            ttl="",
+            message_type="mock",
         ),
     )
 
diff --git a/src/py/flwr/client/rest_client/connection.py b/src/py/flwr/client/rest_client/connection.py
index cee1529ac285..c637475551ed 100644
--- a/src/py/flwr/client/rest_client/connection.py
+++ b/src/py/flwr/client/rest_client/connection.py
@@ -17,18 +17,16 @@
 
 import sys
 from contextlib import contextmanager
+from copy import copy
 from logging import ERROR, INFO, WARN
 from typing import Callable, Dict, Iterator, Optional, Tuple, Union, cast
 
-from flwr.client.message_handler.task_handler import (
-    configure_task_res,
-    get_task_ins,
-    validate_task_ins,
-    validate_task_res,
-)
-from flwr.common import GRPC_MAX_MESSAGE_LENGTH, Message
+from flwr.client.message_handler.message_handler import validate_out_message
+from flwr.client.message_handler.task_handler import get_task_ins, validate_task_ins
+from flwr.common import GRPC_MAX_MESSAGE_LENGTH
 from flwr.common.constant import MISSING_EXTRA_REST
 from flwr.common.logger import log
+from flwr.common.message import Message, Metadata
 from flwr.common.serde import message_from_taskins, message_to_taskres
 from flwr.proto.fleet_pb2 import (  # pylint: disable=E0611
     CreateNodeRequest,
@@ -49,7 +47,7 @@
 
 
 KEY_NODE = "node"
-KEY_TASK_INS = "current_task_ins"
+KEY_METADATA = "in_message_metadata"
 
 
 PATH_CREATE_NODE: str = "api/v0/fleet/create-node"
@@ -121,8 +119,8 @@ def http_request_response(
             "must be provided as a string path to the client.",
         )
 
-    # Necessary state to link TaskRes to TaskIns
-    state: Dict[str, Optional[TaskIns]] = {KEY_TASK_INS: None}
+    # Necessary state to validate messages to be sent
+    state: Dict[str, Optional[Metadata]] = {KEY_METADATA: None}
 
     # Enable create_node and delete_node to store node
     node_store: Dict[str, Optional[Node]] = {KEY_NODE: None}
@@ -257,16 +255,18 @@ def receive() -> Optional[Message]:
         task_ins: Optional[TaskIns] = get_task_ins(pull_task_ins_response_proto)
 
         # Discard the current TaskIns if not valid
-        if task_ins is not None and not validate_task_ins(task_ins):
+        if task_ins is not None and not (
+            task_ins.task.consumer.node_id == node.node_id
+            and validate_task_ins(task_ins)
+        ):
             task_ins = None
 
-        # Remember `task_ins` until `task_res` is available
-        state[KEY_TASK_INS] = task_ins
-
         # Return the Message if available
         message = None
+        state[KEY_METADATA] = None
         if task_ins is not None:
             message = message_from_taskins(task_ins)
+            state[KEY_METADATA] = copy(message.metadata)
             log(INFO, "[Node] POST /%s: success", PATH_PULL_TASK_INS)
         return message
 
@@ -276,25 +276,21 @@ def send(message: Message) -> None:
         if node_store[KEY_NODE] is None:
             log(ERROR, "Node instance missing")
             return
-        node: Node = cast(Node, node_store[KEY_NODE])
 
-        if state[KEY_TASK_INS] is None:
-            log(ERROR, "No current TaskIns")
+        # Get incoming message
+        in_metadata = state[KEY_METADATA]
+        if in_metadata is None:
+            log(ERROR, "No current message")
             return
 
-        task_ins: TaskIns = cast(TaskIns, state[KEY_TASK_INS])
+        # Validate out message
+        if not validate_out_message(message, in_metadata):
+            log(ERROR, "Invalid out message")
+            return
 
         # Construct TaskRes
         task_res = message_to_taskres(message)
 
-        # Check if fields to be set are not initialized
-        if not validate_task_res(task_res):
-            state[KEY_TASK_INS] = None
-            log(ERROR, "TaskRes has been initialized accidentally")
-
-        # Configure TaskRes
-        task_res = configure_task_res(task_res, task_ins, node)
-
         # Serialize ProtoBuf to bytes
         push_task_res_request_proto = PushTaskResRequest(task_res_list=[task_res])
         push_task_res_request_bytes: bytes = (
@@ -313,7 +309,7 @@ def send(message: Message) -> None:
             timeout=None,
         )
 
-        state[KEY_TASK_INS] = None
+        state[KEY_METADATA] = None
 
         # Check status code and headers
         if res.status_code != 200:
diff --git a/src/py/flwr/common/message.py b/src/py/flwr/common/message.py
index 9258edccbcd5..7735ad85323b 100644
--- a/src/py/flwr/common/message.py
+++ b/src/py/flwr/common/message.py
@@ -15,13 +15,15 @@
 """Message."""
 
 
+from __future__ import annotations
+
 from dataclasses import dataclass
 
 from .recordset import RecordSet
 
 
 @dataclass
-class Metadata:
+class Metadata:  # pylint: disable=too-many-instance-attributes
     """A dataclass holding metadata associated with the current message.
 
     Parameters
@@ -30,11 +32,15 @@ class Metadata:
         An identifier for the current run.
     message_id : str
         An identifier for the current message.
+    src_node_id : int
+        An identifier for the node sending this message.
+    dst_node_id : int
+        An identifier for the node receiving this message.
+    reply_to_message : str
+        An identifier for the message this message replies to.
     group_id : str
-        An identifier for grouping messages. In some settings
+        An identifier for grouping messages. In some settings,
         this is used as the FL round.
-    node_id : int
-        An identifier for the node running a message.
     ttl : str
         Time-to-live for this message.
     message_type : str
@@ -42,12 +48,94 @@ class Metadata:
         the receiving end.
     """
 
-    run_id: int
-    message_id: str
-    group_id: str
-    node_id: int
-    ttl: str
-    message_type: str
+    _run_id: int
+    _message_id: str
+    _src_node_id: int
+    _dst_node_id: int
+    _reply_to_message: str
+    _group_id: str
+    _ttl: str
+    _message_type: str
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        run_id: int,
+        message_id: str,
+        src_node_id: int,
+        dst_node_id: int,
+        reply_to_message: str,
+        group_id: str,
+        ttl: str,
+        message_type: str,
+    ) -> None:
+        self._run_id = run_id
+        self._message_id = message_id
+        self._src_node_id = src_node_id
+        self._dst_node_id = dst_node_id
+        self._reply_to_message = reply_to_message
+        self._group_id = group_id
+        self._ttl = ttl
+        self._message_type = message_type
+
+    @property
+    def run_id(self) -> int:
+        """An identifier for the current run."""
+        return self._run_id
+
+    @property
+    def message_id(self) -> str:
+        """An identifier for the current message."""
+        return self._message_id
+
+    @property
+    def src_node_id(self) -> int:
+        """An identifier for the node sending this message."""
+        return self._src_node_id
+
+    @property
+    def reply_to_message(self) -> str:
+        """An identifier for the message this message replies to."""
+        return self._reply_to_message
+
+    @property
+    def dst_node_id(self) -> int:
+        """An identifier for the node receiving this message."""
+        return self._dst_node_id
+
+    @dst_node_id.setter
+    def dst_node_id(self, value: int) -> None:
+        """Set dst_node_id."""
+        self._dst_node_id = value
+
+    @property
+    def group_id(self) -> str:
+        """An identifier for grouping messages."""
+        return self._group_id
+
+    @group_id.setter
+    def group_id(self, value: str) -> None:
+        """Set group_id."""
+        self._group_id = value
+
+    @property
+    def ttl(self) -> str:
+        """Time-to-live for this message."""
+        return self._ttl
+
+    @ttl.setter
+    def ttl(self, value: str) -> None:
+        """Set ttl."""
+        self._ttl = value
+
+    @property
+    def message_type(self) -> str:
+        """A string that encodes the action to be executed on the receiving end."""
+        return self._message_type
+
+    @message_type.setter
+    def message_type(self, value: str) -> None:
+        """Set message_type."""
+        self._message_type = value
 
 
 @dataclass
@@ -63,5 +151,57 @@ class Message:
         logic to a client, or vice-versa) or that will be sent to it.
     """
 
-    metadata: Metadata
-    content: RecordSet
+    _metadata: Metadata
+    _content: RecordSet
+
+    def __init__(self, metadata: Metadata, content: RecordSet) -> None:
+        self._metadata = metadata
+        self._content = content
+
+    @property
+    def metadata(self) -> Metadata:
+        """A dataclass including information about the message to be executed."""
+        return self._metadata
+
+    @property
+    def content(self) -> RecordSet:
+        """The content of this message."""
+        return self._content
+
+    @content.setter
+    def content(self, value: RecordSet) -> None:
+        """Set content."""
+        self._content = value
+
+    def create_reply(self, content: RecordSet, ttl: str) -> Message:
+        """Create a reply to this message with specified content and TTL.
+
+        The method generates a new `Message` as a reply to this message.
+        It inherits 'run_id', 'src_node_id', 'dst_node_id', and 'message_type' from
+        this message and sets 'reply_to_message' to the ID of this message.
+
+        Parameters
+        ----------
+        content : RecordSet
+            The content for the reply message.
+        ttl : str
+            Time-to-live for this message.
+
+        Returns
+        -------
+        Message
+            A new `Message` instance representing the reply.
+        """
+        return Message(
+            metadata=Metadata(
+                run_id=self.metadata.run_id,
+                message_id="",
+                src_node_id=self.metadata.dst_node_id,
+                dst_node_id=self.metadata.src_node_id,
+                reply_to_message=self.metadata.message_id,
+                group_id=self.metadata.group_id,
+                ttl=ttl,
+                message_type=self.metadata.message_type,
+            ),
+            content=content,
+        )
diff --git a/src/py/flwr/common/serde.py b/src/py/flwr/common/serde.py
index 2808cb88fb5c..530597d89807 100644
--- a/src/py/flwr/common/serde.py
+++ b/src/py/flwr/common/serde.py
@@ -20,6 +20,7 @@
 from google.protobuf.message import Message as GrpcMessage
 
 # pylint: disable=E0611
+from flwr.proto.node_pb2 import Node
 from flwr.proto.recordset_pb2 import Array as ProtoArray
 from flwr.proto.recordset_pb2 import BoolList, BytesList
 from flwr.proto.recordset_pb2 import ConfigsRecord as ProtoConfigsRecord
@@ -547,10 +548,16 @@ def recordset_from_proto(recordset_proto: ProtoRecordSet) -> RecordSet:
 
 def message_to_taskins(message: Message) -> TaskIns:
     """Create a TaskIns from the Message."""
+    md = message.metadata
     return TaskIns(
+        group_id=md.group_id,
+        run_id=md.run_id,
         task=Task(
-            ttl=message.metadata.ttl,
-            task_type=message.metadata.message_type,
+            producer=Node(node_id=0, anonymous=True),  # Assume driver node
+            consumer=Node(node_id=md.dst_node_id, anonymous=False),
+            ttl=md.ttl,
+            ancestry=[md.reply_to_message] if md.reply_to_message != "" else [],
+            task_type=md.message_type,
             recordset=recordset_to_proto(message.content),
         ),
     )
@@ -562,8 +569,10 @@ def message_from_taskins(taskins: TaskIns) -> Message:
     metadata = Metadata(
         run_id=taskins.run_id,
         message_id=taskins.task_id,
+        src_node_id=taskins.task.producer.node_id,
+        dst_node_id=taskins.task.consumer.node_id,
+        reply_to_message=taskins.task.ancestry[0] if taskins.task.ancestry else "",
         group_id=taskins.group_id,
-        node_id=taskins.task.consumer.node_id,
         ttl=taskins.task.ttl,
         message_type=taskins.task.task_type,
     )
@@ -577,10 +586,17 @@ def message_from_taskins(taskins: TaskIns) -> Message:
 
 def message_to_taskres(message: Message) -> TaskRes:
     """Create a TaskRes from the Message."""
+    md = message.metadata
     return TaskRes(
+        task_id="",  # This will be generated by the server
+        group_id=md.group_id,
+        run_id=md.run_id,
         task=Task(
-            ttl=message.metadata.ttl,
-            task_type=message.metadata.message_type,
+            producer=Node(node_id=md.src_node_id, anonymous=False),
+            consumer=Node(node_id=0, anonymous=True),  # Assume driver node
+            ttl=md.ttl,
+            ancestry=[md.reply_to_message] if md.reply_to_message != "" else [],
+            task_type=md.message_type,
             recordset=recordset_to_proto(message.content),
         ),
     )
@@ -592,8 +608,10 @@ def message_from_taskres(taskres: TaskRes) -> Message:
     metadata = Metadata(
         run_id=taskres.run_id,
         message_id=taskres.task_id,
+        src_node_id=taskres.task.producer.node_id,
+        dst_node_id=taskres.task.consumer.node_id,
+        reply_to_message=taskres.task.ancestry[0] if taskres.task.ancestry else "",
         group_id=taskres.group_id,
-        node_id=taskres.task.consumer.node_id,
         ttl=taskres.task.ttl,
         message_type=taskres.task.task_type,
     )
diff --git a/src/py/flwr/common/serde_test.py b/src/py/flwr/common/serde_test.py
index 44085e8d9ab8..9a38d7e5ecee 100644
--- a/src/py/flwr/common/serde_test.py
+++ b/src/py/flwr/common/serde_test.py
@@ -219,7 +219,9 @@ def metadata(self) -> Metadata:
             run_id=self.rng.randint(0, 1 << 30),
             message_id=self.get_str(64),
             group_id=self.get_str(30),
-            node_id=self.rng.randint(0, 1 << 63),
+            src_node_id=self.rng.randint(0, 1 << 63),
+            dst_node_id=self.rng.randint(0, 1 << 63),
+            reply_to_message=self.get_str(64),
             ttl=self.get_str(10),
             message_type=self.get_str(10),
         )
@@ -305,24 +307,16 @@ def test_message_to_and_from_taskins() -> None:
     # Prepare
     maker = RecordMaker(state=1)
     metadata = maker.metadata()
+    # pylint: disable-next=protected-access
+    metadata._src_node_id = 0  # Assume driver node
     original = Message(
-        metadata=Metadata(
-            run_id=0,
-            message_id="",
-            group_id="",
-            node_id=metadata.node_id,
-            ttl=metadata.ttl,
-            message_type=metadata.message_type,
-        ),
+        metadata=metadata,
         content=maker.recordset(1, 1, 1),
     )
 
     # Execute
     taskins = message_to_taskins(original)
-    taskins.run_id = metadata.run_id
     taskins.task_id = metadata.message_id
-    taskins.group_id = metadata.group_id
-    taskins.task.consumer.node_id = metadata.node_id
     deserialized = message_from_taskins(taskins)
 
     # Assert
@@ -335,24 +329,15 @@ def test_message_to_and_from_taskres() -> None:
     # Prepare
     maker = RecordMaker(state=2)
     metadata = maker.metadata()
+    metadata.dst_node_id = 0  # Assume driver node
     original = Message(
-        metadata=Metadata(
-            run_id=0,
-            message_id="",
-            group_id="",
-            node_id=metadata.node_id,
-            ttl=metadata.ttl,
-            message_type=metadata.message_type,
-        ),
+        metadata=metadata,
         content=maker.recordset(1, 1, 1),
     )
 
     # Execute
     taskres = message_to_taskres(original)
-    taskres.run_id = metadata.run_id
     taskres.task_id = metadata.message_id
-    taskres.group_id = metadata.group_id
-    taskres.task.consumer.node_id = metadata.node_id
     deserialized = message_from_taskres(taskres)
 
     # Assert
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
index 10fe0f41dd28..405e0920c5a4 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
@@ -106,7 +106,9 @@ def _wrap_recordset_in_message(
                 run_id=0,
                 message_id="",
                 group_id="",
-                node_id=int(self.cid),
+                src_node_id=0,
+                dst_node_id=int(self.cid),
+                reply_to_message="",
                 ttl=str(timeout) if timeout else "",
                 message_type=message_type,
             ),
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
index 5910607e2012..7cc6de0c7315 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
@@ -190,8 +190,10 @@ def _load_app() -> ClientApp:
                 run_id=0,
                 message_id="",
                 group_id="",
+                src_node_id=0,
+                dst_node_id=int(cid),
+                reply_to_message="",
                 ttl="",
-                node_id=int(cid),
                 message_type=MESSAGE_TYPE_GET_PROPERTIES,
             ),
         )

From 03632ffd5040763f4399024c0621d84d98a9e1be Mon Sep 17 00:00:00 2001
From: "Weblate (bot)" <hosted@weblate.org>
Date: Mon, 19 Feb 2024 12:49:59 +0100
Subject: [PATCH 026/102] Translated using Weblate (Chinese (Simplified))
 (#2978)

---
 .../zh_Hans/LC_MESSAGES/framework-docs.po     | 59 ++++++++++---------
 1 file changed, 31 insertions(+), 28 deletions(-)

diff --git a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
index 720de8578261..ab1c8dc39e64 100644
--- a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
@@ -8,15 +8,16 @@ msgstr ""
 "Project-Id-Version: Flower main\n"
 "Report-Msgid-Bugs-To: \n"
 "POT-Creation-Date: 2024-02-13 11:23+0100\n"
-"PO-Revision-Date: 2024-02-10 11:56+0000\n"
+"PO-Revision-Date: 2024-02-19 11:37+0000\n"
 "Last-Translator: Yan Gao <y.gaogy@gmail.com>\n"
+"Language-Team: Chinese (Simplified) <https://hosted.weblate.org/projects/"
+"flower-docs/framework/zh_Hans/>\n"
 "Language: zh_Hans\n"
-"Language-Team: Chinese (Simplified) <https://hosted.weblate.org/projects"
-"/flower-docs/framework/zh_Hans/>\n"
-"Plural-Forms: nplurals=1; plural=0;\n"
 "MIME-Version: 1.0\n"
 "Content-Type: text/plain; charset=utf-8\n"
 "Content-Transfer-Encoding: 8bit\n"
+"Plural-Forms: nplurals=1; plural=0;\n"
+"X-Generator: Weblate 5.4\n"
 "Generated-By: Babel 2.13.1\n"
 
 #: ../../source/contributor-explanation-architecture.rst:2
@@ -55,7 +56,7 @@ msgstr "具有虚拟客户端引擎和边缘客户端引擎的`Flower <https://f
 
 #: ../../source/contributor-how-to-build-docker-images.rst:2
 msgid "How to build Docker Flower images locally"
-msgstr ""
+msgstr "如何在本地搭建Docker Flower images"
 
 #: ../../source/contributor-how-to-build-docker-images.rst:4
 msgid ""
@@ -1183,7 +1184,7 @@ msgstr "从 ``index.rst`` 链接到新的 rst"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:2
 msgid "Good first contributions"
-msgstr "良好的首批捐款"
+msgstr "首次代码贡献"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:4
 msgid ""
@@ -1192,8 +1193,8 @@ msgid ""
 "where to start to increase your chances of getting your PR accepted into "
 "the Flower codebase."
 msgstr ""
-"我们欢迎为《鲜花》投稿！然而，要知道从哪里开始并非易事。因此，我们提出了一些建议，告诉您从哪里开始，以增加您的 PR 被 Flower "
-"代码库接受的机会。"
+"我们欢迎为Flower做出代码贡献！然而，要知道从哪里开始并非易事。因此，我们提出"
+"了一些建议，告诉您从哪里开始，以增加您的 PR 被 Flower 代码库接受的机会。"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:11
 msgid "Where to start"
@@ -1208,19 +1209,19 @@ msgstr "在 Flower 核心库成熟之前，如果 PR 只涉及代码库中的非
 
 #: ../../source/contributor-ref-good-first-contributions.rst:17
 msgid "Documentation: What's missing? What could be expressed more clearly?"
-msgstr "文件： 缺少什么？哪些内容可以表达得更清楚？"
+msgstr "文档： 缺少什么？哪些内容可以表达得更清楚？"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:18
 msgid "Baselines: See below."
-msgstr "基线： 见下文。"
+msgstr "Baselines： 见下文。"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:19
 msgid "Examples: See below."
-msgstr "举例说明： 见下文。"
+msgstr "示例： 见下文。"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:23
 msgid "Request for Flower Baselines"
-msgstr "Flower 基线申请"
+msgstr "Flower Baselines的申请"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:25
 msgid ""
@@ -1228,8 +1229,8 @@ msgid ""
 "out our `contributing guide for baselines <https://flower.ai/docs"
 "/contributing-baselines.html>`_."
 msgstr ""
-"如果您对 Flower Baselines 还不熟悉，也许应该看看我们的 \"基线贡献指南 <https://flower.ai/docs"
-"/contributing-baselines.html>`_\"。"
+"如果您对 Flower Baselines 还不熟悉，也许可以看看我们的 `Baselines贡献指南 "
+"<https://flower.ai/docs/contributing-baselines.html>`_。"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:27
 msgid ""
@@ -1239,30 +1240,33 @@ msgid ""
 " and that has no assignes, feel free to assign it to yourself and start "
 "working on it!"
 msgstr ""
-"然后，您应该查看开放的 `issues "
-"<https://github.com/adap/flower/issues?q=is%3Aopen+is%3Aissue+label%3A%22new+baseline%22>`_"
-" 基线请求。如果您发现了自己想做的基线，而它还没有被分配，请随时把它分配给自己，然后开始工作！"
+"然后查看开放的 `issues <https://github.com/adap/flower/"
+"issues?q=is%3Aopen+is%3Aissue+label%3A%22new+baseline%22>`_ baseline请求。如"
+"果您发现了自己想做的baseline，而它还没有被分配，请随时把它分配给自己，然后开"
+"始工作！"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:31
 msgid ""
 "Otherwise, if you don't find a baseline you'd like to work on, be sure to"
 " open a new issue with the baseline request template!"
-msgstr "否则，如果您没有找到想要处理的基线，请务必使用基线请求模板打开一个新问题！"
+msgstr "如果您没有找到想要做的baseline，请务必使用baseline请求模板打开一个新问题（"
+"GitHub issue）！"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:34
 msgid "Request for examples"
-msgstr "要求提供范例"
+msgstr "示例请求"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:36
 msgid ""
 "We wish we had more time to write usage examples because we believe they "
 "help users to get started with building what they want to build. Here are"
 " a few ideas where we'd be happy to accept a PR:"
-msgstr "我们希望有更多的时间来撰写使用示例，因为我们相信这些示例可以帮助用户开始构建他们想要构建的东西。以下是我们乐意接受 PR 的几个想法："
+msgstr "我们希望有更多的时间来撰写使用示例，因为我们相信这些示例可以帮助用户开始构建"
+"他们想要的东西。以下是我们乐意接受 PR 的几个想法："
 
 #: ../../source/contributor-ref-good-first-contributions.rst:40
 msgid "Llama 2 fine-tuning, with Hugging Face Transformers and PyTorch"
-msgstr "微调 \"拉玛 2\"，使用 \"抱脸变形金刚 \"和 PyTorch"
+msgstr "微调 Llama 2，使用 Hugging Face Transformers 和 PyTorch"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:41
 msgid "XGBoost"
@@ -1270,7 +1274,7 @@ msgstr "XGBoost"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:42
 msgid "Android ONNX on-device training"
-msgstr "安卓 ONNX 设备上培训"
+msgstr "安卓 ONNX 设备上训练"
 
 #: ../../source/contributor-ref-secure-aggregation-protocols.rst:2
 msgid "Secure Aggregation Protocols"
@@ -1326,16 +1330,16 @@ msgid ""
 msgstr "本指南适用于想参与 Flower，但不习惯为 GitHub 项目贡献的人。"
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:6
-#, fuzzy
 msgid ""
 "If you're familiar with how contributing on GitHub works, you can "
 "directly checkout our `getting started guide for contributors "
 "<https://flower.ai/docs/contributor-tutorial-get-started-"
 "as-a-contributor.html>`_."
 msgstr ""
-"如果您熟悉如何在 GitHub 上贡献，可以直接查看我们的 \"贡献者入门指南\" <https://flower.ai/docs"
-"/getting-started-for-contributors.html>`_ 和 \"优秀的首次贡献示例\" "
-"<https://flower.ai/docs/good-first-contributions.html>`_。"
+"如果您熟悉如何在 GitHub 上贡献，可以直接查看我们的 \"贡献者入门指南\" "
+"<https://flower.ai/docs/getting-started-for-contributors.html>`_ 和 "
+"\"优秀的首次贡献示例\" <https://flower.ai/docs/good-first-contributions."
+"html>`_。"
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:11
 msgid "Setting up the repository"
@@ -1997,7 +2001,7 @@ msgid ""
 "ref-good-first-contributions.html>`_, where you should particularly look "
 "into the :code:`baselines` contributions."
 msgstr ""
-"好的第一批贡献 <https://flower.ai/docs/framework/contributor-ref-good-first-"
+"`优秀的首次贡献 <https://flower.ai/docs/framework/contributor-ref-good-first-"
 "contributions.html>`_，在这里你应该特别看看 :code:`baselines` 的贡献。"
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:350
@@ -20489,4 +20493,3 @@ msgstr ""
 #~ "<ref-api-flwr.html#flwr.client.NumPyClient>`_\" "
 #~ "的类来配置，因此行为方式也完全相同。除此之外，由 :code:`VirtualClientEngine` "
 #~ "管理的客户端还包括："
-

From f4b6e2242a9cd0424c07a0c4b4505c067c955585 Mon Sep 17 00:00:00 2001
From: Robert Steiner <robert@flower.dev>
Date: Mon, 19 Feb 2024 18:24:40 +0100
Subject: [PATCH 027/102] Allow to run multiple baseline tests for collobrators
 (#2966)

---
 .github/workflows/baselines.yml | 166 ++++++++++++--------------------
 1 file changed, 60 insertions(+), 106 deletions(-)

diff --git a/.github/workflows/baselines.yml b/.github/workflows/baselines.yml
index bfb26053836d..c4485fe72d10 100644
--- a/.github/workflows/baselines.yml
+++ b/.github/workflows/baselines.yml
@@ -1,14 +1,5 @@
 name: Baselines
-# The aim of this workflow is to test only the changed (or added) baseline.
-# Here is the rough idea of how it works (more details are presented later in the comments):
-# 1. Checks for the changes between the current branch and the main - in case of PR -
-#  or between the HEAD and HEAD~1 (main last commit and the previous one) - in case of
-#  a push to main.
-# 2. Fails the test if there are changes to more than one baseline. Passes the test
-#  (skips the rests) if there are no changes to any baselines. Follows the test if only
-#  one baseline is added or modified.
-# 3. Sets up the env specified for the baseline.
-# 4. Runs the tests.
+
 on:
   push:
     branches:
@@ -24,112 +15,75 @@ concurrency:
 env:
   FLWR_TELEMETRY_ENABLED: 0
 
-defaults:
-  run:
-    working-directory: baselines
-
 jobs:
-  test_baselines:
-    name: Test
+  changes:
     runs-on: ubuntu-22.04
+    permissions:
+      pull-requests: read
+    outputs:
+      baselines: ${{ steps.filter.outputs.changes }}
     steps:
       - uses: actions/checkout@v4
-        # The depth two of the checkout is needed in case of merging to the main
-        # because we compare the HEAD (current version) with HEAD~1 (version before
-        # the PR was merged)
-        with:
-          fetch-depth: 2
-      - name: Fetch main branch
-        run: |
-          # The main branch is needed in case of the PR to make a comparison (by 
-          # default the workflow takes as little information as possible - it does not
-          # have the history
-          if [ ${{ github.event_name }} == "pull_request" ]
-          then
-            git fetch origin main:main
-          fi
-      - name: Find changed/new baselines
-        id: find_changed_baselines_dirs
+
+      - shell: bash
         run: |
-          if [ ${{ github.event_name }} == "push" ]
-          then
-            # Push event triggered when merging to main
-            change_references="HEAD..HEAD~1"
-          else
-            # Pull request event triggered for any commit to a pull request
-            change_references="main..HEAD"
-          fi
-          dirs=$(git diff --dirstat=files,0 ${change_references} . | awk '{print $2}' | grep -E '^baselines/[^/]*/$' | \
-          grep -v \
-            -e '^baselines/dev' \
-            -e '^baselines/baseline_template' \
-            -e '^baselines/flwr_baselines' \
-            -e '^baselines/doc' \
-          | sed 's/^baselines\///')
-          # git diff --dirstat=files,0 ${change_references} . - checks the differences
-          #   and a file is counted as changed if more than 0 lines were changed
-          #   it returns the results in the format x.y% path/to/dir/
-          # awk '{print $2}' - takes only the directories (skips the percentages)
-          # grep -E '^baselines/[^/]*/$' - takes only the paths that start with 
-          #   baseline (and have at least one subdirectory)
-          # grep -v -e ... - excludes the `baseline_template`, `dev`, `flwr_baselines`
-          # sed 's/^baselines\///' - narrows down the path to baseline/<subdirectory>
-          echo "Detected changed directories: ${dirs}"
-          # Save changed dirs to output of this step
-          EOF=$(dd if=/dev/urandom bs=15 count=1 status=none | base64)
-          echo "dirs<<EOF" >> "$GITHUB_OUTPUT"
-          for dir in $dirs
-          do
-            echo "$dir" >> "$GITHUB_OUTPUT"
-          done
-          echo "EOF" >> "$GITHUB_OUTPUT"
-      - name: Validate changed/new baselines
-        id: validate_changed_baselines_dirs
+          # create a list of all directories in baselines
+          {
+            echo 'FILTER_PATHS<<EOF'
+            FILTER=""
+            while read -d $'\0' BASELINES_PATH; do
+                DIR=$(basename $BASELINES_PATH)
+                FILTER+=$(echo "$DIR: ${BASELINES_PATH}/**\n")
+            done < <(find baselines -maxdepth 1 \
+                -name ".*" -prune -o \
+                -path "baselines/doc" -prune -o \
+                -path "baselines/dev" -prune -o \
+                -path "baselines/baseline_template" -prune -o \
+                -path "baselines/flwr_baselines" -prune -o \
+                -type d -print0)
+            FILTER=$(echo -e "$FILTER")
+            # remove first line
+            FILTER=${FILTER#*$'\n'}
+            echo "$FILTER"
+            echo EOF
+          } >> "$GITHUB_ENV"
+
+      - uses: dorny/paths-filter@v3
+        id: filter
+        with:
+          filters: ${{ env.FILTER_PATHS }}
+
+      - if: ${{ github.event.pull_request.head.repo.fork }}
         run: |
-          dirs="${{ steps.find_changed_baselines_dirs.outputs.dirs }}"
-          dirs_array=()
-          if [[ -n $dirs ]]; then
-            while IFS= read -r line; do
-              dirs_array+=("$line")
-            done <<< "$dirs"
-          fi
-          length=${#dirs_array[@]}
-          echo "The number of changed baselines is $length"
-          
-          if [ $length -gt 1 ]; then
-          echo "The changes should only apply to a single baseline"
-          exit 1
+          CHANGES=$(echo "${{ toJson(steps.filter.outputs.changes) }}" | jq '. | length')
+          if [ "$CHANGES" -gt 1 ]; then
+            echo "::error ::The changes should only apply to a single baseline."
+            exit 1
           fi
-          
-          if [ $length -eq 0 ]; then
-          echo "The baselines were not changed - skipping the remaining steps."
-          echo "baseline_changed=false" >> "$GITHUB_OUTPUT"
-          exit 0
-          fi
-          
-          echo "changed_dir=${dirs[0]}" >> "$GITHUB_OUTPUT"
-          echo "baseline_changed=true" >> "$GITHUB_OUTPUT"
+
+  test:
+    runs-on: ubuntu-22.04
+    needs: changes
+    if: ${{ needs.changes.outputs.baselines != '' && toJson(fromJson(needs.changes.outputs.baselines)) != '[]' }}
+    strategy:
+      matrix:
+        baseline: ${{ fromJSON(needs.changes.outputs.baselines) }}
+    steps:
+      - uses: actions/checkout@v4
+
       - name: Bootstrap
-        if: steps.validate_changed_baselines_dirs.outputs.baseline_changed == 'true'
         uses: ./.github/actions/bootstrap
         with:
           python-version: '3.10'
+
       - name: Install dependencies
-        if: steps.validate_changed_baselines_dirs.outputs.baseline_changed == 'true'
-        run: |
-          changed_dir="${{ steps.validate_changed_baselines_dirs.outputs.changed_dir }}"
-          cd "${changed_dir}"
-          python -m poetry install
-      - name: Test
-        if: steps.validate_changed_baselines_dirs.outputs.baseline_changed == 'true'
-        run: |
-          dir="${{ steps.validate_changed_baselines_dirs.outputs.changed_dir }}"
-          echo "Testing ${dir}"
-          ./dev/test-baseline.sh $dir
-      - name: Test Structure
-        if: steps.validate_changed_baselines_dirs.outputs.baseline_changed == 'true'
-        run: |
-          dir="${{ steps.validate_changed_baselines_dirs.outputs.changed_dir }}"
-          echo "Testing ${dir}"
-          ./dev/test-baseline-structure.sh $dir
+        working-directory: baselines/${{ matrix.baseline }}
+        run: python -m poetry install
+
+      - name: Testing ${{ matrix.baseline }}
+        working-directory: baselines
+        run: ./dev/test-baseline.sh ${{ matrix.baseline }}
 
+      - name: Test Structure of ${{ matrix.baseline }}
+        working-directory: baselines
+        run: ./dev/test-baseline-structure.sh ${{ matrix.baseline }}

From 78cf9721f9170f7743cf92c9182561ba58e04e37 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Mon, 19 Feb 2024 18:44:54 +0100
Subject: [PATCH 028/102] Merges dependencies (#2968)

---
 baselines/fedavgm/pyproject.toml                       | 5 ++---
 baselines/fjord/requirements.txt                       | 3 +--
 examples/whisper-federated-finetuning/requirements.txt | 3 +--
 3 files changed, 4 insertions(+), 7 deletions(-)

diff --git a/baselines/fedavgm/pyproject.toml b/baselines/fedavgm/pyproject.toml
index 298deafd8932..cfd55a5b1fba 100644
--- a/baselines/fedavgm/pyproject.toml
+++ b/baselines/fedavgm/pyproject.toml
@@ -38,11 +38,10 @@ classifiers = [
 
 [tool.poetry.dependencies]
 python = ">=3.9, <3.12.0"  # changed! original baseline template uses >= 3.8.15
-flwr = "1.5.0"
-ray = "2.6.3"
+flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 cython = "^3.0.0"
-tensorflow = "2.10"
+tensorflow = "2.11.1"
 numpy = "1.25.2"
 matplotlib = "^3.7.2"
 
diff --git a/baselines/fjord/requirements.txt b/baselines/fjord/requirements.txt
index 35583b1a45c4..28f700c27537 100644
--- a/baselines/fjord/requirements.txt
+++ b/baselines/fjord/requirements.txt
@@ -1,8 +1,7 @@
 coloredlogs==15.0.1
 hydra-core==1.3.2
-flwr==1.5.0
+flwr[simulation]==1.5.0
 omegaconf==2.3.0
-ray==2.6.3
 torch==2.0.1
 torchvision==0.15.2
 tqdm==4.65.0
diff --git a/examples/whisper-federated-finetuning/requirements.txt b/examples/whisper-federated-finetuning/requirements.txt
index eb4a5d7eb47b..f16b3d6993ce 100644
--- a/examples/whisper-federated-finetuning/requirements.txt
+++ b/examples/whisper-federated-finetuning/requirements.txt
@@ -3,5 +3,4 @@ tokenizers==0.13.3
 datasets==2.14.6
 soundfile==0.12.1
 librosa==0.10.1
-flwr==1.5.0
-ray==2.6.3
\ No newline at end of file
+flwr[simulation]>=1.0, <2.0
\ No newline at end of file

From 2b14a81f97bb7222b8c39cda1e31ba0e6e93c4ed Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Mon, 19 Feb 2024 18:49:14 +0000
Subject: [PATCH 029/102] Migrate `Driver` to use `Message` (#2941)

---
 src/py/flwr/server/driver/driver.py      | 182 ++++++++++++++++++++---
 src/py/flwr/server/driver/driver_test.py |  88 ++++++++---
 2 files changed, 234 insertions(+), 36 deletions(-)

diff --git a/src/py/flwr/server/driver/driver.py b/src/py/flwr/server/driver/driver.py
index 8b8b637e47cb..932ec617d7b3 100644
--- a/src/py/flwr/server/driver/driver.py
+++ b/src/py/flwr/server/driver/driver.py
@@ -15,8 +15,12 @@
 """Flower driver service client."""
 
 
+import time
 from typing import Iterable, List, Optional, Tuple
 
+from flwr.common.message import Message, Metadata
+from flwr.common.recordset import RecordSet
+from flwr.common.serde import message_from_taskres, message_to_taskins
 from flwr.proto.driver_pb2 import (  # pylint: disable=E0611
     CreateRunRequest,
     GetNodesRequest,
@@ -24,7 +28,7 @@
     PushTaskInsRequest,
 )
 from flwr.proto.node_pb2 import Node  # pylint: disable=E0611
-from flwr.proto.task_pb2 import TaskIns, TaskRes  # pylint: disable=E0611
+from flwr.proto.task_pb2 import TaskIns  # pylint: disable=E0611
 
 from .grpc_driver import DEFAULT_SERVER_ADDRESS_DRIVER, GrpcDriver
 
@@ -41,7 +45,6 @@ class Driver:
         Tuple containing root certificate, server certificate, and private key
         to start a secure SSL-enabled server. The tuple is expected to have
         three bytes elements in the following order:
-
             * CA certificate.
             * server certificate.
             * server private key.
@@ -69,44 +72,185 @@ def _get_grpc_driver_and_run_id(self) -> Tuple[GrpcDriver, int]:
             self.grpc_driver.connect()
             res = self.grpc_driver.create_run(CreateRunRequest())
             self.run_id = res.run_id
-
         return self.grpc_driver, self.run_id
 
-    def get_nodes(self) -> List[Node]:
+    def _check_message(self, message: Message) -> None:
+        # Check if the message is valid
+        if not (
+            message.metadata.run_id == self.run_id
+            and message.metadata.src_node_id == self.node.node_id
+            and message.metadata.message_id == ""
+            and message.metadata.reply_to_message == ""
+        ):
+            raise ValueError(f"Invalid message: {message}")
+
+    def create_message(  # pylint: disable=too-many-arguments
+        self,
+        content: RecordSet,
+        message_type: str,
+        dst_node_id: int,
+        group_id: str,
+        ttl: str,
+    ) -> Message:
+        """Create a new message with specified parameters.
+
+        This method constructs a new `Message` with given content and metadata.
+        The `run_id` and `src_node_id` will be set automatically.
+
+        Parameters
+        ----------
+        content : RecordSet
+            The content for the new message. This holds records that are to be sent
+            to the destination node.
+        message_type : str
+            The type of the message, defining the action to be executed on
+            the receiving end.
+        dst_node_id : int
+            The ID of the destination node to which the message is being sent.
+        group_id : str
+            The ID of the group to which this message is associated. In some settings,
+            this is used as the FL round.
+        ttl : str
+            Time-to-live for the round trip of this message, i.e., the time from sending
+            this message to receiving a reply. It specifies the duration for which the
+            message and its potential reply are considered valid.
+
+        Returns
+        -------
+        message : Message
+            A new `Message` instance with the specified content and metadata.
+        """
+        _, run_id = self._get_grpc_driver_and_run_id()
+        metadata = Metadata(
+            run_id=run_id,
+            message_id="",  # Will be set by the server
+            src_node_id=self.node.node_id,
+            dst_node_id=dst_node_id,
+            reply_to_message="",
+            group_id=group_id,
+            ttl=ttl,
+            message_type=message_type,
+        )
+        return Message(metadata=metadata, content=content)
+
+    def get_node_ids(self) -> List[int]:
         """Get node IDs."""
         grpc_driver, run_id = self._get_grpc_driver_and_run_id()
-
         # Call GrpcDriver method
         res = grpc_driver.get_nodes(GetNodesRequest(run_id=run_id))
-        return list(res.nodes)
+        return [node.node_id for node in res.nodes]
 
-    def push_task_ins(self, task_ins_list: List[TaskIns]) -> List[str]:
-        """Schedule tasks."""
-        grpc_driver, run_id = self._get_grpc_driver_and_run_id()
+    def push_messages(self, messages: Iterable[Message]) -> Iterable[str]:
+        """Push messages to specified node IDs.
 
-        # Set run_id
-        for task_ins in task_ins_list:
-            task_ins.run_id = run_id
+        This method takes an iterable of messages and sends each message
+        to the node specified in `dst_node_id`.
 
+        Parameters
+        ----------
+        messages : Iterable[Message]
+            An iterable of messages to be sent.
+
+        Returns
+        -------
+        message_ids : Iterable[str]
+            An iterable of IDs for the messages that were sent, which can be used
+            to pull replies.
+        """
+        grpc_driver, _ = self._get_grpc_driver_and_run_id()
+        # Construct TaskIns
+        task_ins_list: List[TaskIns] = []
+        for msg in messages:
+            # Check message
+            self._check_message(msg)
+            # Convert Message to TaskIns
+            taskins = message_to_taskins(msg)
+            # Add to list
+            task_ins_list.append(taskins)
         # Call GrpcDriver method
         res = grpc_driver.push_task_ins(PushTaskInsRequest(task_ins_list=task_ins_list))
         return list(res.task_ids)
 
-    def pull_task_res(self, task_ids: Iterable[str]) -> List[TaskRes]:
-        """Get task results."""
-        grpc_driver, _ = self._get_grpc_driver_and_run_id()
+    def pull_messages(self, message_ids: Iterable[str]) -> Iterable[Message]:
+        """Pull messages based on message IDs.
 
-        # Call GrpcDriver method
+        This method is used to collect messages from the SuperLink
+        that correspond to a set of given message IDs.
+
+        Parameters
+        ----------
+        message_ids : Iterable[str]
+            An iterable of message IDs for which reply messages are to be retrieved.
+
+        Returns
+        -------
+        messages : Iterable[Message]
+            An iterable of messages received.
+        """
+        grpc_driver, _ = self._get_grpc_driver_and_run_id()
+        # Pull TaskRes
         res = grpc_driver.pull_task_res(
-            PullTaskResRequest(node=self.node, task_ids=task_ids)
+            PullTaskResRequest(node=self.node, task_ids=message_ids)
         )
-        return list(res.task_res_list)
+        # Convert TaskRes to Message
+        msgs = [message_from_taskres(taskres) for taskres in res.task_res_list]
+        return msgs
+
+    def send_and_receive(
+        self,
+        messages: Iterable[Message],
+        *,
+        timeout: Optional[float] = None,
+    ) -> Iterable[Message]:
+        """Push messages to specified node IDs and pull the reply messages.
+
+        This method sends a list of messages to their destination node IDs and then
+        waits for the replies. It continues to pull replies until either all
+        replies are received or the specified timeout duration is exceeded.
+
+        Parameters
+        ----------
+        messages : Iterable[Message]
+            An iterable of messages to be sent.
+        timeout : Optional[float] (default: None)
+            The timeout duration in seconds. If specified, the method will wait for
+            replies for this duration. If `None`, there is no time limit and the method
+            will wait until replies for all messages are received.
+
+        Returns
+        -------
+        replies : Iterable[Message]
+            An iterable of reply messages received from the SuperLink.
+
+        Notes
+        -----
+        This method uses `push_messages` to send the messages and `pull_messages`
+        to collect the replies. If `timeout` is set, the method may not return
+        replies for all sent messages. A message remains valid until its TTL,
+        which is not affected by `timeout`.
+        """
+        # Push messages
+        msg_ids = set(self.push_messages(messages))
+
+        # Pull messages
+        end_time = time.time() + (timeout if timeout is not None else 0.0)
+        ret: List[Message] = []
+        while timeout is None or time.time() < end_time:
+            res_msgs = self.pull_messages(msg_ids)
+            ret.extend(res_msgs)
+            msg_ids.difference_update(
+                {msg.metadata.reply_to_message for msg in res_msgs}
+            )
+            if len(msg_ids) == 0:
+                break
+            # Sleep
+            time.sleep(3)
+        return ret
 
     def __del__(self) -> None:
         """Disconnect GrpcDriver if connected."""
         # Check if GrpcDriver is initialized
         if self.grpc_driver is None:
             return
-
         # Disconnect
         self.grpc_driver.disconnect()
diff --git a/src/py/flwr/server/driver/driver_test.py b/src/py/flwr/server/driver/driver_test.py
index b0d548af53a5..bd5c23a407fd 100644
--- a/src/py/flwr/server/driver/driver_test.py
+++ b/src/py/flwr/server/driver/driver_test.py
@@ -15,15 +15,17 @@
 """Tests for driver SDK."""
 
 
+import time
 import unittest
 from unittest.mock import Mock, patch
 
+from flwr.common import RecordSet
 from flwr.proto.driver_pb2 import (  # pylint: disable=E0611
     GetNodesRequest,
     PullTaskResRequest,
     PushTaskInsRequest,
 )
-from flwr.proto.task_pb2 import Task, TaskIns, TaskRes  # pylint: disable=E0611
+from flwr.proto.task_pb2 import Task, TaskRes  # pylint: disable=E0611
 
 from .driver import Driver
 
@@ -74,11 +76,11 @@ def test_get_nodes(self) -> None:
         """Test retrieval of nodes."""
         # Prepare
         mock_response = Mock()
-        mock_response.nodes = [Mock(), Mock()]
+        mock_response.nodes = [Mock(node_id=404), Mock(node_id=200)]
         self.mock_grpc_driver.get_nodes.return_value = mock_response
 
         # Execute
-        nodes = self.driver.get_nodes()
+        node_ids = self.driver.get_node_ids()
         args, kwargs = self.mock_grpc_driver.get_nodes.call_args
 
         # Assert
@@ -87,18 +89,19 @@ def test_get_nodes(self) -> None:
         self.assertEqual(len(kwargs), 0)
         self.assertIsInstance(args[0], GetNodesRequest)
         self.assertEqual(args[0].run_id, 61016)
-        self.assertEqual(nodes, mock_response.nodes)
+        self.assertEqual(node_ids, [404, 200])
 
-    def test_push_task_ins(self) -> None:
-        """Test pushing task instructions."""
+    def test_push_messages_valid(self) -> None:
+        """Test pushing valid messages."""
         # Prepare
-        mock_response = Mock()
-        mock_response.task_ids = ["id1", "id2"]
+        mock_response = Mock(task_ids=["id1", "id2"])
         self.mock_grpc_driver.push_task_ins.return_value = mock_response
-        task_ins_list = [TaskIns(), TaskIns()]
+        msgs = [
+            self.driver.create_message(RecordSet(), "", 0, "", "") for _ in range(2)
+        ]
 
         # Execute
-        task_ids = self.driver.push_task_ins(task_ins_list)
+        msg_ids = self.driver.push_messages(msgs)
         args, kwargs = self.mock_grpc_driver.push_task_ins.call_args
 
         # Assert
@@ -106,12 +109,27 @@ def test_push_task_ins(self) -> None:
         self.assertEqual(len(args), 1)
         self.assertEqual(len(kwargs), 0)
         self.assertIsInstance(args[0], PushTaskInsRequest)
-        self.assertEqual(task_ids, mock_response.task_ids)
+        self.assertEqual(msg_ids, mock_response.task_ids)
         for task_ins in args[0].task_ins_list:
             self.assertEqual(task_ins.run_id, 61016)
 
-    def test_pull_task_res_with_given_task_ids(self) -> None:
-        """Test pulling task results with specific task IDs."""
+    def test_push_messages_invalid(self) -> None:
+        """Test pushing invalid messages."""
+        # Prepare
+        mock_response = Mock(task_ids=["id1", "id2"])
+        self.mock_grpc_driver.push_task_ins.return_value = mock_response
+        msgs = [
+            self.driver.create_message(RecordSet(), "", 0, "", "") for _ in range(2)
+        ]
+        # Use invalid run_id
+        msgs[1].metadata._run_id += 1  # pylint: disable=protected-access
+
+        # Execute and assert
+        with self.assertRaises(ValueError):
+            self.driver.push_messages(msgs)
+
+    def test_pull_messages_with_given_message_ids(self) -> None:
+        """Test pulling messages with specific message IDs."""
         # Prepare
         mock_response = Mock()
         mock_response.task_res_list = [
@@ -119,10 +137,11 @@ def test_pull_task_res_with_given_task_ids(self) -> None:
             TaskRes(task=Task(ancestry=["id3"])),
         ]
         self.mock_grpc_driver.pull_task_res.return_value = mock_response
-        task_ids = ["id1", "id2", "id3"]
+        msg_ids = ["id1", "id2", "id3"]
 
         # Execute
-        task_res_list = self.driver.pull_task_res(task_ids)
+        msgs = self.driver.pull_messages(msg_ids)
+        reply_tos = {msg.metadata.reply_to_message for msg in msgs}
         args, kwargs = self.mock_grpc_driver.pull_task_res.call_args
 
         # Assert
@@ -130,8 +149,43 @@ def test_pull_task_res_with_given_task_ids(self) -> None:
         self.assertEqual(len(args), 1)
         self.assertEqual(len(kwargs), 0)
         self.assertIsInstance(args[0], PullTaskResRequest)
-        self.assertEqual(args[0].task_ids, task_ids)
-        self.assertEqual(task_res_list, mock_response.task_res_list)
+        self.assertEqual(args[0].task_ids, msg_ids)
+        self.assertEqual(reply_tos, {"id2", "id3"})
+
+    def test_send_and_receive_messages_complete(self) -> None:
+        """Test send and receive all messages successfully."""
+        # Prepare
+        mock_response = Mock(task_ids=["id1"])
+        self.mock_grpc_driver.push_task_ins.return_value = mock_response
+        mock_response = Mock(task_res_list=[TaskRes(task=Task(ancestry=["id1"]))])
+        self.mock_grpc_driver.pull_task_res.return_value = mock_response
+        msgs = [self.driver.create_message(RecordSet(), "", 0, "", "")]
+
+        # Execute
+        ret_msgs = list(self.driver.send_and_receive(msgs))
+
+        # Assert
+        self.assertEqual(len(ret_msgs), 1)
+        self.assertEqual(ret_msgs[0].metadata.reply_to_message, "id1")
+
+    def test_send_and_receive_messages_timeout(self) -> None:
+        """Test send and receive messages but time out."""
+        # Prepare
+        sleep_fn = time.sleep
+        mock_response = Mock(task_ids=["id1"])
+        self.mock_grpc_driver.push_task_ins.return_value = mock_response
+        mock_response = Mock(task_res_list=[])
+        self.mock_grpc_driver.pull_task_res.return_value = mock_response
+        msgs = [self.driver.create_message(RecordSet(), "", 0, "", "")]
+
+        # Execute
+        with patch("time.sleep", side_effect=lambda t: sleep_fn(t * 0.01)):
+            start_time = time.time()
+            ret_msgs = list(self.driver.send_and_receive(msgs, timeout=0.15))
+
+        # Assert
+        self.assertLess(time.time() - start_time, 0.2)
+        self.assertEqual(len(ret_msgs), 0)
 
     def test_del_with_initialized_driver(self) -> None:
         """Test cleanup behavior when Driver is initialized."""

From 4d96a398f33067afc22f0b6b1b583ddb67eedde3 Mon Sep 17 00:00:00 2001
From: Raj Parekh <rajparekh144@gmail.com>
Date: Mon, 19 Feb 2024 13:32:54 -0800
Subject: [PATCH 030/102] FedStar (#2482)

Co-authored-by: jafermarq <javier@flower.dev>
---
 README.md                                     |   1 +
 baselines/fedstar/.gitignore                  |   4 +
 baselines/fedstar/LICENSE                     | 202 ++++++
 baselines/fedstar/README.md                   | 163 +++++
 baselines/fedstar/data_splits.tar             | Bin 0 -> 13105152 bytes
 baselines/fedstar/fedstar/.gitattributes      |   1 +
 baselines/fedstar/fedstar/.gitignore          |   3 +
 baselines/fedstar/fedstar/__init__.py         |   1 +
 baselines/fedstar/fedstar/client.py           | 154 ++++
 baselines/fedstar/fedstar/clients.py          | 238 +++++++
 baselines/fedstar/fedstar/conf/L/L20.yaml     |   3 +
 baselines/fedstar/fedstar/conf/L/L3.yaml      |   3 +
 baselines/fedstar/fedstar/conf/L/L5.yaml      |   3 +
 baselines/fedstar/fedstar/conf/L/L50.yaml     |   3 +
 baselines/fedstar/fedstar/conf/table3.yaml    |  39 ++
 baselines/fedstar/fedstar/conf/table4.yaml    |  40 ++
 baselines/fedstar/fedstar/dataset.py          | 506 ++++++++++++++
 .../fedstar/fedstar/dataset_preparation.py    |  43 ++
 baselines/fedstar/fedstar/main.py             |   1 +
 baselines/fedstar/fedstar/models.py           | 427 +++++++++++
 baselines/fedstar/fedstar/server.py           | 291 ++++++++
 baselines/fedstar/fedstar/strategy.py         |   5 +
 baselines/fedstar/fedstar/utils.py            | 660 ++++++++++++++++++
 baselines/fedstar/pyproject.toml              | 141 ++++
 baselines/fedstar/setup_datasets.sh           |  42 ++
 25 files changed, 2974 insertions(+)
 create mode 100644 baselines/fedstar/.gitignore
 create mode 100644 baselines/fedstar/LICENSE
 create mode 100644 baselines/fedstar/README.md
 create mode 100644 baselines/fedstar/data_splits.tar
 create mode 100644 baselines/fedstar/fedstar/.gitattributes
 create mode 100644 baselines/fedstar/fedstar/.gitignore
 create mode 100644 baselines/fedstar/fedstar/__init__.py
 create mode 100644 baselines/fedstar/fedstar/client.py
 create mode 100644 baselines/fedstar/fedstar/clients.py
 create mode 100644 baselines/fedstar/fedstar/conf/L/L20.yaml
 create mode 100644 baselines/fedstar/fedstar/conf/L/L3.yaml
 create mode 100644 baselines/fedstar/fedstar/conf/L/L5.yaml
 create mode 100644 baselines/fedstar/fedstar/conf/L/L50.yaml
 create mode 100644 baselines/fedstar/fedstar/conf/table3.yaml
 create mode 100644 baselines/fedstar/fedstar/conf/table4.yaml
 create mode 100644 baselines/fedstar/fedstar/dataset.py
 create mode 100644 baselines/fedstar/fedstar/dataset_preparation.py
 create mode 100644 baselines/fedstar/fedstar/main.py
 create mode 100644 baselines/fedstar/fedstar/models.py
 create mode 100644 baselines/fedstar/fedstar/server.py
 create mode 100644 baselines/fedstar/fedstar/strategy.py
 create mode 100644 baselines/fedstar/fedstar/utils.py
 create mode 100644 baselines/fedstar/pyproject.toml
 create mode 100644 baselines/fedstar/setup_datasets.sh

diff --git a/README.md b/README.md
index 38a11d951fe7..00e0a277104a 100644
--- a/README.md
+++ b/README.md
@@ -101,6 +101,7 @@ Flower Baselines is a collection of community-contributed projects that reproduc
 - [FedNova](https://github.com/adap/flower/tree/main/baselines/fednova)
 - [HeteroFL](https://github.com/adap/flower/tree/main/baselines/heterofl)
 - [FedAvgM](https://github.com/adap/flower/tree/main/baselines/fedavgm)
+- [FedStar](https://github.com/adap/flower/tree/main/baselines/fedstar)
 - [FedWav2vec2](https://github.com/adap/flower/tree/main/baselines/fedwav2vec2)
 - [FjORD](https://github.com/adap/flower/tree/main/baselines/fjord)
 - [MOON](https://github.com/adap/flower/tree/main/baselines/moon)
diff --git a/baselines/fedstar/.gitignore b/baselines/fedstar/.gitignore
new file mode 100644
index 000000000000..e39a6de1f73d
--- /dev/null
+++ b/baselines/fedstar/.gitignore
@@ -0,0 +1,4 @@
+datasets
+outputs
+data_splits
+multirun
diff --git a/baselines/fedstar/LICENSE b/baselines/fedstar/LICENSE
new file mode 100644
index 000000000000..d64569567334
--- /dev/null
+++ b/baselines/fedstar/LICENSE
@@ -0,0 +1,202 @@
+
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diff --git a/baselines/fedstar/README.md b/baselines/fedstar/README.md
new file mode 100644
index 000000000000..9005ae01679d
--- /dev/null
+++ b/baselines/fedstar/README.md
@@ -0,0 +1,163 @@
+---
+title: Federated Self-training for Semi-supervised Audio Recognition
+url: https://dl.acm.org/doi/10.1145/3520128
+labels: [Audio Classification, Semi Supervised learning]
+dataset: [Ambient Context, Speech Commands]
+---
+
+# FedStar: Federated Self-training for Semi-supervised Audio Recognition
+
+> Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper.
+
+**Paper:**  [dl.acm.org/doi/10.1145/3520128](https://dl.acm.org/doi/10.1145/3520128)
+
+**Authors:** Vasileios Tsouvalas, Aaqib Saeed, Tanir Özcelebi
+
+**Abstract:** Federated Learning is a distributed machine learning paradigm dealing with decentralized and personal datasets. Since data reside on devices such as smartphones and virtual assistants, labeling is entrusted to the clients or labels are extracted in an automated way. Specifically, in the case of audio data, acquiring semantic annotations can be prohibitively expensive and time-consuming. As a result, an abundance of audio data remains unlabeled and unexploited on users’ devices. Most existing federated learning approaches focus on supervised learning without harnessing the unlabeled data. In this work, we study the problem of semi-supervised learning of audio models via self-training in conjunction with federated learning. We propose FedSTAR to exploit large-scale on-device unlabeled data to improve the generalization of audio recognition models. We further demonstrate that self-supervised pre-trained models can accelerate the training of on-device models, significantly improving convergence within fewer training rounds. We conduct experiments on diverse public audio classification datasets and investigate the performance of our models under varying percentages of labeled and unlabeled data. Notably, we show that with as little as 3% labeled data available, FedSTAR on average can improve the recognition rate by 13.28% compared to the fully supervised federated model.
+
+
+## About this baseline
+
+**What’s implemented:** The code is structured in such a way that all experiments for ambient context and speech commands can be derived.
+
+**Datasets:** Ambient Context, Speech Commands
+
+**Hardware Setup:** These experiments were run on a linux server with 56 CPU threads with 325 GB Ram with A10 GPU in it. Any machine with 16 CPU cores and 32 GB memory would be able to run experiments with small number of clients in a reasonable amount of time. For context, a machine with 24 cores and a RTX3090Ti ran the Speech Commands experiment in Table 3 with 10 clients in 1h. For this experiment 30GB of RAM was used and clients required ~1.4GB of VRAM each. The same experiment but with the Ambient Context dataset too 13minutes.
+
+**Contributors:** Raj Parekh [GitHub](https://github.com/Raj-Parekh24), [Mail](rajparekhwc@gmail.com)
+
+## Environment Setup
+```bash
+# Set python version
+pyenv local 3.10.6
+# Tell poetry to use python 3.10
+poetry env use 3.10.6
+# Now install the environment
+poetry install
+# Start the shell to activate your environment.
+poetry shell
+```
+
+Next, you'll need to download the datasets. In the case of SpeechCommands, some preprocessing is also required:
+
+```bash
+# Make the shell script executable
+chmod +x setup_datasets.sh
+
+# The below script will download the datasets and create a directory structure requir to run this experiment.
+./setup_datasets.sh
+
+# If you want to run the SpeechCommands experiment, pre-process the dataset
+# This will genereate a few training example from the _silence_ category
+python -m fedstar.dataset_preparation
+# Please note the above will make following changes:
+#    * Add new files to datasets/speech_commands/Data/Train/_silence_
+#    * Add new entries to data_splits/speech_commands/train_split.txt
+# Therefore the above command should only be run once. If you want to run it again
+# after making modifications to the script, please either revert the changes outlined
+# above or erase the dataset and repeat the download + preprocessing as defined in setup_datasets.sh script.
+```
+
+## Setting up GPU Memory
+
+**Note:** The experiment is designed to run on both GPU and CPU, but runs better on a system with GPU (specially when using the SpeechCommands dataset). If you wish to use GPU, make sure you have installed the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). This baseline has been tested with CUDA 12.3. By default, it will run only on the CPU. Please update the value of the list `gpu_total_mem` with the corresponding memory for each GPU in your machine that you want to expose to the experiment. The variable is in the `distribute_gpus` function inside the `clients.py`. Reference is shown below.
+
+```python
+# For Eg:- We have a system with two GPUs with 8GB and 4GB VRAM.
+#          The modified varaible will looks like below.
+gpu_free_mem = [8000,4000]
+```
+
+
+
+## Running the Experiments
+
+By default, the `Ambient Context` experiment in Table 3 with 10 clients will be run.
+
+```bash
+python -m fedstar.server
+python -m fedstar.clients
+```
+
+You can change the dataset, number of clients and number of rounds like this:
+
+```bash
+python -m fedstar.server num_clients=5 dataset_name=speech_commands server.rounds=20
+python -m fedstar.clients num_clients=5 dataset_name=speech_commands
+```
+
+To run experiments for Table 4, you should pass a different config file (i.e. that in `fedstar/conf/table4.yaml`). You can do this as follows:
+
+```bash
+# by default will run FedStar with Ambient Context and L=3%
+python -m fedstar.server --config-name table4
+python -m fedstar.clients --config-name table4
+```
+
+To modify the ratio of labelled data do so as follows:
+```bash
+# To use a different L setting
+python -m fedstar.server --config-name table4 L=L5 # {L3, L5, L20, L50}
+# same for fedstar.clients
+```
+
+To run in supervised mode, pass `fedstar=false` to any of the commands above (when launching both the server and clients). Naturally, you can also override any other setting, like `dataset_name` and `num_clients` if desired.
+
+
+## Expected Results
+
+
+This section indicates the commands to execute to obtain the results shown below in Table 3 and Table 4. While both configs fix the number of rounds to 100, in many settings fewer rounds are enough for the model to reach the accuracy shown in the tables. The commands below make use of Hydra's `--multirun` to run multiple experiments. This is better suited when using Flower simulations. Here they work fine but, if you encounter any issues, you can always "unroll" the multirun and run one configuration at a time. If you do this, results won't go into the `multirun/` directory, instead to the default `outputs/` directory.
+
+
+### Table 3
+
+Results will be stored in `multirun/Table3/<dataset_name>/N_<num_clients>/<date>/<time>`. Please note since we are running two Hydra processes, both server and client will generate a log and therefore respective subdirectories in `multirun/`. This is a small compromise of not using Flower simulation. 
+
+```bash
+# For Ambient Context
+python -m fedstar.server --multirun num_clients=5,10,15,30
+python -m fedstar.clients --multirun num_clients=5,10,15,30
+
+# For SpeechCommands
+python -m fedstar.server --multirun num_clients=5,10,15,30 dataset_name=speech_commands
+python -m fedstar.clients --multirun num_clients=5,10,15,30 dataset_name=speech_commands
+```
+
+| Clients | Speech Commands |                | Ambient Context |                |
+|---------|-----------------|----------------|-----------------|----------------|
+|         | Actual          | Implementation | Actual          | Implementation |
+| N=5     | 96.93           | 97.15          | 71.88           | 72.60          |
+| N=10    | 96.78           | 96.42          | 68.01           | 68.43          |
+| N=15    | 96.33           | 96.43          | 66.86           | 66.28          |
+| N=30    | 94.62           | 95.37          | 65.14           | 59.45          |
+
+
+### Table 4
+
+Following the logic presented for obtaining Table 3 results, the larger Table 4 set of results can be obtained by running the `--multirun` commands shown below.
+
+```bash
+# Generate supervised results for Ambient Context (note this will run 4x4=16 experiments)
+python -m fedstar.server --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50 fedstar=false
+python -m fedstar.clients --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50 fedstar=false
+
+# Generate supervised results for Speech Commands (note this will run 4x4=16 experiments)
+python -m fedstar.server --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50 dataset_name=speech_commands fedstar=false
+python -m fedstar.clients --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50 dataset_name=speech_commands fedstar=false
+
+# Generate FedStar results for Ambient Context
+python -m fedstar.server --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50
+python -m fedstar.clients --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50
+
+# Generate FedStar results for Speech Commands
+python -m fedstar.server --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50 dataset_name=speech_commands
+python -m fedstar.clients --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50 dataset_name=speech_commands
+```
+
+
+
+|Dataset|Clients|SupervisedFederatedLearning<br>L=3&#8195;&#8195;L=5&#8195;&#8195;L=20&#8195;&#8195;L=50&#8195;&#8195;L=100|FedStar<br>L=3&#8195;&#8195;L=5&#8195;&#8195;L=20&#8195;&#8195;L=50|
+|:--:|:--:|:---:|:---:|
+Ambient<br></p>Context|5<br>10<br>15<br>30|43.75&#8195;&#8195;45.17&#8195;&#8195;63.40&#8195;&#8195;67.57&#8195;75.27<br>41.75&#8195;&#8195;44.90&#8195;&#8195;56.23&#8195;&#8195;60.82&#8195;70.39<br>43.18&#8195;&#8195;42.75&#8195;&#8195;49.60&#8195;&#8195;59.47&#8195;68.41<br>36.44&#8195;&#8195;38.73&#8195;&#8195;48.91&#8195;&#8195;55.83&#8195;67.23|49.60&#8195;&#8195;52.78&#8195;&#8195;66.12&#8195;&#8195;66.71<br>47.84&#8195;&#8195;52.48&#8195;&#8195;61.98&#8195;&#8195;63.46<br>49.05&#8195;&#8195;56.05&#8195;&#8195;64.25&#8195;&#8195;64.05<br>47.34&#8195;&#8195;46.51&#8195;&#8195;60.45&#8195;&#8195;55.47|
+Speech<br></p>Commands|5<br>10<br>15<br>30|80.83&#8195;&#8195;87.97&#8195;&#8195;92.35&#8195;&#8195;94.66&#8195;95.87<br>54.66&#8195;&#8195;78.90&#8195;&#8195;92.13&#8195;&#8195;93.71&#8195;96.55<br>53.44&#8195;&#8195;64.53&#8195;&#8195;91.83&#8195;&#8195;94.23&#8195;96.35<br>36.41&#8195;&#8195;42.87&#8195;&#8195;83.68&#8195;&#8195;93.18&#8195;94.24|87.39&#8195;&#8195;90.11&#8195;&#8195;94.09&#8195;&#8195;94.85<br>87.15&#8195;&#8195;91.06&#8195;&#8195;94.74&#8195;&#8195;96.15<br>86.78&#8195;&#8195;91.01&#8195;&#8195;95.21&#8195;&#8195;95.70<br>80.89&#8195;&#8195;85.62&#8195;&#8195;94.08&#8195;&#8195;94.18|
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HcmV?d00001

diff --git a/baselines/fedstar/fedstar/.gitattributes b/baselines/fedstar/fedstar/.gitattributes
new file mode 100644
index 000000000000..dc87e624e315
--- /dev/null
+++ b/baselines/fedstar/fedstar/.gitattributes
@@ -0,0 +1 @@
+setup_project.sh text eol=lf
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/.gitignore b/baselines/fedstar/fedstar/.gitignore
new file mode 100644
index 000000000000..a844c7eb581e
--- /dev/null
+++ b/baselines/fedstar/fedstar/.gitignore
@@ -0,0 +1,3 @@
+datasets
+outputs
+data_splits
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/__init__.py b/baselines/fedstar/fedstar/__init__.py
new file mode 100644
index 000000000000..02a650fb44fa
--- /dev/null
+++ b/baselines/fedstar/fedstar/__init__.py
@@ -0,0 +1 @@
+"""Template baseline package."""
diff --git a/baselines/fedstar/fedstar/client.py b/baselines/fedstar/fedstar/client.py
new file mode 100644
index 000000000000..e126d94933fe
--- /dev/null
+++ b/baselines/fedstar/fedstar/client.py
@@ -0,0 +1,154 @@
+"""Required imports for client.py script."""
+import gc
+import logging
+import os
+from typing import Dict, List
+
+import flwr
+import tensorflow as tf
+
+from fedstar.dataset import DataBuilder
+from fedstar.models import Network, PslNetwork
+
+os.environ["GRPC_VERBOSITY"] = "ERROR"
+
+LOG_LEVEL = logging.ERROR
+
+
+class AudioClient(
+    flwr.client.NumPyClient
+):  # pylint: disable=too-many-instance-attributes
+    """AudioClient class act as a client for FedStar implementation.
+
+    It extends NumpyClient feature because flower internally requires a NumpyClient.
+    """
+
+    # pylint: disable=too-many-arguments, too-many-locals, unexpected-keyword-arg
+    def __init__(
+        self,
+        client_id,
+        num_clients,
+        dataset_dir,
+        fedstar,
+        parent_path,
+        variance=0.25,
+        batch_size=64,
+        learning_rate=0.001,
+        l_per=0.2,
+        u_per=1.0,
+        class_distribute: bool = False,
+        balance_dataset: bool = False,
+        mean_class_distribution=3,
+        seed=2021,
+        verbose=2,
+    ):
+        # Client Parameters
+        self.client_id = client_id
+        self.batch_size = batch_size
+        self.verbose = verbose
+        self.fedstar = fedstar
+        self.aux_loss_weight = 0.5
+        self.parent_path = parent_path
+        # Load Clients Data
+        (
+            self.train_labelled,
+            self.train_unlabelled,
+            self.num_classes,
+            self.num_batches,
+        ) = DataBuilder.load_sharded_dataset(
+            parent_path=self.parent_path,
+            data_dir=dataset_dir,
+            num_clients=num_clients,
+            client=client_id,
+            variance=variance,
+            batch_size=batch_size,
+            l_per=l_per,
+            u_per=u_per,
+            fedstar=fedstar,
+            class_distribute=class_distribute,
+            mean_class_distribution=mean_class_distribution,
+            balance_dataset=balance_dataset,
+            seed=seed,
+        )
+        self.num_examples_train = (
+            self.num_batches * batch_size if self.train_labelled else 0
+        )
+        # Local Variables Initialize
+        self.local_train_round = 0
+        self.local_evaluate_round = 0
+        self.weights = Network.get_init_weights(num_classes=self.num_classes)
+        self.history: Dict[str, List[float]] = {"loss": [], "accuracy": []}
+
+        if self.fedstar:
+            self.model = PslNetwork(
+                num_classes=self.num_classes, aux_loss_weight=self.aux_loss_weight
+            )
+            self.model.compile(optimizer=tf.keras.optimizers.Adam(float(learning_rate)))
+        else:
+            self.model = Network(num_classes=self.num_classes).get_network()
+            self.model.compile(
+                optimizer=tf.keras.optimizers.Adam(float(learning_rate)),
+                loss=tf.keras.losses.SparseCategoricalCrossentropy(
+                    from_logits=True, name="loss"
+                ),
+                metrics=[tf.keras.metrics.SparseCategoricalAccuracy(name="accuracy")],
+            )  # type: ignore
+
+        # tf.keras.backend.clear_session()
+
+    def get_parameters(self, config):
+        """Return the current client model weights."""
+        return self.weights
+
+    # pylint: disable=no-value-for-parameter
+    def fit(self, parameters, config):
+        """Update the current client model weights."""
+        self.local_train_round += 1
+        # Run Training Proccess
+        if self.fedstar:
+            self.model.set_weights(parameters)
+            history = self.model.fit(
+                (self.train_labelled, self.train_unlabelled),
+                num_batches=self.num_batches,
+                epochs=int(config["epochs"]),
+                c_round=int(config["c_round"]),
+                rounds=int(config["rounds"]),
+                verbose=self.verbose,
+            )
+        else:
+            self.model.set_weights(parameters)
+            history = self.model.fit(
+                self.train_labelled,
+                batch_size=int(config["batch_size"]),
+                epochs=int(config["epochs"]),
+                verbose=self.verbose,
+            )
+        # Print Results
+        print(
+            f"""Client {self.client_id}
+            finished {self.local_train_round}th round of training with
+            loss {history.history['loss'][0]:.4f} and
+            accuracy {history.history['accuracy'][0]:.4f}"""
+        )
+        # Clear Memory
+        # tf.keras.backend.clear_session()
+        gc.collect()
+        return (
+            self.model.get_weights(),
+            self.num_examples_train,
+            {"local_train_round": self.local_train_round},
+        )
+
+    def evaluate(self, parameters, config):
+        """Essential function for Numpy Client but not required for FedStar."""
+        raise NotImplementedError("Client Side evaluation not required.")
+
+
+def set_logger_level():
+    """Set the logging level for the 'flower' logger."""
+    if "flower" in [
+        repr(logging.getLogger(name))[8:].split(" ")[0]
+        for name in logging.Logger.manager.loggerDict
+    ]:
+        logger = logging.getLogger("flower")
+        logger.setLevel(LOG_LEVEL)
diff --git a/baselines/fedstar/fedstar/clients.py b/baselines/fedstar/fedstar/clients.py
new file mode 100644
index 000000000000..55ca213e86f1
--- /dev/null
+++ b/baselines/fedstar/fedstar/clients.py
@@ -0,0 +1,238 @@
+"""Required imports for clients.py script."""
+import multiprocessing
+import os
+import sys
+from multiprocessing import Process
+
+import flwr as fl
+import hydra
+import tensorflow as tf
+from omegaconf import OmegaConf
+
+from fedstar.client import AudioClient, set_logger_level
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+os.environ["GRPC_VERBOSITY"] = "ERROR"
+
+multiprocessing.set_start_method("spawn", force=True)
+
+parent_path = os.getcwd()
+
+
+class Client(Process):
+    """Client class acts as handler for client class.
+
+    It manages the client creation and also manages resource allocation
+    """
+
+    def __init__(self, queue):
+        Process.__init__(self)
+        self.queue = queue
+
+    def run(self):
+        """Run client processes from a configuration queue.
+
+        Continuously retrieves configurations from a queue and initiates an AudioClient
+        for each. Configures GPU settings, creates and starts the client. The loop ends
+        when a `None` configuration is encountered, indicating no more tasks.
+
+        Each client is started with settings from the config, including client ID,
+        server address, dataset directory, and other parameters.
+        The method marks each task as done upon client completion.
+
+        Assumes the existence of `queue`, `AudioClient`, and `Client.setup_gpu`.
+        Designed for threading or multiprocessing.
+        """
+        while True:
+            cfg = self.queue.get()
+            if cfg is None:
+                self.queue.task_done()
+                break
+            # Configure GPU
+            Client.setup_gpu(
+                gpu=cfg["gpu_id"],
+                gpu_memory=cfg["gpu_memory"],
+                client_id=cfg["client_id"],
+            )
+            process_path = (os.sep).join(parent_path.split(os.sep)) + os.sep
+            # Create Client
+            client = AudioClient(
+                client_id=cfg["client_id"],
+                num_clients=cfg["num_clients"],
+                dataset_dir=cfg["dataset_dir"],
+                parent_path=process_path,
+                l_per=cfg["l_per"],
+                u_per=cfg["u_per"],
+                batch_size=cfg["batch_size"],
+                learning_rate=cfg["learning_rate"],
+                verbose=cfg["verbose"],
+                seed=cfg["seed"],
+                fedstar=cfg["fedstar"],
+                class_distribute=cfg["class_distribute"],
+                balance_dataset=cfg["balance_dataset"],
+            )
+            # Start Client
+            try:
+                if bool(cfg["verbose"]):
+                    print(
+                        f"""This is client {client.client_id}
+                        with train dataset of {client.num_examples_train}
+                        elements."""
+                    )
+                set_logger_level()
+                fl.client.start_numpy_client(
+                    server_address=cfg["server_address"], client=client
+                )
+                print("Client Shutdown.")
+            except RuntimeError as runtime_error:
+                print(runtime_error)
+            # Return job done once client is terminated.
+            self.queue.task_done()
+
+    @staticmethod
+    def setup_gpu(gpu, gpu_memory, client_id):
+        """Configure GPU settings for TensorFlow.
+
+        Sets the environment variable for CUDA_VISIBLE_DEVICES based on the provided
+        GPU identifier. If the GPU is specified and available, it configures TensorFlow
+        to limit the GPU memory usage to the specified amount. If no GPUs are available,
+        or if 'gpu' is None, TensorFlow will default to CPU usage.
+
+        Parameters
+        ----------
+        - gpu (str): Identifier for the GPU to be used. Set to None for CPU usage.
+        - gpu_memory (int): The maximum amount of memory (in MB) to be
+                            allocated to the GPU.
+
+        Note:
+        - This method is specific to TensorFlow's handling of GPU resources.
+        - It should be called before initializing any TensorFlow models or operations.
+        """
+        os.environ["CUDA_VISIBLE_DEVICES"] = gpu if gpu is not None else ""
+
+        gpus = tf.config.list_physical_devices("GPU")
+        if not gpus:
+            print(f"No GPU's available. Client {client_id} will run on CPU.")
+        else:
+            try:
+                tf.config.experimental.set_virtual_device_configuration(
+                    gpus[0],
+                    [
+                        tf.config.experimental.VirtualDeviceConfiguration(
+                            memory_limit=gpu_memory
+                        )
+                    ],
+                )
+            except RuntimeError as runtime_error:
+                print(runtime_error)
+
+
+# pylint: disable=unsubscriptable-object
+def distribute_gpus(num_clients, client_memory=1024):
+    """Distribute GPU resources among multiple clients.
+
+    Parameters
+    ----------
+    - num_clients (int): Number of clients to distribute GPUs among.
+    - client_memory (int): The amount of memory (in MB) to allocate per client.
+
+    Returns
+    -------
+    - List of GPU IDs or None for each client, based on memory availability.
+    """
+    gpus = tf.config.experimental.list_physical_devices("GPU")
+    if not gpus:
+        # Scenario 1: No GPUs available, return None for all clients
+        return [None] * num_clients
+
+    # Manually specify the memory you want to expose in each GPU
+    gpu_total_mem = [4000]  # Adjust based on your GPUs
+
+    if gpus and len(gpu_total_mem) == 0:
+        print("If you want to enable GPU training, please set `gpu_total_mem`")
+
+    gpu_free_mem = gpu_total_mem.copy()
+    clients_gpu = [None] * num_clients
+
+    for client_id in range(num_clients):
+        suitable_gpus = [
+            i for i, mem in enumerate(gpu_free_mem) if mem >= client_memory
+        ]
+        if suitable_gpus:
+            # Scenario 2: Allocate GPU if memory is available
+            chosen_gpu = min(
+                suitable_gpus, key=lambda i: gpu_total_mem[i] - gpu_free_mem[i]
+            )
+            gpu_free_mem[chosen_gpu] -= client_memory
+            gpu_id = gpus[chosen_gpu].name.split(":")[-1]
+            clients_gpu[client_id] = gpu_id
+        else:
+            # Scenario 3: No sufficient memory in GPUs, allocate CPU (None)
+            clients_gpu[client_id] = None
+    print("*" * 50)
+    print(clients_gpu)
+    return clients_gpu
+
+
+# pylint: disable=expression-not-assigned
+@hydra.main(config_path="conf", config_name="table3", version_base=None)
+def main(cfg):
+    """Initialize and run a multi-processing client setup using Hydra configuration.
+
+    Loads the configuration using Hydra, distributes GPUs among clients based on
+    the configuration, and sets up data for each client. The function then creates
+    and starts a pool of `Client` objects, each in its own process, and feeds them
+    client-specific configurations. It waits for all clients to complete their tasks.
+
+    Parameters
+    ----------
+    - cfg: The configuration object provided by Hydra, containing settings for
+           the number of clients, server address, dataset information, GPU memory,
+           and other client-related configurations.
+
+    The configuration (`cfg`) is expected to follow a specific structure as defined
+    in the Hydra configuration files under 'conf/table3'.
+    """
+    print(OmegaConf.to_yaml(cfg))
+
+    clients_gpu = distribute_gpus(
+        num_clients=cfg.client.num_clients,
+        client_memory=cfg.client.gpu_memory,
+    )
+    dataset_name = cfg.client.dataset_name
+
+    # Load Configurations of Clients
+    clients_data = [
+        {
+            "client_id": i,
+            "num_clients": cfg.client.num_clients,
+            "server_address": cfg.client.server_address,
+            "dataset_dir": dataset_name,
+            "batch_size": cfg.client.batch_size,
+            "learning_rate": cfg.client.learning_rate,
+            "gpu_id": clients_gpu[i],
+            "gpu_memory": cfg.client.gpu_memory,
+            "seed": cfg.client.seed,
+            "l_per": cfg.client.l_per,
+            "u_per": cfg.client.u_per,
+            "fedstar": cfg.client.fedstar,
+            "class_distribute": cfg.client.class_distribute,
+            "balance_dataset": cfg.dataset_name == "speech_commands",
+            "verbose": cfg.client.verbose,
+        }
+        for i in range(cfg.client.num_clients)
+    ]
+
+    # Start Multi-processing Clients and wait for them to finish
+    clients_queue = multiprocessing.JoinableQueue()
+    clients = [Client(clients_queue) for i in range(cfg.client.num_clients)]
+    [client.start() for client in clients]
+    [clients_queue.put(client) for client in clients_data]
+    [clients_queue.put(None) for i in range(cfg.client.num_clients)]
+    clients_queue.join()
+
+
+if __name__ == "__main__":
+    multiprocessing.freeze_support()
+    main()
+    sys.exit(0)
diff --git a/baselines/fedstar/fedstar/conf/L/L20.yaml b/baselines/fedstar/fedstar/conf/L/L20.yaml
new file mode 100644
index 000000000000..9eca9821d444
--- /dev/null
+++ b/baselines/fedstar/fedstar/conf/L/L20.yaml
@@ -0,0 +1,3 @@
+---
+l_per: 0.2
+u_per: 0.8
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/conf/L/L3.yaml b/baselines/fedstar/fedstar/conf/L/L3.yaml
new file mode 100644
index 000000000000..76c0be488f71
--- /dev/null
+++ b/baselines/fedstar/fedstar/conf/L/L3.yaml
@@ -0,0 +1,3 @@
+---
+l_per: 0.03
+u_per: 0.97
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/conf/L/L5.yaml b/baselines/fedstar/fedstar/conf/L/L5.yaml
new file mode 100644
index 000000000000..3ea87ab02553
--- /dev/null
+++ b/baselines/fedstar/fedstar/conf/L/L5.yaml
@@ -0,0 +1,3 @@
+---
+l_per: 0.05
+u_per: 0.95
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/conf/L/L50.yaml b/baselines/fedstar/fedstar/conf/L/L50.yaml
new file mode 100644
index 000000000000..dd61476bb1ca
--- /dev/null
+++ b/baselines/fedstar/fedstar/conf/L/L50.yaml
@@ -0,0 +1,3 @@
+---
+l_per: 0.5
+u_per: 0.5
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/conf/table3.yaml b/baselines/fedstar/fedstar/conf/table3.yaml
new file mode 100644
index 000000000000..198e91e7d6a8
--- /dev/null
+++ b/baselines/fedstar/fedstar/conf/table3.yaml
@@ -0,0 +1,39 @@
+---
+# this is the config that will be loaded as default by main.py
+# Please follow the provided structure (this will ensuring all baseline follow
+# a similar configuration structure and hence be easy to customise)
+
+dataset_name: ambient_context # or speech_commands
+num_clients: 10
+
+server:
+  server_address: "localhost:8080"
+  rounds: 100
+  num_clients: ${num_clients}
+  fraction_fit: 0.8
+  gpu_memory: 2048
+  eval_step: 1
+  train_epochs: 1
+  batch_size: 64
+  dataset_name: ${dataset_name}
+  seed: 2021
+  verbose: 1
+
+client:
+  server_address: "localhost:8080"
+  num_clients: ${num_clients}
+  gpu_memory: 1024
+  dataset_name: ${dataset_name}
+  seed: 2021
+  batch_size: 64
+  learning_rate: 0.001
+  l_per: 1.00
+  u_per: 0.00
+  fedstar: false
+  class_distribute: false
+  verbose: 0
+
+hydra:
+  sweep:
+    dir: multirun
+    subdir: Table3/${dataset_name}/N_${num_clients}/${now:%Y-%m-%d}/${now:%H-%M-%S}
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/conf/table4.yaml b/baselines/fedstar/fedstar/conf/table4.yaml
new file mode 100644
index 000000000000..05f87666debb
--- /dev/null
+++ b/baselines/fedstar/fedstar/conf/table4.yaml
@@ -0,0 +1,40 @@
+
+dataset_name: ambient_context # or speech_commands
+num_clients: 5 # this also fixes min_sample_size (both values are the same always)
+fedstar: true # set false for supervised
+
+defaults:
+  - _self_
+  - L: L3
+
+server:
+  server_address: "localhost:8080"
+  rounds: 100
+  num_clients: ${num_clients}
+  fraction_fit: 0.8
+  gpu_memory: 2048
+  eval_step: 1
+  train_epochs: 1
+  batch_size: 64
+  dataset_name: ${dataset_name}
+  seed: 2021
+  verbose: 1
+
+client:
+  server_address: "localhost:8080"
+  num_clients: ${num_clients}
+  gpu_memory: 1024
+  dataset_name: ${dataset_name}
+  seed: 2021
+  batch_size: 64
+  learning_rate: 0.001
+  l_per: ${L.l_per}
+  u_per: ${L.u_per}
+  fedstar: ${fedstar}
+  class_distribute: false
+  verbose: 0
+
+hydra:
+  sweep:
+    dir: multirun
+    subdir: Table4/${dataset_name}/fedstar_${fedstar}/N_${num_clients}/L_${L}/${now:%Y-%m-%d}/${now:%H-%M-%S}
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/dataset.py b/baselines/fedstar/fedstar/dataset.py
new file mode 100644
index 000000000000..e376a6b318f0
--- /dev/null
+++ b/baselines/fedstar/fedstar/dataset.py
@@ -0,0 +1,506 @@
+"""Required imports for data.py script."""
+import os
+
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+
+from fedstar.utils import AudioTools, DataTools
+
+
+def ambient_context_path_extracter(path):
+    """Extract and construct a file path for ambient context from a given path string.
+
+    This function takes a path string, splits it at underscores,
+    removes the last segment, and then reconstructs the path by
+    re-joining the segments and appending the original
+    path at the end, separated by the OS-specific directory separator.
+
+    Parameters
+    ----------
+    - path (str): The original path string to be processed.
+
+    Returns
+    -------
+    - str: The reconstructed file path suitable for ambient context usage.
+
+    Example:
+    Given a path string 'dir_subdir_file_xyz', the function will return
+    'dir_subdir/dir_subdir_file_xyz'.
+    """
+    arr = path.split("_")[:-1]
+    file_path = "_".join(arr) + os.sep + path
+    return file_path
+
+
+class DataBuilder:
+    """A utility class for building and processing datasets for audio analysis.
+
+    This class provides methods to load, split, and prepare datasets for training
+    and testing in audio-related machine learning tasks. It supports operations like
+    loading data from files, splitting datasets into labelled and unlabelled sets,
+    balancing datasets, and converting them into TensorFlow dataset objects.
+
+    Class Attributes:
+    - AUTOTUNE: TensorFlow's setting for optimized parallel data loading.
+    - WORDS: List of specific words used in the audio processing context.
+
+    Methods are designed to work with specific data structures and assume a certain
+    directory and file naming convention.
+    """
+
+    AUTOTUNE = tf.data.experimental.AUTOTUNE
+    WORDS = [
+        "down",
+        "go",
+        "left",
+        "no",
+        "off",
+        "on",
+        "right",
+        "stop",
+        "up",
+        "yes",
+        "_silence_",
+    ]
+
+    # pylint: disable=too-many-arguments, too-many-locals, too-many-statements
+    @staticmethod
+    def get_files(parent_path, data_dir, train=False, raw=False):
+        """Load audio file paths and labels from specified directories.
+
+        Reads training or testing data file paths and their corresponding labels from
+        given directories. Supports different dataset types like 'speech_commands' and
+        'ambient_context'. Can return raw file paths and labels or a TensorFlow dataset.
+
+        Parameters
+        ----------
+        - parent_path (str): Base path for the dataset directories.
+        - data_dir (str): Specific directory name containing the dataset.
+        - train (bool): If True, load training data; otherwise, load testing data.
+        - raw (bool): If True, return raw file paths and labels; otherwise, return a
+        TensorFlow dataset.
+
+        Returns
+        -------
+        - A TensorFlow dataset or a tuple of file paths and labels,
+        along with the number of classes in the dataset.
+        """
+        path = os.path.join(parent_path, "data_splits", data_dir)
+        train_path = os.path.join(path, "train_split.txt")
+        test_path = os.path.join(path, "test_split.txt")
+        path_data_dir = os.path.join(parent_path, "datasets", data_dir)
+        if train:
+            train_files_path, train_labels = [], []
+            if data_dir == "speech_commands":
+                print("Dataset is speech_commands")
+                train_user_files = pd.read_csv(train_path, header=None).values.flatten()
+                for tr_uf in train_user_files:
+                    path_tr_uf = os.path.join(*tr_uf.split("/"))
+                    train_files_path.append(
+                        os.path.join(path_data_dir, "Data", "Train", path_tr_uf)
+                    )
+                    label = tr_uf.split("/")[0]
+                    if label in DataBuilder.WORDS:
+                        train_labels.append(tr_uf.split("/")[0])
+                    else:
+                        train_labels.append("_unknown_")
+            elif data_dir == "ambient_context":
+                print("Dataset is ambient_context")
+                train_user_files = pd.read_csv(train_path, sep="\t", header=None).values
+                for path, label in train_user_files:
+                    file_path = ambient_context_path_extracter(path)
+                    train_files_path.append(
+                        os.path.join(path_data_dir, "Data", file_path)
+                    )
+                    train_labels.append(label)
+            # One-hot labels transformation
+            train_labels = np.array(train_labels, dtype=object)
+            # Map labels to 0-11
+            unique_labels = np.unique(train_labels)
+            num_classes = len(unique_labels)
+            labels_dict = {unique_labels[i]: i for i in range(len(unique_labels))}
+            train_labels = [
+                labels_dict[train_labels[i]] for i in range(len(train_labels))
+            ]
+            # Change from object type to int
+            train_labels = np.array(train_labels, dtype=np.int64)
+            # Convert to dataset (if necessary)
+            dataset = (
+                (train_files_path, train_labels)
+                if raw
+                else tf.data.Dataset.from_tensor_slices(
+                    (train_files_path, train_labels)
+                ).map(AudioTools.read_audio, num_parallel_calls=DataBuilder.AUTOTUNE)
+            )
+        else:
+            # Read files from txt file.
+            test_files_path, test_labels = [], []
+            if data_dir == "speech_commands":
+                print("Dataset is speech_commands")
+                test_user_files = pd.read_csv(test_path, header=None).values.flatten()
+                for ts_uf in test_user_files:
+                    path_ts_uf = os.path.join(*ts_uf.split("/"))
+                    test_files_path.append(
+                        os.path.join(path_data_dir, "Data", "Test", path_ts_uf)
+                    )
+                    test_labels.append(ts_uf.split("/")[0])
+            elif data_dir == "ambient_context":
+                print("Dataset is ambient_context")
+                test_user_files = pd.read_csv(test_path, sep="\t", header=None).values
+                for path, label in test_user_files:
+                    file_path = ambient_context_path_extracter(path)
+                    test_files_path.append(
+                        os.path.join(path_data_dir, "Data", file_path)
+                    )
+                    test_labels.append(label)
+            # One-hot labels transformation
+            test_labels = np.array(test_labels, dtype=object)
+            # Map labels to 0-12
+            unique_labels = np.unique(test_labels)
+            num_classes = len(unique_labels)
+            labels_dict = {unique_labels[i]: i for i in range(len(unique_labels))}
+            test_labels = [labels_dict[test_labels[i]] for i in range(len(test_labels))]
+            # Change from object type to int
+            test_labels = np.array(test_labels, dtype=np.int64)
+            # Convert to dataset (if necessary)
+            dataset = (
+                (test_files_path, test_labels)
+                if raw
+                else tf.data.Dataset.from_tensor_slices(
+                    (test_files_path, test_labels)
+                ).map(AudioTools.read_audio, num_parallel_calls=DataBuilder.AUTOTUNE)
+            )
+        return dataset, num_classes
+
+    # pylint: disable=too-many-arguments, too-many-locals
+    @staticmethod
+    def split_dataset(
+        parent_path,
+        data_dir,
+        num_clients,
+        client,
+        batch_size=64,
+        variance=0.25,
+        l_per=0.1,
+        u_per=1.0,
+        mean_class_distribution=3,
+        class_distribute=False,
+        fedstar=False,
+        seed=2021,
+    ):
+        """Split dataset into labelled and unlabelled sets for federated learning.
+
+        This method divides the dataset into portions suitable for training by multiple
+        clients in a federated learning context. It handles both labelled and unlabelled
+        data, and supports class-based and sample-based distribution strategies.
+
+        Parameters
+        ----------
+        - parent_path (str): Base path for the dataset directories.
+        - data_dir (str): Specific directory name containing the dataset.
+        - num_clients (int): Total number of clients in the federated learning setup.
+        - client (int): Identifier for the current client.
+        - batch_size (int): Size of the batches for training.
+        - variance (float): Variance allowed in the distribution of samples/classes.
+        - l_per (float): Percentage of labelled data to be used.
+        - u_per (float): Percentage of unlabelled data to be used.
+        - mean_class_distribution (int): Average number of classes per client.
+        - class_distribute (bool): If True, distribute data based on classes.
+        - fedstar (bool): If True, apply federated star learning setup.
+        - seed (int): Random seed for reproducibility.
+
+        Returns
+        -------
+        - Labelled and unlabelled TensorFlow datasets, number of classes, and number
+        of batches for the specified client.
+        """
+        # Load data
+        ds_train, num_classes = DataBuilder.get_files(
+            parent_path=parent_path, data_dir=data_dir, train=True, raw=True
+        )
+        (ds_train_labelled, labelled_size), (
+            ds_train_un_labelled,
+            unlabelled_size,
+        ) = DataTools.get_subset(
+            dataset=ds_train,
+            percentage=l_per,
+            u_per=u_per,
+            num_classes=num_classes,
+            seed=seed,
+        )
+        ds_train_un_labelled = (
+            DataTools.convert_to_unlabelled(
+                dataset=ds_train_un_labelled, unlabelled_data_identifier=-1
+            )
+            if fedstar
+            else []
+        )
+        # Split data
+        if not class_distribute:  # Split according to number of samples
+            labelled_sets = list(
+                DataTools.distribute_per_samples(
+                    dataset=ds_train_labelled,
+                    num_clients=num_clients,
+                    variance=variance,
+                    seed=seed,
+                )
+            )
+        else:  # Split according to classes
+            labelled_sets = DataTools.distribute_per_class_with_class_limit(
+                dataset=ds_train_labelled,
+                num_clients=num_clients,
+                num_classes=num_classes,
+                mean_class_distribution=mean_class_distribution,
+                class_variance=variance,
+                seed=seed,
+            )
+        unlabelled_sets = (
+            list(
+                DataTools.distribute_per_samples(
+                    dataset=ds_train_un_labelled,
+                    num_clients=num_clients,
+                    variance=variance,
+                    seed=seed,
+                )
+            )
+            if fedstar
+            else ([], [])
+        )
+        # Convert to tf dataset objects
+        ds_train_labelled = tf.data.Dataset.from_tensor_slices(
+            (labelled_sets[client][0], labelled_sets[client][1])
+        ).map(AudioTools.read_audio, num_parallel_calls=DataBuilder.AUTOTUNE)
+        ds_train_unlabelled = (
+            tf.data.Dataset.from_tensor_slices(
+                (unlabelled_sets[client][0], unlabelled_sets[client][1])
+            ).map(AudioTools.read_audio, num_parallel_calls=DataBuilder.AUTOTUNE)
+            if fedstar
+            else None
+        )
+        # Calculate datasets info for training
+        labelled_size, unlabelled_size = (
+            len(labelled_sets[client][0]),
+            len(unlabelled_sets[client][0]) if fedstar else 0,
+        )
+        num_batches = (
+            (unlabelled_size + batch_size - 1) // batch_size
+            if fedstar
+            else (labelled_size + batch_size - 1) // batch_size
+        )
+        # Print datasets sizes
+        print(
+            f"""Client {client}: Train data {labelled_size+unlabelled_size}
+            (Unlabelled: {unlabelled_size} - Labelled: {labelled_size})"""
+        )
+        return ds_train_labelled, ds_train_unlabelled, num_classes, num_batches
+
+    @staticmethod
+    def get_ds_test(parent_path, data_dir, batch_size, buffer=1024, seed=2021):
+        """Load and prepare the testing dataset.
+
+        Reads testing data from specified paths, converts it into a TensorFlow dataset,
+        and prepares it for model evaluation or testing.
+
+        Parameters
+        ----------
+        - parent_path (str): Base path for the dataset directories.
+        - data_dir (str): Specific directory name containing the testing dataset.
+        - batch_size (int): Size of the batches for testing.
+        - buffer (int): Buffer size for shuffling the dataset.
+        - seed (int): Random seed for reproducibility.
+
+        Returns
+        -------
+        - A TensorFlow dataset for testing and the number of classes in the dataset.
+        """
+        ds_test, num_classes = DataBuilder.get_files(
+            parent_path=parent_path, data_dir=data_dir
+        )
+        _, _, ds_test = DataBuilder.to_dataset(
+            ds_train_labelled=None,
+            ds_train_un_labelled=None,
+            ds_test=ds_test,
+            buffer=buffer,
+            batch_size=batch_size,
+            seed=seed,
+        )
+        return ds_test, num_classes
+
+    # pylint: disable=too-many-arguments, too-many-locals
+    @staticmethod
+    def load_sharded_dataset(
+        parent_path,
+        data_dir,
+        num_clients,
+        client,
+        variance=0.25,
+        batch_size=64,
+        l_per=1.0,
+        u_per=1.0,
+        mean_class_distribution=5,
+        fedstar=False,
+        class_distribute=False,
+        balance_dataset: bool = False,
+        seed=2021,
+    ):
+        """Load and shard the dataset for federated learning scenarios.
+
+        This method prepares a dataset specifically sharded for a given client in a
+        federated learning setup. It supports configurations for balanced and unbalanced
+        datasets, federated star learning, and class-based distribution.
+
+        Parameters
+        ----------
+        - parent_path (str): Base path for the dataset directories.
+        - data_dir (str): Specific directory name containing the dataset.
+        - num_clients (int): Total number of clients in the federated learning setup.
+        - client (int): Identifier for the current client.
+        - variance (float): Variance allowed in the distribution of samples/classes.
+        - batch_size (int): Size of the batches for training.
+        - l_per (float): Percentage of labelled data to be used.
+        - u_per (float): Percentage of unlabelled data to be used.
+        - mean_class_distribution (int): Average number of classes per client.
+        - fedstar (bool): If True, apply federated star learning setup.
+        - class_distribute (bool): If True, distribute data based on classes.
+        - balance_dataset (bool): If True, balance the dataset for each class.
+        - seed (int): Random seed for reproducibility.
+
+        Returns
+        -------
+        - Labelled and unlabelled TensorFlow datasets, number of classes, and number
+        of batches for the specified client.
+        """
+        batch_size = batch_size // 2 if fedstar else batch_size
+        (
+            ds_train_labelled,
+            ds_train_un_labelled,
+            num_classes,
+            num_batches,
+        ) = DataBuilder.split_dataset(
+            parent_path=parent_path,
+            data_dir=data_dir,
+            client=client,
+            num_clients=num_clients,
+            l_per=l_per,
+            u_per=u_per,
+            fedstar=fedstar,
+            class_distribute=class_distribute,
+            mean_class_distribution=mean_class_distribution,
+            batch_size=batch_size,
+            variance=variance,
+            seed=seed,
+        )
+        ds_train_labelled, ds_train_un_labelled, _ = DataBuilder.to_dataset(
+            ds_train_labelled=ds_train_labelled,
+            ds_train_un_labelled=ds_train_un_labelled,
+            ds_test=None,
+            seed=seed,
+            batch_size=batch_size,
+            balance_dataset=balance_dataset,
+        )
+        return ds_train_labelled, ds_train_un_labelled, num_classes, num_batches
+
+    # pylint: disable=too-many-arguments, too-many-locals
+    @staticmethod
+    def to_dataset(
+        ds_train_labelled,
+        ds_train_un_labelled,
+        ds_test,
+        batch_size,
+        buffer=1024,
+        seed=2021,
+        balance_dataset: bool = False,
+    ):
+        """Convert data into TensorFlow datasets for training and testing.
+
+        Transforms raw data into shuffled and batched TensorFlow datasets. It handles
+        both labelled and unlabelled training data, as well as testing data.
+
+        Parameters
+        ----------
+        - ds_train_labelled: Labelled training data.
+        - ds_train_un_labelled: Unlabelled training data.
+        - ds_test: Testing data.
+        - batch_size (int): Size of the batches for the datasets.
+        - buffer (int): Buffer size for shuffling the dataset.
+        - seed (int): Random seed for shuffling reproducibility.
+        - balance_dataset (bool): If True, balance the training dataset.
+
+        Returns
+        -------
+        - Labelled and unlabelled training TensorFlow datasets and a testing dataset.
+        """
+        ds_train_labelled = (
+            ds_train_labelled.shuffle(
+                buffer_size=buffer, seed=seed, reshuffle_each_iteration=True
+            )
+            .map(AudioTools.prepare_example, num_parallel_calls=DataBuilder.AUTOTUNE)
+            .batch(batch_size=batch_size)
+            # .prefetch(DataBuilder.AUTOTUNE)
+            if ds_train_labelled
+            else None
+        )
+        ds_test = (
+            ds_test.map(
+                AudioTools.prepare_test_example, num_parallel_calls=DataBuilder.AUTOTUNE
+            )
+            .batch(1)
+            .prefetch(DataBuilder.AUTOTUNE)
+            if ds_test
+            else None
+        )
+        ds_train_un_labelled = (
+            ds_train_un_labelled.shuffle(
+                buffer_size=buffer, seed=seed + 1, reshuffle_each_iteration=True
+            )
+            .map(AudioTools.prepare_example, num_parallel_calls=DataBuilder.AUTOTUNE)
+            .batch(batch_size=batch_size)
+            # .prefetch(DataBuilder.AUTOTUNE)
+            if ds_train_un_labelled
+            else None
+        )
+
+        # make balanced
+        if balance_dataset:
+            if ds_train_labelled:
+                ds_train_labelled = balance_sampler(ds_train_labelled, batch_size)
+            # re sampling not required for unlabelled data.
+            # if ds_train_un_labelled:
+            #     ds_train_un_labelled=balance_sampler(ds_train_un_labelled, batch_size)
+        return ds_train_labelled, ds_train_un_labelled, ds_test
+
+
+def balance_sampler(dataset, batch_size):
+    """Address the skew in the speechcommands dataset.
+
+    Here we resample it so we see the same number of instances for all classes. Here we
+    use TF's rejection_resample functionality
+    https://www.tensorflow.org/guide/data#rejection_resampling.
+    """
+
+    # pylint: disable=unused-argument
+    def class_func(features, label):
+        return label
+
+    # Measure initial distribution over labels
+    ddd = np.concatenate([lbl for _, lbl in list(dataset.as_numpy_iterator())])
+    initial_dist = np.bincount(ddd) / len(ddd)
+
+    # target distribution is uniform
+    target_dist = np.ones_like(initial_dist) / len(initial_dist)
+
+    # resample
+    dataset_resampled = (
+        dataset.unbatch()
+        .rejection_resample(
+            class_func, target_dist=target_dist, initial_dist=initial_dist
+        )
+        .batch(batch_size)
+    )
+
+    dataset_balanced = dataset_resampled.map(
+        lambda extra_label, features_and_label: features_and_label
+    )
+
+    return dataset_balanced
diff --git a/baselines/fedstar/fedstar/dataset_preparation.py b/baselines/fedstar/fedstar/dataset_preparation.py
new file mode 100644
index 000000000000..64857318b747
--- /dev/null
+++ b/baselines/fedstar/fedstar/dataset_preparation.py
@@ -0,0 +1,43 @@
+"""Generate 1-second audio clip from background sounds in speechcommands."""
+import os
+import random
+
+import scipy.io.wavfile as wav
+from tqdm import tqdm
+
+# Folder path where your audio files are stored
+FOLDER_PATH = "datasets/speech_commands/Data/Train/_silence_"
+# Path of the text file where you want to append the filenames
+TEXT_FILE_PATH = "data_splits/speech_commands/train_split.txt"
+
+
+def split_audio_v2(read_file_path, samples_per_clip=1000):
+    """Split an audio file into a specified number of clips.
+
+    Parameters
+    ----------
+    read_file_path : str
+        The file path of the audio file to be split.
+    samples_per_clip : int, optional
+        The number of one-second clips to extract from the audio file. Defaults to 1000.
+    """
+    sample_rate, data = wav.read(read_file_path)
+
+    # extract `samples_per_clip` 1-second long clips at random
+    for i in tqdm(range(samples_per_clip)):
+        start = random.randint(0, len(data) - sample_rate)
+        segment = data[start : start + sample_rate]
+        new_filename = file_path.replace(".wav", "") + f"_{i:05d}.wav"
+
+        wav.write(new_filename, sample_rate, segment)
+        sound_name = new_filename.split("/")[-1]
+        # Append the new filename to the text file
+        with open(TEXT_FILE_PATH, "a", encoding="utf-8") as write_file:
+            write_file.write(f"\n_silence_/{sound_name}")
+
+
+# Iterate over files in the folder
+for filename in os.listdir(FOLDER_PATH):
+    if filename.endswith(".wav"):  # Check if the file is a WAV file
+        file_path = os.path.join(FOLDER_PATH, filename)
+        split_audio_v2(file_path)
diff --git a/baselines/fedstar/fedstar/main.py b/baselines/fedstar/fedstar/main.py
new file mode 100644
index 000000000000..f5f425272fdb
--- /dev/null
+++ b/baselines/fedstar/fedstar/main.py
@@ -0,0 +1 @@
+"""Run server.py and client.py files separately as directed in the Readme.md."""
diff --git a/baselines/fedstar/fedstar/models.py b/baselines/fedstar/fedstar/models.py
new file mode 100644
index 000000000000..2e2682096a0e
--- /dev/null
+++ b/baselines/fedstar/fedstar/models.py
@@ -0,0 +1,427 @@
+"""Required imports for data.py script."""
+import itertools
+import time
+
+import numpy as np
+import tensorflow as tf
+
+
+class History(tf.keras.callbacks.History):
+    """A custom implementation of the Keras History callback for logging history.
+
+    This class extends tf.keras.callbacks.History to provide custom functionalities for
+    tracking the training process. It captures and stores metrics at the end of each
+    epoch.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.history = {}
+
+    def on_epoch_end(self, epoch, logs=None):
+        """Handle the end of an epoch during model training.
+
+        Updates the history attribute with information about the completed epoch,
+        including any provided logs such as loss and accuracy metrics.
+
+        Parameters
+        ----------
+        - epoch (int): The index of the recently completed epoch.
+        - logs (dict, optional): A dictionary containing metrics and their values
+        for the completed epoch.
+        """
+        logs = logs or {}
+        self.epoch.append(epoch)
+        for key, value in logs.items():
+            self.history.setdefault(key, []).append(value)
+
+
+class Network:
+    """A network class for building a custom audio classification model.
+
+    This class constructs a convolutional neural network for audio data classification.
+    It includes methods to define convolutional blocks and assemble the full model.
+
+    Attributes
+    ----------
+    - num_classes (int): Number of classes for classification.
+    - dropout_rate (float): Dropout rate for regularization.
+    - l2_rate (float): L2 regularization rate.
+    - input_shape (tuple): Shape of the input data.
+    - sequence_length (int): Length of the input sequences.
+    """
+
+    # pylint: disable=too-many-arguments, too-many-locals
+    def __init__(
+        self,
+        num_classes,
+        dropout_rate=0.1,
+        l2_rate=0.0001,
+        input_shape=(None, 64, 1),
+        sequence_length=16000,
+    ):
+        self._num_classes = num_classes
+        self._dropout_rate = dropout_rate
+        self._l2_rate = l2_rate
+        self._input_shape = input_shape
+        self._sequence_length = sequence_length
+
+    @staticmethod
+    def conv_block(
+        input_shape, num_features, l2_rate, dropout_rate=0.1, add_max_pool=True
+    ):
+        """Define a convolutional block for the neural network.
+
+        Constructs a block with convolutional layers, normalization, activation,
+        and optional pooling and dropout, based on the specified parameters.
+
+        Parameters
+        ----------
+        - input_shape (tuple): Shape of the input to the convolutional block.
+        - num_features (int): Number of filters in the convolutional layers.
+        - l2_rate (float): L2 regularization rate.
+        - dropout_rate (float, optional): Dropout rate for regularization.
+        - add_max_pool (bool, optional): Whether to include max pooling in the block.
+
+        Returns
+        -------
+        - tf.keras.Model: A Keras model representing the convolutional block.
+        """
+        inputs = tf.keras.layers.Input(shape=input_shape)
+        x_t = tf.keras.layers.Conv2D(
+            num_features,
+            (1, 4),
+            padding="same",
+            kernel_regularizer=tf.keras.regularizers.l2(l2_rate),
+        )(inputs)
+        x_t = tf.keras.layers.GroupNormalization(groups=4)(x_t)
+        x_t = tf.keras.layers.Activation("relu")(x_t)
+        x_f = tf.keras.layers.Conv2D(
+            num_features,
+            (4, 1),
+            padding="same",
+            kernel_regularizer=tf.keras.regularizers.l2(l2_rate),
+        )(inputs)
+        x_f = tf.keras.layers.GroupNormalization(groups=4)(x_f)
+        x_f = tf.keras.layers.Activation("relu")(x_f)
+        x = tf.keras.layers.Concatenate(axis=-1)([x_t, x_f])
+        x = tf.keras.layers.Conv2D(
+            num_features,
+            (1, 1),
+            padding="same",
+            kernel_regularizer=tf.keras.regularizers.l2(l2_rate),
+        )(x)
+        x = tf.keras.layers.GroupNormalization(groups=4)(x)
+        x = tf.keras.layers.Activation("relu")(x)
+        if add_max_pool:
+            x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
+        x = tf.keras.layers.SpatialDropout2D(dropout_rate)(x)
+        return tf.keras.Model(inputs, x)
+
+    def get_network(self):
+        """Assembles and returns the custom audio classification model.
+
+        Constructs the neural network layer by layer using the class attributes and
+        predefined convolutional blocks.
+
+        Returns
+        -------
+        - tf.keras.Model: The assembled TensorFlow Keras model
+          ready for training or inference.
+        """
+        inputs = tf.keras.layers.Input(shape=self._input_shape, name="audio_input")
+        x = __class__.conv_block(
+            self._input_shape, 24, self._l2_rate, dropout_rate=self._dropout_rate
+        )(inputs)
+        x = __class__.conv_block(
+            x.shape[1:], 32, self._l2_rate, dropout_rate=self._dropout_rate
+        )(x)
+        x = __class__.conv_block(
+            x.shape[1:], 64, self._l2_rate, dropout_rate=self._dropout_rate
+        )(x)
+        x = __class__.conv_block(
+            x.shape[1:], 128, self._l2_rate, dropout_rate=self._dropout_rate
+        )(x)
+        x = tf.keras.layers.GlobalMaxPool2D()(x)
+        outputs = tf.keras.layers.Dense(self._num_classes)(x)
+        model = tf.keras.Model(inputs, outputs, name="audio_classifier")
+        return model
+
+    def get_evaluation_network(self, input_shape=(None, None, 64, 1)):
+        """Create an evaluation model for the network.
+
+        Constructs a model for evaluation which applies the primary network to a
+        time-distributed input and computes the mean output.
+
+        Parameters
+        ----------
+        - input_shape (tuple, optional):
+          Shape of the input data for the evaluation model.
+
+        Returns
+        -------
+        - tf.keras.Model: The evaluation model based on the primary network.
+        """
+        model = self.get_network()
+        input_layer = tf.keras.layers.Input(shape=input_shape, name="audio_input")
+        output = tf.reduce_mean(
+            tf.keras.layers.TimeDistributed(model)(input_layer), axis=1
+        )
+        return tf.keras.Model(input_layer, output)
+
+    @staticmethod
+    def get_init_weights(num_classes, input_shape=(None, 64, 1), l2_rate=0.0001):
+        """Generate and return initial weights for the network.
+
+        Constructs a temporary model for the sole purpose of initializing weights,
+        based on the specified number of classes, input shape, and regularization rate.
+
+        Parameters
+        ----------
+        - num_classes (int): Number of classes for the classification task.
+        - input_shape (tuple, optional): Shape of the input data.
+        - l2_rate (float, optional): L2 regularization rate.
+
+        Returns
+        -------
+        - List: A list of numpy arrays representing the initial weights of the model.
+        """
+        inputs = tf.keras.layers.Input(shape=input_shape, name="audio_input")
+        x = Network.conv_block(input_shape, 24, l2_rate)(inputs)
+        x = Network.conv_block(x.shape[1:], 32, l2_rate)(x)
+        x = Network.conv_block(x.shape[1:], 64, l2_rate)(x)
+        x = Network.conv_block(x.shape[1:], 128, l2_rate)(x)
+        x = tf.keras.layers.GlobalMaxPool2D()(x)
+        outputs = tf.keras.layers.Dense(num_classes)(x)
+        model = tf.keras.Model(inputs, outputs, name="audio_classifier")
+        return model.get_weights()
+
+
+class PslNetwork(tf.keras.Model):  # pylint: disable=abstract-method
+    """A TensorFlow Keras model for Pseudo-Label Semi-supervised Learning (PSL).
+
+    This class extends tf.keras.Model to implement a specific architecture and
+    training procedure for semi-supervised learning using pseudo-labeling.
+
+    Attributes
+    ----------
+    - num_classes (int): Number of classes for classification.
+    - aux_loss_weight (float): Weight for the auxiliary loss in training.
+    - dropout_rate (float): Dropout rate for regularization.
+    - model (tf.keras.Model): The underlying audio classification model.
+    - confidence (float): Confidence threshold for pseudo-labeling.
+    """
+
+    def __init__(self, num_classes, aux_loss_weight=0.5, dropout_rate=0.1):
+        super().__init__()
+        self.model = Network(
+            num_classes=num_classes, dropout_rate=dropout_rate
+        ).get_network()
+        self.confidence = 0.5
+        self.aux_loss_weight = aux_loss_weight
+        self.history = History()
+        self.optimizer = None
+        self.loss_fn = None
+
+    # pylint: disable=arguments-differ
+    def compile(self, optimizer):
+        """Configure the model for training.
+
+        Sets the optimizer and loss function for the model.
+
+        Parameters
+        ----------
+        - optimizer: The optimizer to use for training the model.
+        """
+        super().compile()
+        self.optimizer = optimizer
+        self.loss_fn = tf.nn.sparse_softmax_cross_entropy_with_logits
+
+    @staticmethod
+    def cosine_confidence_scheduler(
+        step, total_steps, confidence_min=0.5, confidence_max=0.9
+    ):
+        """Determine the confidence level for pseudo-labeling using a cosine schedule.
+
+        Gradually adjusts the confidence threshold over the course of training based
+        on the cosine function.
+
+        Parameters
+        ----------
+        - step (int): The current training step or round.
+        - total_steps (int): The total number of steps or rounds
+                             in the training process.
+        - confidence_min (float, optional): Minimum confidence threshold.
+        - confidence_max (float, optional): Maximum confidence threshold.
+
+        Returns
+        -------
+        - float: The computed confidence threshold for the current step.
+        """
+        return confidence_max - (confidence_max - confidence_min) * 0.5 * (
+            1 + np.cos(step / total_steps * np.pi)
+        )
+
+    # pylint: disable=too-many-arguments, too-many-locals, arguments-differ
+    def fit(
+        self, train_datasets, epochs, c_round, rounds, num_batches=None, T=1, verbose=0
+    ):
+        """Trains the model on given datasets for a specified number of epochs.
+
+        This custom training loop handles both labeled and unlabeled data, applying
+        pseudo-labeling for semi-supervised learning.
+
+        Parameters
+        ----------
+        - train_datasets (tuple): A tuple containing labeled
+                                  and unlabeled training datasets.
+        - epochs (int): Number of epochs to train the model.
+        - c_round (int): Current round of training in a federated learning scenario.
+        - rounds (int): Total number of rounds in the federated learning scenario.
+        - num_batches (int, optional): Number of batches per epoch.
+        - T (float, optional): Temperature parameter for softmax in pseudo-labeling.
+        - verbose (int, optional): Verbosity mode.
+
+        Returns
+        -------
+        - History: An object containing training history information.
+        """
+        l_dataset, u_dataset = train_datasets
+        self.history.on_train_begin()
+        for epoch in range(epochs):
+            start, epoch_loss = time.time(), 0.0
+            # Threshold value to get good psuedo labels, part of Eq-5
+            self.confidence = __class__.cosine_confidence_scheduler(
+                step=c_round, total_steps=rounds
+            )
+            progbar = tf.keras.utils.Progbar(
+                target=num_batches - 1,
+                width=30,
+                verbose=verbose if verbose != 2 else 0,
+                interval=0.05,
+                stateful_metrics=None,
+                unit_name="step",
+            )
+            if verbose == 1:
+                tf.print(f"Epoch {epoch+1}/{epochs}")
+            step = 0
+            # Run train step on each batch
+            # tf.data.Dataset.repeat
+            # (https://www.tensorflow.org/api_docs/python/tf/data/Dataset#repeat)
+            # can also be used instead of itertools.cycle
+            for step, ((l_batch_x, l_batch_y), (u_batch_x, u_batch_y)) in enumerate(
+                zip(itertools.cycle(l_dataset), u_dataset)
+            ):
+                batch_x, batch_y = tf.concat([l_batch_x, u_batch_x], 0), tf.concat(
+                    [l_batch_y, u_batch_y], 0
+                )
+                loss = self.train_step(x=batch_x, y=batch_y, T=T)
+                progbar.update(current=step, values=[("loss", float(loss["loss"]))])
+                epoch_loss += float(loss["loss"])
+            # Store result in history
+            self.history.on_epoch_end(
+                epoch=epoch,
+                logs={"loss": epoch_loss / num_batches, "accuracy": float(0.0)},
+            )
+            if verbose == 2:
+                tf.print(
+                    f"Epoch {epoch + 1}/{epochs}\n"
+                    f"{step}/{step} - {time.time() - start:.1f}s - "
+                    f"loss: {epoch_loss / num_batches:.4f} - "
+                    f"accuracy: {float(0.0):.4f}"
+                )
+        return self.history
+
+    # pylint: disable=too-many-arguments, too-many-locals, arguments-differ
+    @tf.function
+    def train_step(self, x, y, T=4):
+        """Perform a single training step on a batch of data.
+
+        Applies the forward pass, loss computation, and backpropagation to update
+        the model weights using both labeled and unlabeled data.
+
+        Parameters
+        ----------
+        - x (Tensor): Input data.
+        - y (Tensor): Corresponding labels (or -1 for unlabeled data).
+        - T (float, optional): Temperature parameter for softmax in pseudo-labeling.
+
+        Returns
+        -------
+        - dict: A dictionary containing the computed losses for the training step.
+        """
+        with tf.GradientTape() as tape:
+            logits, labels = self.model(x, training=True), y
+            labeled_mask, unlabeled_mask = tf.not_equal(-1, labels), tf.cast(
+                tf.equal(-1, labels), dtype=tf.float32
+            )
+            num_labeled, num_unlabeled = tf.math.reduce_sum(
+                tf.cast(labeled_mask, dtype=tf.float32)
+            ), tf.math.reduce_sum(unlabeled_mask)
+            masked_logits, masked_labels = tf.boolean_mask(
+                logits, labeled_mask
+            ), tf.boolean_mask(labels, labeled_mask)
+            # Equation 4: Loss function for labelled images.
+            loss_labeled = (
+                tf.reduce_sum(self.loss_fn(labels=masked_labels, logits=masked_logits))
+                / num_labeled
+            )
+            # Equation 5: Psuedo labels for the unlabelled images.
+            pseudo_labels = tf.stop_gradient(input=tf.nn.softmax(logits / T))
+            pseudo_mask = tf.cast(
+                tf.reduce_max(pseudo_labels, axis=1) >= self.confidence,
+                dtype=tf.float32,
+            )
+            # Equation 6: Loss function for unlabelled images.
+            loss_unlabeled = (
+                tf.reduce_sum(
+                    (
+                        tf.cast(
+                            self.loss_fn(
+                                labels=tf.argmax(pseudo_labels, axis=1), logits=logits
+                            ),
+                            dtype=tf.float32,
+                        )
+                        * pseudo_mask
+                    )
+                    * unlabeled_mask
+                )
+                / num_unlabeled
+            )
+            # Equation 7: loss of kth client in rth round (Eq-4 + Eq-6)
+            total_loss = tf.add_n(
+                [
+                    ((1 - self.aux_loss_weight) * loss_labeled)
+                    + (self.aux_loss_weight * loss_unlabeled)
+                ]
+                + self.model.losses
+            )
+        trainable_vars = self.trainable_variables
+        gradients = tape.gradient(total_loss, trainable_vars)
+        self.optimizer.apply_gradients(zip(gradients, trainable_vars))
+        return {
+            "loss": total_loss,
+            "labelled_loss": loss_labeled,
+            "unlabelled_loss": loss_unlabeled,
+        }
+
+    def get_evaluation_model(self, input_shape=(None, None, 64, 1)):
+        """Create an evaluation model based on the trained PSL network.
+
+        Constructs a model for evaluation by applying the trained network in a
+        time-distributed manner to process variable-length inputs.
+
+        Parameters
+        ----------
+        - input_shape (tuple, optional): Shape of the input
+                                         data for the evaluation model.
+
+        Returns
+        -------
+        - tf.keras.Model: The evaluation model based on the PSL network.
+        """
+        input_layer = tf.keras.layers.Input(shape=input_shape)
+        output = tf.reduce_mean(
+            tf.keras.layers.TimeDistributed(self.model)(input_layer), axis=1
+        )
+        return tf.keras.Model(input_layer, output)
diff --git a/baselines/fedstar/fedstar/server.py b/baselines/fedstar/fedstar/server.py
new file mode 100644
index 000000000000..318613b4db14
--- /dev/null
+++ b/baselines/fedstar/fedstar/server.py
@@ -0,0 +1,291 @@
+"""Required imports for server.py script."""
+import gc
+import logging
+import os
+import sys
+import time
+import uuid
+
+import flwr
+import hydra
+import tensorflow as tf
+from omegaconf import DictConfig, OmegaConf
+
+from fedstar.dataset import DataBuilder
+from fedstar.models import Network
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+os.environ["GRPC_VERBOSITY"] = "ERROR"
+GRPC_MAX_MESSAGE_LENGTH: int = 536_870_912
+
+parent_path = os.getcwd()
+
+
+def get_eval_fn(ds_test, evaluation_step, num_classes, verbose):
+    """Return a function to evaluate the model during federated learning.
+
+    This function is used by the Flower framework to evaluate the model at specified
+    intervals using the provided test dataset.
+
+    Parameters
+    ----------
+    - ds_test (tf.data.Dataset): The dataset used for evaluation.
+    - evaluation_step: After this rounds of aggregartion server evaluates the model.
+    - num_classes: Number of classes/labels model has to classify.
+    - verbose: How much details should be displayed.
+
+    Returns
+    -------
+    - Function: An evaluation function that takes server_round, weights, and configs
+    as parameters and returns the loss and accuracy.
+    """
+
+    # pylint: disable=unused-argument
+    def evaluate(server_round, weights, configs):
+        # Clear session
+        # https://www.tensorflow.org/api_docs/python/tf/keras/backend/clear_session
+        tf.keras.backend.clear_session()
+        loss, acc = 0, 0
+        if (server_round - 1) % evaluation_step == 0:
+            model = Network(num_classes=num_classes).get_evaluation_network()
+            model.compile(
+                optimizer=tf.keras.optimizers.Adam(),
+                loss=tf.keras.losses.SparseCategoricalCrossentropy(
+                    from_logits=True, name="loss"
+                ),
+                metrics=[tf.keras.metrics.SparseCategoricalAccuracy(name="accuracy")],
+            )
+            model.set_weights(weights)
+            loss, acc = model.evaluate(ds_test, verbose=verbose)
+        gc.collect()
+        return float(loss), {"accuracy": float(acc)}
+
+    return evaluate
+
+
+def get_on_fit_config_fn(total_rounds: int, epochs: int, batch_size: int):
+    """Return a function to configure the federated learning fit process.
+
+    This function is called by the Flower framework to obtain configuration
+    parameters for each training round.
+
+    Parameters
+    ----------
+    - total_rounds: Number of coummunication rounds between client and server.
+    - epochs: Number of epochs/ iterations
+    - batch_size: The number of training instances in the batch
+
+    Returns
+    -------
+    - Function: A function that takes rounds, epochs, and batch_size
+    as parameters and returns a configuration dictionary.
+    """
+
+    # pylint: disable=unused-argument
+    def fit_config(server_round: int):
+        print(
+            f"\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n"
+            f"Server started {server_round}th round of training.\n"
+            f"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n"
+        )
+        return {
+            "rounds": str(total_rounds),
+            "c_round": str(server_round),
+            "epochs": str(epochs),
+            "batch_size": str(batch_size),
+        }
+
+    return fit_config
+
+
+class AudioServer:  # pylint: disable=too-many-instance-attributes
+    """A server class for federated learning using Flower framework.
+
+    This class sets up and runs a federated learning server using the Flower framework,
+    handling the orchestration of training across multiple clients. It manages model
+    training rounds, evaluation steps, and tracks overall progress and performance.
+
+    Attributes
+    ----------
+    - flwr_evalution_step (int): After this many rounds the server evaluates the model.
+    - flwr_min_sample_size (int): Minimum number of clients participate in a FL round.
+    - flwr_num_clients (int): Total number of clients for federated learning.
+    - flwr_rounds (int): Number of coummunication rounds between client and server.
+    - model_num_classes (int): Number of classes/labels model has to classify.
+    - model_batch_size (int): Batch size for model training.
+    - model_epochs (int): Number of epochs for model training.
+    - model_ds_test (tf.data.Dataset): Dataset for testing the model.
+    - model_verbose (int): Verbosity level for model training.
+    """
+
+    # pylint: disable=too-many-arguments, too-many-locals
+    def __init__(
+        self,
+        flwr_evalution_step,
+        fraction_fit,
+        flwr_num_clients,
+        flwr_rounds,
+        model_num_classes,
+        model_batch_size,
+        model_epochs,
+        model_ds_test,
+        model_verbose,
+    ):
+        # Flower Parameters
+        self.rounds = flwr_rounds
+        # Local Variables Counters and Variables
+        self.current_round = 0
+        self.final_accuracy = 0.0
+        self.round_time = time.time()
+        self.strategy = flwr.server.strategy.FedAvg(
+            fraction_fit=float(fraction_fit),
+            min_available_clients=flwr_num_clients,
+            on_fit_config_fn=get_on_fit_config_fn(
+                self.rounds, model_epochs, model_batch_size
+            ),
+            fraction_evaluate=0,
+            min_evaluate_clients=0,
+            on_evaluate_config_fn=None,
+            evaluate_fn=get_eval_fn(
+                model_ds_test,
+                flwr_evalution_step,
+                num_classes=model_num_classes,
+                verbose=model_verbose,
+            ),
+            accept_failures=True,
+        )
+        self.client_manager = flwr.server.client_manager.SimpleClientManager()
+
+    def server_start(self, server_address):
+        """Start the federated learning server with the given address.
+
+        Initializes and runs the Flower federated learning server using the specified
+        server address and pre-configured strategy and client manager.
+
+        Parameters
+        ----------
+        - server_address (str): The address at which the server will be accessible.
+        """
+        # print("|" * 50)
+        # print(server_address)
+        flwr.server.start_server(
+            server_address=server_address,
+            server=flwr.server.Server(
+                client_manager=self.client_manager, strategy=self.strategy
+            ),
+            config=flwr.server.ServerConfig(num_rounds=self.rounds),
+            strategy=self.strategy,
+            grpc_max_message_length=GRPC_MAX_MESSAGE_LENGTH,
+        )
+
+
+def set_logger_level():
+    """Set the logging level for the 'flower' logger.
+
+    Adjusts the logging level to INFO for the 'flower' logger if it is present in the
+    logging manager's dictionary.
+    """
+    if "flower" in [
+        repr(logging.getLogger(name))[8:].split(" ")[0]
+        for name in logging.Logger.manager.loggerDict
+    ]:
+        logger = logging.getLogger("flower")
+        logger.setLevel(logging.INFO)
+
+
+def set_gpu_limits(gpu_id, gpu_memory):
+    """Configure GPU settings for TensorFlow.
+
+    Sets the CUDA_VISIBLE_DEVICES environment variable and configures TensorFlow's
+    virtual device settings for GPU memory limits.
+
+    Parameters
+    ----------
+    - gpu_id (str): Identifier for the GPU to be used.
+    - gpu_memory (int): The maximum amount of memory (in MB) to be allocated to the GPU.
+    """
+    os.environ["CUDA_VISIBLE_DEVICES"] = gpu_id
+
+    gpus = tf.config.list_physical_devices("GPU")
+    if not gpus:
+        print("No GPU's available. Server will run on CPU.")
+    else:
+        print("GPU is available trying to configure memory.")
+        try:
+            tf.config.experimental.set_virtual_device_configuration(
+                gpus[0],
+                [
+                    tf.config.experimental.VirtualDeviceConfiguration(
+                        memory_limit=gpu_memory
+                    )
+                ],
+            )
+        except RuntimeError as runtime_error:
+            print(runtime_error)
+
+
+# Change the config path and yaml file name based on expermients you want to run.
+# The current configs will call the basic config file which runs the small
+# experiment to check integrity.
+# Eg:-
+# To carry out results for experiment in row 3 row 1 col 1. The parameters will be
+# config_path = conf/table_3/row_2_clients_10
+# config_name = speech_commands
+@hydra.main(config_path="conf", config_name="table3", version_base=None)
+def main(cfg: DictConfig):
+    """Run the federated learning server with Hydra configuration.
+
+    Initializes and starts the federated learning server based on configurations
+    provided by Hydra. It sets up the environment, loads the test dataset, and
+    initiates the AudioServer instance to manage the federated learning process.
+
+    Parameters
+    ----------
+    - cfg (DictConfig): The configuration object provided by Hydra.
+    """
+    # Set Experiment Parameters
+    unique_id = str(uuid.uuid1())
+
+    # print config structured as YAML
+    print(OmegaConf.to_yaml(cfg))
+
+    dataset_name = cfg.server.dataset_name
+    # Notify Experiment ID to console.
+    print(
+        f"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n"
+        f" Experiment ID : {unique_id}\n"
+        f"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n"
+    )
+
+    # GPU Setup
+    gpus = tf.config.experimental.list_physical_devices("GPU")
+    if gpus:
+        set_gpu_limits(gpu_id="0", gpu_memory=cfg.server.gpu_memory)
+    # Load Test Dataset
+    ds_test, num_classes = DataBuilder.get_ds_test(
+        parent_path=parent_path,
+        data_dir=dataset_name,
+        batch_size=cfg.server.batch_size,
+        buffer=1024,
+        seed=cfg.server.seed,
+    )
+    # Create Server Object
+    audio_server = AudioServer(
+        flwr_evalution_step=cfg.server.eval_step,
+        fraction_fit=cfg.server.fraction_fit,
+        flwr_num_clients=cfg.server.num_clients,
+        flwr_rounds=cfg.server.rounds,
+        model_num_classes=num_classes,
+        model_batch_size=cfg.server.batch_size,
+        model_epochs=cfg.server.train_epochs,
+        model_ds_test=ds_test,
+        model_verbose=cfg.server.verbose,
+    )
+    # Run server
+    set_logger_level()
+    audio_server.server_start(cfg.server.server_address)
+
+
+if __name__ == "__main__":
+    main()
+    sys.exit(0)
diff --git a/baselines/fedstar/fedstar/strategy.py b/baselines/fedstar/fedstar/strategy.py
new file mode 100644
index 000000000000..17436c401c30
--- /dev/null
+++ b/baselines/fedstar/fedstar/strategy.py
@@ -0,0 +1,5 @@
+"""Optionally define a custom strategy.
+
+Needed only when the strategy is not yet implemented in Flower or because you want to
+extend or modify the functionality of an existing strategy.
+"""
diff --git a/baselines/fedstar/fedstar/utils.py b/baselines/fedstar/fedstar/utils.py
new file mode 100644
index 000000000000..18bd049614e7
--- /dev/null
+++ b/baselines/fedstar/fedstar/utils.py
@@ -0,0 +1,660 @@
+"""Required imports for utils.py script."""
+import collections
+import itertools
+import random
+
+import numpy as np
+import tensorflow as tf
+
+
+class AudioTools:  # pylint: disable=too-many-instance-attributes
+    """Provide static methods for audio data processing in a machine learning context.
+
+    This class includes methods for reading, padding, extracting, and transforming audio
+    waveforms into features suitable for machine learning models.
+    """
+
+    @staticmethod
+    def read_audio(path, label):
+        """Read an audio file and return its waveform along with the label.
+
+        Parameters
+        ----------
+        - path: The file path of the audio file.
+        - label: The associated label of the audio file.
+
+        Returns
+        -------
+        - Tuple containing the waveform of the audio file and its label.
+        """
+        raw_audio = tf.io.read_file(path)
+        waveform, _ = tf.audio.decode_wav(raw_audio)
+        waveform = waveform[Ellipsis, 0]
+        return waveform, label
+
+    @staticmethod
+    def pad(waveform, sequence_length=16000):
+        """Pad the given waveform to a specified sequence length.
+
+        Parameters
+        ----------
+        - waveform: The waveform to be padded.
+        - sequence_length: The length to which the waveform should be padded.
+
+        Returns
+        -------
+        - The padded waveform.
+        """
+        padding = tf.maximum(sequence_length - tf.shape(waveform)[0], 0)
+        left_pad = padding // 2
+        right_pad = padding - left_pad
+        return tf.pad(waveform, paddings=[[left_pad, right_pad]])
+
+    @staticmethod
+    def extract_window(waveform, seg_length=15690):
+        """Extract a window of specified length from the given waveform.
+
+        Parameters
+        ----------
+        - waveform: The input waveform.
+        - seg_length: The length of the segment to extract.
+
+        Returns
+        -------
+        - A segment of the waveform of the specified length.
+        """
+        waveform = AudioTools.pad(waveform)
+        return tf.image.random_crop(waveform, [seg_length])
+
+    # pylint: disable=too-many-arguments, too-many-locals
+    @staticmethod
+    def extract_spectrogram(
+        waveform,
+        sample_rate=16000,
+        frame_length=400,
+        frame_step=160,
+        fft_length=1024,
+        n_mels=64,
+        fmin=60.0,
+        fmax=7800.0,
+    ):
+        """Convert a waveform to a log-mel spectrogram.
+
+        Parameters
+        ----------
+        - waveform: The input waveform.
+        - sample_rate: Sampling rate of the audio.
+        - frame_length: Length of each audio frame.
+        - frame_step: Step size between frames.
+        - fft_length: Length of the Fast Fourier Transform.
+        - n_mels: Number of Mel bands.
+        - fmin: Minimum frequency.
+        - fmax: Maximum frequency.
+
+        Returns
+        -------
+        - Log-mel spectrogram of the waveform.
+        """
+        stfts = tf.signal.stft(
+            waveform,
+            frame_length=frame_length,
+            frame_step=frame_step,
+            fft_length=fft_length,
+        )
+        spectrograms = tf.abs(stfts)
+        num_spectrogram_bins = stfts.shape[-1]
+        lower_edge_hertz, upper_edge_hertz, num_mel_bins = fmin, fmax, n_mels
+        linear_to_mel_weight_matrix = tf.signal.linear_to_mel_weight_matrix(
+            num_mel_bins,
+            num_spectrogram_bins,
+            sample_rate,
+            lower_edge_hertz,
+            upper_edge_hertz,
+        )
+        mel_spectrograms = tf.tensordot(spectrograms, linear_to_mel_weight_matrix, 1)
+        mel_spectrograms.set_shape(
+            spectrograms.shape[:-1].concatenate(linear_to_mel_weight_matrix.shape[-1:])
+        )
+        mel_spectrograms = tf.clip_by_value(
+            mel_spectrograms, clip_value_min=1e-5, clip_value_max=1e8
+        )
+        log_mel_spectrograms = tf.math.log(mel_spectrograms)
+        return log_mel_spectrograms[Ellipsis, tf.newaxis]
+
+    @staticmethod
+    def prepare_example(waveform, label):
+        """Prepare an audio example for model training or evaluation.
+
+        Parameters
+        ----------
+        - waveform: The input waveform.
+        - label: The label of the audio example.
+
+        Returns
+        -------
+        - Prepared log-mel spectrogram feature and the corresponding label.
+        """
+        waveform = AudioTools.pad(waveform)
+        waveform = tf.math.l2_normalize(waveform, epsilon=1e-9)
+        waveform = AudioTools.extract_window(waveform)
+        log_mel_spectrogram = AudioTools.extract_spectrogram(waveform)
+        return log_mel_spectrogram, label
+
+    @staticmethod
+    def prepare_test_example(waveform, label):
+        """Prepare an audio example specifically for testing.
+
+        Parameters
+        ----------
+        - waveform: The input waveform.
+        - label: The label of the audio example.
+
+        Returns
+        -------
+        - Prepared log-mel spectrogram feature for testing and the corresponding label.
+        """
+        waveform = AudioTools.pad(waveform)
+        waveform = tf.signal.frame(
+            waveform, frame_length=98 * 160, frame_step=98 * 160, pad_end=False
+        )
+        waveform = tf.math.l2_normalize(waveform, axis=-1, epsilon=1e-9)
+        log_mel_spectrogram = AudioTools.extract_spectrogram(waveform)
+        return log_mel_spectrogram, label
+
+
+class DataTools:  # pylint: disable=too-many-instance-attributes
+    """Provide methods for dataset manipulation and distribution in federated learning.
+
+    Includes methods for calculating statistics, distributing datasets among clients,
+    and transforming datasets for semi-supervised learning.
+    """
+
+    @staticmethod
+    def get_num_samples(dataset):
+        """Get the number of samples in a dataset.
+
+        Parameters
+        ----------
+        - dataset: The dataset to evaluate.
+
+        Returns
+        -------
+        - The number of samples in the dataset.
+        """
+        return len(dataset[0])
+
+    @staticmethod
+    def get_statistics(num_samples, num_clients, var):
+        """Calculate statistical parameters for dataset distribution.
+
+        Parameters
+        ----------
+        - num_samples: Total number of samples in the dataset.
+        - num_clients: Number of clients in federated learning.
+        - var: Variance factor for distribution.
+
+        Returns
+        -------
+        - Calculated mean, variance, limits, and error values for distribution.
+        """
+        mean = num_samples / num_clients
+        var = int(var * mean)
+        minimum, maximum = mean - var, int(mean + var)
+        error = num_samples - minimum * num_clients  # values to be distributed
+        round_error = (error - int(error)) + ((mean - int(mean)) * num_clients)
+        return mean, var, (int(minimum), maximum), (int(error), round_error)
+
+    @staticmethod
+    def get_class_distribution(dataset):
+        """Determine the class distribution in a dataset.
+
+        Parameters
+        ----------
+        - dataset: The dataset whose class distribution is to be analyzed.
+
+        Returns
+        -------
+        - A counter object representing the class distribution.
+        """
+        return collections.Counter(dataset[1])
+
+    @staticmethod
+    def split_class_samples(dataset, partitions=1):
+        """Split class samples in a dataset into specified number of partitions.
+
+        Parameters
+        ----------
+        - dataset: The dataset to be split.
+        - partitions: The number of partitions.
+
+        Returns
+        -------
+        - A list of dataset partitions.
+        """
+        total = len(dataset[0])
+        size = total // partitions
+        rest = total % partitions
+        ranges = []
+        assert size != 0 and partitions > 0
+        if rest:
+            index = list(range(0, total, size))
+            extra = [index[i] + i for i in range(rest + 1)] + [
+                x + rest for x in index[rest + 1 :][: partitions - rest]
+            ]
+            ranges = [(extra[i], extra[i + 1]) for i in range(len(extra) - 1)]
+        else:
+            index = list(range(0, total + 1, size))
+            ranges = [(index[i], index[i + 1]) for i in range(len(index) - 1)]
+        return [(dataset[0][i:j], dataset[1][i:j]) for i, j in ranges]
+
+    @staticmethod
+    def get_dataset_class_samples(dataset, class_number):
+        """Extract and return samples of a specific class from the dataset.
+
+        Parameters
+        ----------
+        - dataset: The dataset from which to extract samples.
+        - class_number: The class number to filter by.
+
+        Returns
+        -------
+        - Tuple of lists: (samples, labels) of the specified class.
+        """
+        dataset = list(
+            zip(
+                *[
+                    (dataset[0][idx], label)
+                    for idx, label in enumerate(dataset[1])
+                    if label == class_number
+                ]
+            )
+        )
+        return list(dataset[0]), list(dataset[1])
+
+    @staticmethod
+    def get_subset(dataset, percentage, num_classes, u_per=1.0, seed=2021):
+        """Generate labeled and unlabeled subsets based on the given percentage.
+
+        Parameters
+        ----------
+        - dataset: The dataset to split into subsets.
+        - percentage: The percentage of data to include in the labeled subset.
+        - num_classes: The number of classes in the dataset.
+        - u_per: The percentage of data to include in the unlabeled subset.
+        - seed: The seed for random operations to ensure reproducibility.
+
+        Returns
+        -------
+        - Two tuples: The labeled subset and the unlabeled subset.
+        """
+        # num_samples = DataTools.get_num_samples(dataset=dataset)
+        class_distribution = DataTools.get_class_distribution(dataset=dataset)
+        class_distribution = dict(sorted(class_distribution.items()))
+        class_samples = {k: int(percentage * v) for k, v in class_distribution.items()}
+        class_samples = dict(sorted(class_samples.items()))
+        dataset = list(zip(dataset[0], dataset[1]))
+        dataset_per_class = [
+            [s for s in dataset if s[1] == i] for i in range(num_classes)
+        ]
+        class_num_samples = list(class_samples.values())
+        # Get Subset of labelled samples
+        subset = list(
+            itertools.chain(
+                *[
+                    [
+                        dataset_per_class[i].pop(
+                            random.Random(seed).randrange(len(dataset_per_class[i]))
+                        )
+                        for _ in range(class_num_samples[i])
+                    ]
+                    for i in range(num_classes)
+                ]
+            )
+        )
+        subset_u = list(itertools.chain(*dataset_per_class))
+        random.Random(seed).shuffle(subset)
+        random.Random(seed).shuffle(subset_u)
+        subset_u = (
+            subset_u[0 : int((len(subset_u) - 1) * u_per)] if u_per < 1.0 else subset_u
+        )
+        return (subset, len(subset)), (subset_u, len(subset_u))
+
+    @staticmethod
+    def distribute_per_samples(dataset, num_clients, variance=0.25, seed=2021):
+        """Distribute dataset samples evenly among a specified number of clients.
+
+        Parameters
+        ----------
+        - dataset: The dataset to be distributed.
+        - num_clients: The number of clients among whom to distribute the dataset.
+        - variance: The variance allowed in the distribution of samples.
+        - seed: The seed for random operations to ensure reproducibility.
+
+        Returns
+        -------
+        - Generator yielding dataset subsets for each client.
+        """
+        dataset = tuple(list(t) for t in zip(*dataset))
+        num_samples = DataTools.get_num_samples(dataset=dataset)
+        _, _, limits, errors = DataTools.get_statistics(
+            num_samples=num_samples, num_clients=num_clients, var=variance
+        )
+        distribution = DataTools.create_distribution(
+            num_clients=num_clients,
+            num_samples=num_samples,
+            remain_samples=errors[0],
+            minimum=limits[0],
+            maximum=limits[1],
+            round_error=errors[1],
+            seed=seed,
+        )
+        samples, labels = dataset
+        dataset = DataTools.shuffle_dataset(dataset=dataset, seed=seed)
+        iter_samples, iter_labels = iter(samples), iter(labels)
+        for i in range(num_clients):
+            yield list(itertools.islice(iter_samples, distribution[i])), list(
+                itertools.islice(iter_labels, distribution[i])
+            )
+
+    # pylint: disable=too-many-locals
+    @staticmethod
+    def distribute_per_class(
+        dataset, num_clients, num_classes, class_variance=0.0, seed=2021
+    ):
+        """Distribute dataset based on class distribution among clients.
+
+        Parameters
+        ----------
+        - dataset: The dataset to be distributed.
+        - num_clients: The number of clients.
+        - num_classes: The number of classes in the dataset.
+        - class_variance: The variance in the number of classes per client.
+        - seed: The seed for random operations to ensure reproducibility.
+
+        Returns
+        -------
+        - A list of datasets for each client, distributed based on class.
+        """
+        dataset = tuple(list(t) for t in zip(*dataset))
+        (overlap, _num_classes_, _num_clients_) = (
+            (True, num_clients, num_classes)
+            if num_classes < num_clients
+            else (False, num_classes, num_clients)
+        )
+        _, _, limits, errors = DataTools.get_statistics(
+            num_samples=_num_classes_, num_clients=_num_clients_, var=class_variance
+        )
+        distribution = DataTools.create_distribution(
+            num_clients=_num_clients_,
+            num_samples=_num_classes_,
+            remain_samples=errors[0],
+            minimum=limits[0],
+            maximum=limits[1],
+            round_error=errors[1],
+            seed=seed,
+        )
+
+        if (
+            overlap
+        ):  # Clients will contain only one class and clients classes will overlap
+            distribution = list(
+                itertools.chain(
+                    *[[idx] * clients for idx, clients in enumerate(distribution)]
+                )
+            )
+            random.Random(seed).shuffle(distribution)
+        else:
+            classes = list(range(_num_classes_))
+            random.Random(seed).shuffle(classes)
+            _iter_ = iter(classes)
+
+            def distribute_fun():
+                return (
+                    yield from (
+                        list(itertools.islice(_iter_, distribution[i]))
+                        for i in range(_num_clients_)
+                    )
+                )
+
+            distribution = list(distribute_fun())
+        class_datasets = [
+            DataTools.get_dataset_class_samples(
+                dataset=dataset, class_number=class_number
+            )
+            for class_number in range(num_classes)
+        ]
+
+        # Distribute dataset to clients according to distribution
+        if overlap:
+            dataset_use = dict(sorted(collections.Counter(distribution).items()))
+            class_datasets = [
+                class_datasets[c]
+                if dataset_use[c] == 1
+                else DataTools.split_class_samples(class_datasets[c], dataset_use[c])
+                for c in range(num_classes)
+            ]
+            parts = list(
+                itertools.chain.from_iterable(
+                    [list(range(len(c))) for c in class_datasets if isinstance(c, list)]
+                )
+            )
+            _iter_ = iter(parts)
+            datasets = [
+                class_datasets[i]
+                if dataset_use[i] == 1
+                else class_datasets[i][next(_iter_)]
+                for i in distribution
+            ]
+            datasets = [DataTools.shuffle_dataset(dataset) for dataset in datasets]
+        else:
+            datasets = [
+                [class_datasets[class_number] for class_number in i]
+                for i in distribution
+            ]
+            datasets = [np.hstack((datasets[i])).tolist() for i in range(len(datasets))]
+            datasets = [tuple(data) for data in datasets]
+            datasets = [DataTools.shuffle_dataset(dataset) for dataset in datasets]
+
+        datasets = [
+            [dataset[0], [int(label) for label in dataset[1]]] for dataset in datasets
+        ]
+        return datasets
+
+    @staticmethod
+    def distribute_per_speaker(dataset, num_speakers, seed=2021):
+        """Distribute the dataset based on speaker identity.
+
+        Parameters
+        ----------
+        - dataset: The dataset to be distributed.
+        - num_speakers: The number of distinct speakers in the dataset.
+        - seed: The seed for random operations to ensure reproducibility.
+
+        Returns
+        -------
+        - A list of datasets, each corresponding to a different speaker.
+        """
+        random.seed(seed)
+        np.random.seed(seed)
+        dataset = tuple(list(t) for t in zip(*dataset))
+        datasets = [
+            [
+                (file, label)
+                for file, label in zip(dataset[0], dataset[1])
+                if int(((file.split("/")[-1]).split(".")[0]).split("-")[-1]) == i + 1
+            ]
+            for i in range(num_speakers)
+        ]
+        datasets = [list(zip(*dataset)) for dataset in datasets]
+        datasets = [(list(dataset[0]), list(dataset[1])) for dataset in datasets]
+        return datasets
+
+    # pylint: disable=too-many-arguments
+    @staticmethod
+    def distribute_per_class_with_class_limit(
+        dataset,
+        num_clients,
+        num_classes,
+        mean_class_distribution=3,
+        class_variance=0.0,
+        seed=2021,
+    ):
+        """Distribute the dataset with a limit on the number of classes per client.
+
+        Parameters
+        ----------
+        - dataset: The dataset to be distributed.
+        - num_clients: The number of clients.
+        - num_classes: The number of classes in the dataset.
+        - mean_class_distribution: The mean number of classes per client.
+        - class_variance: The variance in the number of classes per client.
+        - seed: The seed for random operations to ensure reproducibility.
+
+        Returns
+        -------
+        - A list of datasets for each client, with a controlled class distribution.
+        """
+        dataset = tuple(list(t) for t in zip(*dataset))
+        random.seed(seed)
+        np.random.seed(seed)
+        probability_distribution = np.random.normal(
+            mean_class_distribution, np.sqrt(class_variance), num_clients
+        )
+        probability_distribution = [int(p) for p in probability_distribution]
+        # Create class distribution across clients
+        distributions = [
+            list(np.random.choice(a=num_classes, size=p, replace=False))
+            for p in probability_distribution
+        ]
+        # Find number of clients that share dataset.
+        dataset_use = dict(
+            sorted(collections.Counter(list(itertools.chain(*distributions))).items())
+        )
+        # Make sure that all classes are used. Otherwise change seed.
+        assert (
+            len(dataset_use) == num_classes
+        ), "Not all classes were selected. Choose a different seed!"
+        # Split classes into clients partitions
+        class_datasets = [
+            DataTools.get_dataset_class_samples(
+                dataset=dataset, class_number=class_number
+            )
+            for class_number in range(num_classes)
+        ]
+        class_datasets = [
+            class_datasets[c]
+            if dataset_use[c] == 1
+            else DataTools.split_class_samples(class_datasets[c], dataset_use[c])
+            for c in range(num_classes)
+        ]
+        # Keep track of splitted partitions
+        parts = [list(np.arange(i)) for i in list(dataset_use.values())]
+        # Fill datasets with examples from class distribution.
+        datasets = [
+            [
+                class_datasets[j]
+                if (len(parts[j]) == 1 and isinstance(class_datasets[j], tuple))
+                else class_datasets[j][parts[j].pop()]
+                for j in dis
+            ]
+            for dis in distributions
+        ]
+        # Flatten clients datasets to ([samples],[labels]) format.
+        datasets = [
+            [
+                list(itertools.chain(*list(zip(*d))[0])),
+                list(itertools.chain(*list(zip(*d))[1])),
+            ]
+            for d in datasets
+        ]
+        # Shuffle each dataset.
+        datasets = [DataTools.shuffle_dataset(d) for d in datasets]
+        return datasets
+
+    # pylint: disable=too-many-arguments
+    @staticmethod
+    def create_distribution(
+        num_clients,
+        num_samples,
+        remain_samples,
+        minimum,
+        maximum,
+        round_error,
+        seed=2021,
+    ):
+        """Create a distribution plan for distributing samples among clients.
+
+        Parameters
+        ----------
+        - num_clients: The number of clients.
+        - num_samples: The total number of samples to distribute.
+        - remain_samples: Remaining samples to distribute after initial division.
+        - minimum: The minimum number of samples per client.
+        - maximum: The maximum number of samples per client.
+        - round_error: The error due to rounding in distribution.
+        - seed: The seed for random operations to ensure reproducibility.
+
+        Returns
+        -------
+        - A list representing the distribution of samples among clients.
+        """
+        distribution = [minimum] * num_clients
+        random.seed(seed)
+        while remain_samples > 0:
+            idx = random.randint(0, len(distribution) - 1)
+            distribution[idx], remain_samples = (
+                (distribution[idx] + 1, remain_samples - 1)
+                if distribution[idx] < maximum
+                else (distribution[idx], remain_samples)
+            )
+        # Add error due to rounding to random positions.
+        for _i in range(round(round_error)):
+            idx = random.randint(0, len(distribution) - 1)
+            distribution[idx] = (
+                distribution[idx] + 1
+                if sum(distribution) + 1 <= num_samples
+                else distribution[idx]
+            )
+        return distribution
+
+    @staticmethod
+    def convert_to_unlabelled(dataset, unlabelled_data_identifier=-1):
+        """Convert a labeled dataset to an unlabeled one by replacing labels.
+
+        Replaces the labels in the dataset with a specified identifier, effectively
+        converting it to an unlabeled dataset. This is useful in semi-supervised
+        learning scenarios.
+
+        Parameters
+        ----------
+        - dataset: The dataset to be converted, consisting of data and labels.
+        - unlabelled_data_identifier: The identifier to replace the labels with,
+        defaulting to -1.
+
+        Returns
+        -------
+        - A list of tuples, where each tuple contains data and unlabelled identifier.
+        """
+        return [(data[0], unlabelled_data_identifier) for data in dataset]
+
+    @staticmethod
+    def shuffle_dataset(dataset, seed=2021):
+        """Shuffle a dataset in a reproducible manner.
+
+        Randomly shuffles the dataset using a specified seed to ensure the shuffle
+        can be reproduced.
+
+        Parameters
+        ----------
+        - dataset: The dataset to shuffle, consisting of data and labels.
+        - seed: The seed for the random number generator to ensure reproducibility,
+        defaulting to 2021.
+
+        Returns
+        -------
+        - The shuffled dataset as a tuple of two lists, one for data and one for labels.
+        """
+        random.seed(seed)
+        dataset = list(zip(dataset[0], dataset[1]))
+        random.shuffle(dataset)
+        return tuple(list(t) for t in zip(*dataset))
diff --git a/baselines/fedstar/pyproject.toml b/baselines/fedstar/pyproject.toml
new file mode 100644
index 000000000000..b7109cdc7232
--- /dev/null
+++ b/baselines/fedstar/pyproject.toml
@@ -0,0 +1,141 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.masonry.api"
+
+[tool.poetry]
+name = "fedstar" # <----- Ensure it matches the name of your baseline directory containing all the source code
+version = "1.0.0"
+description = "Federated Self-training for Semi-supervised Audio Recognition"
+license = "Apache-2.0"
+authors = ["Raj Parekh <rajparekhwc@gmail.com>", "The Flower Authors <hello@flower.dev>>"]
+readme = "README.md"
+homepage = "https://flower.dev"
+repository = "https://github.com/adap/flower"
+documentation = "https://flower.dev"
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: MacOS :: MacOS X",
+    "Operating System :: POSIX :: Linux",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: Implementation :: CPython",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Scientific/Engineering :: Mathematics",
+    "Topic :: Software Development",
+    "Topic :: Software Development :: Libraries",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+    "Typing :: Typed",
+]
+
+[tool.poetry.dependencies]
+python = ">=3.10.0, <3.11.0"  # don't change this
+flwr = "1.5.0" # don't change this
+hydra-core = "1.3.2" # don't change this
+tensorflow = { extras = ["and-cuda"], version = "2.14.0"}
+pandas = "2.1.1"
+scipy = "^1.11.4"
+tqdm = "^4.66.1"
+
+[tool.poetry.dev-dependencies]
+isort = "==5.11.5"
+black = "==23.1.0"
+docformatter = "==1.5.1"
+mypy = "==1.4.1"
+pylint = "==3.0.3"
+flake8 = "==3.9.2"
+pytest = "==6.2.4"
+pytest-watch = "==4.2.0"
+ruff = "==0.0.272"
+types-requests = "==2.27.7"
+
+[tool.isort]
+line_length = 88
+indent = "    "
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+use_parentheses = true
+
+[tool.black]
+line-length = 88
+target-version = ["py38", "py39", "py310", "py311"]
+
+[tool.pytest.ini_options]
+minversion = "6.2"
+addopts = "-qq"
+testpaths = [
+    "flwr_baselines",
+]
+
+[tool.mypy]
+ignore_missing_imports = true
+strict = false
+plugins = "numpy.typing.mypy_plugin"
+
+[tool.pylint."MESSAGES CONTROL"]
+disable = "duplicate-code,too-few-public-methods,useless-import-alias"
+good-names = "i,j,k,_,x,y,X,Y"
+signature-mutators="hydra.main.main"
+
+[tool.pylint.typecheck]
+generated-members="numpy.*, torch.*, tensorflow.*"
+
+[[tool.mypy.overrides]]
+module = [
+    "importlib.metadata.*",
+    "importlib_metadata.*",
+]
+follow_imports = "skip"
+follow_imports_for_stubs = true
+disallow_untyped_calls = false
+
+[[tool.mypy.overrides]]
+module = "torch.*"
+follow_imports = "skip"
+follow_imports_for_stubs = true
+
+[tool.docformatter]
+wrap-summaries = 88
+wrap-descriptions = 88
+
+[tool.ruff]
+target-version = "py38"
+line-length = 88
+select = ["D", "E", "F", "W", "B", "ISC", "C4"]
+fixable = ["D", "E", "F", "W", "B", "ISC", "C4"]
+ignore = ["B024", "B027"]
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+    "proto",
+]
+
+[tool.ruff.pydocstyle]
+convention = "numpy"
diff --git a/baselines/fedstar/setup_datasets.sh b/baselines/fedstar/setup_datasets.sh
new file mode 100644
index 000000000000..cd9583983eb3
--- /dev/null
+++ b/baselines/fedstar/setup_datasets.sh
@@ -0,0 +1,42 @@
+#!/bin/bash
+
+# Extract the data_splits.tar file
+tar -xvf data_splits.tar
+
+# Create datasets directory and navigate into it
+mkdir datasets
+cd datasets
+
+# Create speech_commands directory and navigate into it
+mkdir speech_commands
+cd speech_commands
+
+# Download and extract the training data
+wget http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz -O train.tar.gz
+mkdir -p ./Data/Train
+tar -xvzf ./train.tar.gz -C ./Data/Train
+
+# Download and extract the testing data
+wget http://download.tensorflow.org/data/speech_commands_test_set_v0.02.tar.gz -O test.tar.gz
+mkdir -p ./Data/Test
+tar -xvzf ./test.tar.gz -C ./Data/Test
+
+# # Rename the _background_noise_ directory to _silence_
+mv ./Data/Train/_background_noise_ ./Data/Train/_silence_
+
+cd ..
+# Create speech_commands directory and navigate into it
+mkdir ambient_context
+cd ambient_context
+
+wget http://sensix.tech/datasets/ambientacousticcontext/audioset_1sec_v1.tar.gz -O ambient_data.tar.gz
+mkdir -p ./Data
+mkdir -p ./temp_data
+tar -xvzf ./ambient_data.tar.gz -C ./temp_data
+mv ./temp_data/audioset_1sec/* ./Data
+rm -r ./temp_data
+cd ..
+cd ..
+
+# Print completion message
+echo "Dataset setup completed."
\ No newline at end of file

From 29dfaff7be28487f0de7933cf07502abd16e01e4 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 20 Feb 2024 08:35:12 +0000
Subject: [PATCH 031/102] Bump actions/cache from 3 to 4 (#2836)

---
 .github/workflows/cpp.yml | 8 ++++----
 .github/workflows/e2e.yml | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/cpp.yml b/.github/workflows/cpp.yml
index a1d918aa5b29..7efc879bcee2 100644
--- a/.github/workflows/cpp.yml
+++ b/.github/workflows/cpp.yml
@@ -19,13 +19,13 @@ jobs:
       uses: ./.github/actions/bootstrap
 
     - name: Cache restore SDK build
-      uses: actions/cache/restore@v3
+      uses: actions/cache/restore@v4
       with:
         path: build/
         key: ${{ runner.os }}-sdk-build
 
     - name: Cache restore example build
-      uses: actions/cache/restore@v3
+      uses: actions/cache/restore@v4
       with:
         path: examples/quickstart-cpp/build/
         key: ${{ runner.os }}-example-build
@@ -82,14 +82,14 @@ jobs:
         fi
 
     - name: Cache save SDK build
-      uses: actions/cache/save@v3
+      uses: actions/cache/save@v4
       if: github.ref_name == 'main'
       with:
         path: build/
         key: ${{ runner.os }}-sdk-build
 
     - name: Cache save example build
-      uses: actions/cache/save@v3
+      uses: actions/cache/save@v4
       if: github.ref_name == 'main'
       with:
         path: examples/quickstart-cpp/build/
diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml
index c89896dd9d6b..db9f65a4f4f3 100644
--- a/.github/workflows/e2e.yml
+++ b/.github/workflows/e2e.yml
@@ -167,7 +167,7 @@ jobs:
         run: |
           python -m pip install https://${{ env.ARTIFACT_BUCKET }}/py/${{ needs.wheel.outputs.dir }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Cache Datasets
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         with:
           path: "~/.keras"
           key: keras-datasets

From 8433a07eda90a9871d9ff385b861fb9289e60772 Mon Sep 17 00:00:00 2001
From: Robert Steiner <robert@flower.dev>
Date: Tue, 20 Feb 2024 10:08:01 +0100
Subject: [PATCH 032/102] format baselines (#2960)

---
 baselines/fedbn/docs/multirun_plot.ipynb      | 50 ++++++++++---------
 baselines/hfedxgboost/hfedxgboost/__init__.py |  2 +-
 baselines/hfedxgboost/hfedxgboost/strategy.py |  3 +-
 3 files changed, 29 insertions(+), 26 deletions(-)

diff --git a/baselines/fedbn/docs/multirun_plot.ipynb b/baselines/fedbn/docs/multirun_plot.ipynb
index 6314e0033b9d..2660d1cc4e19 100644
--- a/baselines/fedbn/docs/multirun_plot.ipynb
+++ b/baselines/fedbn/docs/multirun_plot.ipynb
@@ -58,12 +58,12 @@
    "source": [
     "def fuse_by_dataset(losses):\n",
     "    \"\"\"Transform per-round history (list of dicts) into\n",
-    "    \n",
+    "\n",
     "    a single dict, with values as lists.\"\"\"\n",
     "    fussed_losses = {}\n",
     "\n",
     "    for _, loss_dict in losses:\n",
-    "        for k,v in loss_dict.items():\n",
+    "        for k, v in loss_dict.items():\n",
     "            if k in fussed_losses:\n",
     "                fussed_losses[k].append(v)\n",
     "            else:\n",
@@ -82,9 +82,14 @@
     "    res_list = []\n",
     "    for results in list(Path(path_multirun).glob(\"**/history.pkl\")):\n",
     "        data, config = read_pickle_and_config(results)\n",
-    "        pre_train_loss = data['history'].metrics_distributed_fit['pre_train_losses']\n",
+    "        pre_train_loss = data[\"history\"].metrics_distributed_fit[\"pre_train_losses\"]\n",
     "        fussed_losses = fuse_by_dataset(pre_train_loss)\n",
-    "        res_list.append({'strategy': config['client']['client_label'], 'train_losses': fussed_losses})\n",
+    "        res_list.append(\n",
+    "            {\n",
+    "                \"strategy\": config[\"client\"][\"client_label\"],\n",
+    "                \"train_losses\": fussed_losses,\n",
+    "            }\n",
+    "        )\n",
     "    return res_list"
    ]
   },
@@ -95,7 +100,7 @@
    "outputs": [],
    "source": [
     "# Here replace with the path of the multi run you just comleted\n",
-    "all_losses = process_multirun_data('../multirun/2023-11-15/22-28-28')"
+    "all_losses = process_multirun_data(\"../multirun/2023-11-15/22-28-28\")"
    ]
   },
   {
@@ -104,30 +109,29 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "def average_by_client_type(all_fused_lossed):\n",
     "    \"\"\"If there are multliple runs for the same strategy add them up,\n",
-    "    \n",
+    "\n",
     "    average them later. This is useful if you run the `--multirun` running\n",
     "    more than one time the same configuration.\"\"\"\n",
-    "    \n",
+    "\n",
     "    # identify how many unique clients were used\n",
     "    to_plot = {}\n",
     "    for res in all_fused_lossed:\n",
-    "        strategy = res['strategy']\n",
+    "        strategy = res[\"strategy\"]\n",
     "        if strategy not in to_plot:\n",
     "            to_plot[strategy] = {}\n",
-    "        \n",
-    "        for dataset, train_loss in res['train_losses'].items():\n",
+    "\n",
+    "        for dataset, train_loss in res[\"train_losses\"].items():\n",
     "            if dataset in to_plot[strategy]:\n",
-    "                to_plot[strategy][dataset]['train_loss'] += np.array(train_loss)\n",
-    "                to_plot[strategy][dataset]['run_count'] += 1\n",
+    "                to_plot[strategy][dataset][\"train_loss\"] += np.array(train_loss)\n",
+    "                to_plot[strategy][dataset][\"run_count\"] += 1\n",
     "            else:\n",
-    "                to_plot[strategy][dataset] = {'train_loss': np.array(train_loss)}\n",
-    "                to_plot[strategy][dataset]['run_count'] = 1\n",
+    "                to_plot[strategy][dataset] = {\"train_loss\": np.array(train_loss)}\n",
+    "                to_plot[strategy][dataset][\"run_count\"] = 1\n",
     "\n",
     "    # print(to_plot)\n",
-    "    return to_plot\n"
+    "    return to_plot"
    ]
   },
   {
@@ -167,25 +171,25 @@
     "print(datasets)\n",
     "\n",
     "num_datasets = len(datasets)\n",
-    "fig, axs = plt.subplots(figsize=(14,4), nrows=1, ncols=num_datasets)\n",
+    "fig, axs = plt.subplots(figsize=(14, 4), nrows=1, ncols=num_datasets)\n",
     "\n",
     "\n",
     "for s_id, (strategy, results) in enumerate(to_plot.items()):\n",
     "    for i, dataset in enumerate(datasets):\n",
-    "        loss = results[dataset]['train_loss']/results[dataset]['run_count']\n",
+    "        loss = results[dataset][\"train_loss\"] / results[dataset][\"run_count\"]\n",
     "        axs[i].plot(range(len(loss)), loss, label=strategy)\n",
-    "        axs[i].set_xlabel('Round')\n",
+    "        axs[i].set_xlabel(\"Round\")\n",
     "        if i == 0:\n",
-    "            axs[i].set_ylabel('Train Loss')\n",
+    "            axs[i].set_ylabel(\"Train Loss\")\n",
     "\n",
     "        axs[i].legend()\n",
     "\n",
-    "        if s_id==0:\n",
+    "        if s_id == 0:\n",
     "            axs[i].grid()\n",
     "            axs[i].set_title(dataset)\n",
-    "            axs[i].set_xticks(np.arange(0,100+1, 25))\n",
+    "            axs[i].set_xticks(np.arange(0, 100 + 1, 25))\n",
     "\n",
-    "saveFig('train_loss.png', fig)"
+    "saveFig(\"train_loss.png\", fig)"
    ]
   },
   {
diff --git a/baselines/hfedxgboost/hfedxgboost/__init__.py b/baselines/hfedxgboost/hfedxgboost/__init__.py
index 543147a05591..ce8299b55c1a 100644
--- a/baselines/hfedxgboost/hfedxgboost/__init__.py
+++ b/baselines/hfedxgboost/hfedxgboost/__init__.py
@@ -1 +1 @@
-"""hfedxgboost baseline package."""
+"""Hfedxgboost baseline package."""
diff --git a/baselines/hfedxgboost/hfedxgboost/strategy.py b/baselines/hfedxgboost/hfedxgboost/strategy.py
index eb067a89e5f0..b8e9c6704603 100644
--- a/baselines/hfedxgboost/hfedxgboost/strategy.py
+++ b/baselines/hfedxgboost/hfedxgboost/strategy.py
@@ -9,9 +9,8 @@
 from flwr.common import FitRes, Scalar, ndarrays_to_parameters, parameters_to_ndarrays
 from flwr.common.logger import log
 from flwr.server.client_proxy import ClientProxy
-
-from flwr.server.strategy.aggregate import aggregate
 from flwr.server.strategy import FedAvg
+from flwr.server.strategy.aggregate import aggregate
 
 
 class FedXgbNnAvg(FedAvg):

From 538908bbf632f75d5f060fb299dcbee709d300ad Mon Sep 17 00:00:00 2001
From: Taner Topal <taner@flower.dev>
Date: Tue, 20 Feb 2024 12:25:04 +0100
Subject: [PATCH 033/102] Add `flwr` CLI (#2942)

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
Co-authored-by: Daniel J. Beutel <daniel@flower.ai>
---
 pyproject.toml                                |   2 +
 src/py/flwr/cli/__init__.py                   |  15 ++
 src/py/flwr/cli/app.py                        |  35 +++++
 src/py/flwr/cli/example.py                    |  64 +++++++++
 src/py/flwr/cli/new/__init__.py               |  21 +++
 src/py/flwr/cli/new/new.py                    | 130 ++++++++++++++++++
 src/py/flwr/cli/new/new_test.py               |  93 +++++++++++++
 src/py/flwr/cli/new/templates/__init__.py     |  15 ++
 .../flwr/cli/new/templates/app/README.md.tpl  |  33 +++++
 src/py/flwr/cli/new/templates/app/__init__.py |  15 ++
 .../cli/new/templates/app/code/__init__.py    |  15 ++
 .../new/templates/app/code/__init__.py.tpl    |   1 +
 .../templates/app/code/client.pytorch.py.tpl  |   1 +
 .../app/code/client.tensorflow.py.tpl         |   1 +
 .../templates/app/code/server.pytorch.py.tpl  |   1 +
 .../app/code/server.tensorflow.py.tpl         |   1 +
 .../cli/new/templates/app/flower.toml.tpl     |  10 ++
 .../app/requirements.pytorch.txt.tpl          |   4 +
 .../app/requirements.tensorflow.txt.tpl       |   4 +
 src/py/flwr/cli/utils.py                      |  54 ++++++++
 20 files changed, 515 insertions(+)
 create mode 100644 src/py/flwr/cli/__init__.py
 create mode 100644 src/py/flwr/cli/app.py
 create mode 100644 src/py/flwr/cli/example.py
 create mode 100644 src/py/flwr/cli/new/__init__.py
 create mode 100644 src/py/flwr/cli/new/new.py
 create mode 100644 src/py/flwr/cli/new/new_test.py
 create mode 100644 src/py/flwr/cli/new/templates/__init__.py
 create mode 100644 src/py/flwr/cli/new/templates/app/README.md.tpl
 create mode 100644 src/py/flwr/cli/new/templates/app/__init__.py
 create mode 100644 src/py/flwr/cli/new/templates/app/code/__init__.py
 create mode 100644 src/py/flwr/cli/new/templates/app/code/__init__.py.tpl
 create mode 100644 src/py/flwr/cli/new/templates/app/code/client.pytorch.py.tpl
 create mode 100644 src/py/flwr/cli/new/templates/app/code/client.tensorflow.py.tpl
 create mode 100644 src/py/flwr/cli/new/templates/app/code/server.pytorch.py.tpl
 create mode 100644 src/py/flwr/cli/new/templates/app/code/server.tensorflow.py.tpl
 create mode 100644 src/py/flwr/cli/new/templates/app/flower.toml.tpl
 create mode 100644 src/py/flwr/cli/new/templates/app/requirements.pytorch.txt.tpl
 create mode 100644 src/py/flwr/cli/new/templates/app/requirements.tensorflow.txt.tpl
 create mode 100644 src/py/flwr/cli/utils.py

diff --git a/pyproject.toml b/pyproject.toml
index 297574ef67ed..065d5bc1f216 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -52,6 +52,7 @@ exclude = [
 ]
 
 [tool.poetry.scripts]
+flwr = "flwr.cli.app:app"
 flower-driver-api = "flwr.server:run_driver_api"
 flower-fleet-api = "flwr.server:run_fleet_api"
 flower-superlink = "flwr.server:run_superlink"
@@ -67,6 +68,7 @@ protobuf = "^4.25.2"
 cryptography = "^41.0.2"
 pycryptodome = "^3.18.0"
 iterators = "^0.0.2"
+typer = { version = "^0.9.0", extras=["all"] }
 # Optional dependencies (VCE)
 ray = { version = "==2.6.3", optional = true }
 pydantic = { version = "<2.0.0", optional = true }
diff --git a/src/py/flwr/cli/__init__.py b/src/py/flwr/cli/__init__.py
new file mode 100644
index 000000000000..d4d3b8ac4d48
--- /dev/null
+++ b/src/py/flwr/cli/__init__.py
@@ -0,0 +1,15 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Flower command line interface."""
diff --git a/src/py/flwr/cli/app.py b/src/py/flwr/cli/app.py
new file mode 100644
index 000000000000..dc390de03547
--- /dev/null
+++ b/src/py/flwr/cli/app.py
@@ -0,0 +1,35 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Flower command line interface."""
+
+import typer
+
+from .example import example
+from .new import new
+
+app = typer.Typer(
+    help=typer.style(
+        "flwr is the Flower command line interface.",
+        fg=typer.colors.BRIGHT_YELLOW,
+        bold=True,
+    ),
+    no_args_is_help=True,
+)
+
+app.command()(new)
+app.command()(example)
+
+if __name__ == "__main__":
+    app()
diff --git a/src/py/flwr/cli/example.py b/src/py/flwr/cli/example.py
new file mode 100644
index 000000000000..625ca8729640
--- /dev/null
+++ b/src/py/flwr/cli/example.py
@@ -0,0 +1,64 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Flower command line interface `example` command."""
+
+import json
+import os
+import subprocess
+import tempfile
+import urllib.request
+
+from .utils import prompt_options
+
+
+def example() -> None:
+    """Clone a Flower example.
+
+    All examples available in the Flower repository are available through this command.
+    """
+    # Load list of examples directly from GitHub
+    url = "https://api.github.com/repos/adap/flower/git/trees/main"
+    with urllib.request.urlopen(url) as res:
+        data = json.load(res)
+        examples_directory_url = [
+            item["url"] for item in data["tree"] if item["path"] == "examples"
+        ][0]
+
+    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"]
+        ]
+
+    example_name = prompt_options(
+        "Please select example by typing in the number",
+        example_names,
+    )
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        subprocess.check_output(
+            [
+                "git",
+                "clone",
+                "--depth=1",
+                "https://github.com/adap/flower.git",
+                tmpdirname,
+            ]
+        )
+        examples_dir = os.path.join(tmpdirname, "examples", example_name)
+        subprocess.check_output(["mv", examples_dir, "."])
+
+        print()
+        print(f"Example ready to use in {os.path.join(os.getcwd(), example_name)}")
diff --git a/src/py/flwr/cli/new/__init__.py b/src/py/flwr/cli/new/__init__.py
new file mode 100644
index 000000000000..a973f47021c3
--- /dev/null
+++ b/src/py/flwr/cli/new/__init__.py
@@ -0,0 +1,21 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Flower command line interface `new` command."""
+
+from .new import new as new
+
+__all__ = [
+    "new",
+]
diff --git a/src/py/flwr/cli/new/new.py b/src/py/flwr/cli/new/new.py
new file mode 100644
index 000000000000..d5db6091344d
--- /dev/null
+++ b/src/py/flwr/cli/new/new.py
@@ -0,0 +1,130 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Flower command line interface `new` command."""
+
+import os
+from enum import Enum
+from string import Template
+from typing import Dict, Optional
+
+import typer
+from typing_extensions import Annotated
+
+from ..utils import prompt_options
+
+
+class MlFramework(str, Enum):
+    """Available frameworks."""
+
+    PYTORCH = "PyTorch"
+    TENSORFLOW = "TensorFlow"
+
+
+class TemplateNotFound(Exception):
+    """Raised when template does not exist."""
+
+
+def load_template(name: str) -> str:
+    """Load template from template directory and return as text."""
+    tpl_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "templates"))
+    tpl_file_path = os.path.join(tpl_dir, name)
+
+    if not os.path.isfile(tpl_file_path):
+        raise TemplateNotFound(f"Template '{name}' not found")
+
+    with open(tpl_file_path, encoding="utf-8") as tpl_file:
+        return tpl_file.read()
+
+
+def render_template(template: str, data: Dict[str, str]) -> str:
+    """Render template."""
+    tpl_file = load_template(template)
+    tpl = Template(tpl_file)
+    result = tpl.substitute(data)
+    return result
+
+
+def create_file(file_path: str, content: str) -> None:
+    """Create file including all nessecary directories and write content into file."""
+    os.makedirs(os.path.dirname(file_path), exist_ok=True)
+    with open(file_path, "w", encoding="utf-8") as f:
+        f.write(content)
+
+
+def render_and_create(file_path: str, template: str, context: Dict[str, str]) -> None:
+    """Render template and write to file."""
+    content = render_template(template, context)
+    create_file(file_path, content)
+
+
+def new(
+    project_name: Annotated[
+        str,
+        typer.Argument(metavar="project_name", help="The name of the project"),
+    ],
+    framework: Annotated[
+        Optional[MlFramework],
+        typer.Option(case_sensitive=False, help="The ML framework to use"),
+    ] = None,
+) -> None:
+    """Create new Flower project."""
+    print(f"Creating Flower project {project_name}...")
+
+    if framework is not None:
+        framework_str = str(framework.value)
+    else:
+        framework_value = prompt_options(
+            "Please select ML framework by typing in the number",
+            [mlf.value for mlf in MlFramework],
+        )
+        selected_value = [
+            name
+            for name, value in vars(MlFramework).items()
+            if value == framework_value
+        ]
+        framework_str = selected_value[0]
+
+    # Set project directory path
+    cwd = os.getcwd()
+    pnl = project_name.lower()
+    project_dir = os.path.join(cwd, pnl)
+
+    # List of files to render
+    files = {
+        "README.md": {
+            "template": "app/README.md.tpl",
+        },
+        "requirements.txt": {
+            "template": f"app/requirements.{framework_str.lower()}.txt.tpl"
+        },
+        "flower.toml": {"template": "app/flower.toml.tpl"},
+        f"{pnl}/__init__.py": {"template": "app/code/__init__.py.tpl"},
+        f"{pnl}/server.py": {
+            "template": f"app/code/server.{framework_str.lower()}.py.tpl"
+        },
+        f"{pnl}/client.py": {
+            "template": f"app/code/client.{framework_str.lower()}.py.tpl"
+        },
+    }
+    context = {"project_name": project_name}
+
+    for file_path, value in files.items():
+        render_and_create(
+            file_path=os.path.join(project_dir, file_path),
+            template=value["template"],
+            context=context,
+        )
+
+    print("Project creation successful.")
diff --git a/src/py/flwr/cli/new/new_test.py b/src/py/flwr/cli/new/new_test.py
new file mode 100644
index 000000000000..39717bc67ab3
--- /dev/null
+++ b/src/py/flwr/cli/new/new_test.py
@@ -0,0 +1,93 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test for Flower command line interface `new` command."""
+
+import os
+
+from .new import MlFramework, create_file, load_template, new, render_template
+
+
+def test_load_template() -> None:
+    """Test if load_template returns a string."""
+    # Prepare
+    filename = "app/README.md.tpl"
+
+    # Execute
+    text = load_template(filename)
+
+    # Assert
+    assert isinstance(text, str)
+
+
+def test_render_template() -> None:
+    """Test if a string is correctly substituted."""
+    # Prepare
+    filename = "app/README.md.tpl"
+    data = {"project_name": "FedGPT"}
+
+    # Execute
+    result = render_template(filename, data)
+
+    # Assert
+    assert "# FedGPT" in result
+
+
+def test_create_file(tmp_path: str) -> None:
+    """Test if file with content is created."""
+    # Prepare
+    file_path = os.path.join(tmp_path, "test.txt")
+    content = "Foobar"
+
+    # Execute
+    create_file(file_path, content)
+
+    # Assert
+    with open(file_path, encoding="utf-8") as f:
+        text = f.read()
+
+    assert text == "Foobar"
+
+
+def test_new(tmp_path: str) -> None:
+    """Test if project is created for framework."""
+    # Prepare
+    project_name = "FedGPT"
+    framework = MlFramework.PYTORCH
+    expected_files_top_level = {
+        "requirements.txt",
+        "fedgpt",
+        "README.md",
+        "flower.toml",
+    }
+    expected_files_module = {
+        "__init__.py",
+        "server.py",
+        "client.py",
+    }
+
+    # Change into the temprorary directory
+    os.chdir(tmp_path)
+
+    # Execute
+    new(project_name=project_name, framework=framework)
+
+    # Assert
+    file_list = os.listdir(os.path.join(tmp_path, project_name.lower()))
+    assert set(file_list) == expected_files_top_level
+
+    file_list = os.listdir(
+        os.path.join(tmp_path, project_name.lower(), project_name.lower())
+    )
+    assert set(file_list) == expected_files_module
diff --git a/src/py/flwr/cli/new/templates/__init__.py b/src/py/flwr/cli/new/templates/__init__.py
new file mode 100644
index 000000000000..7a951c2da1a2
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/__init__.py
@@ -0,0 +1,15 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Flower CLI `new` command templates."""
diff --git a/src/py/flwr/cli/new/templates/app/README.md.tpl b/src/py/flwr/cli/new/templates/app/README.md.tpl
new file mode 100644
index 000000000000..7904fa8d3a3c
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/README.md.tpl
@@ -0,0 +1,33 @@
+# $project_name
+
+## Install dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+## Start the SuperLink
+
+```bash
+flower-superlink --insecure
+```
+
+## Start the long-running Flower client
+
+In a new terminal window, start the first long-running Flower client:
+
+```bash
+flower-client-app client:app --insecure
+```
+
+In yet another new terminal window, start the second long-running Flower client:
+
+```bash
+flower-client-app client:app --insecure
+```
+
+## Start the ServerApp
+
+```bash
+flower-server-app server:app --insecure
+```
diff --git a/src/py/flwr/cli/new/templates/app/__init__.py b/src/py/flwr/cli/new/templates/app/__init__.py
new file mode 100644
index 000000000000..617628fc9138
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/__init__.py
@@ -0,0 +1,15 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Flower CLI `new` command app templates."""
diff --git a/src/py/flwr/cli/new/templates/app/code/__init__.py b/src/py/flwr/cli/new/templates/app/code/__init__.py
new file mode 100644
index 000000000000..7f1a0e9f4fa2
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/__init__.py
@@ -0,0 +1,15 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Flower CLI `new` command app / code templates."""
diff --git a/src/py/flwr/cli/new/templates/app/code/__init__.py.tpl b/src/py/flwr/cli/new/templates/app/code/__init__.py.tpl
new file mode 100644
index 000000000000..57998c81efb8
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/__init__.py.tpl
@@ -0,0 +1 @@
+"""$project_name."""
diff --git a/src/py/flwr/cli/new/templates/app/code/client.pytorch.py.tpl b/src/py/flwr/cli/new/templates/app/code/client.pytorch.py.tpl
new file mode 100644
index 000000000000..006d00f75e40
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/client.pytorch.py.tpl
@@ -0,0 +1 @@
+"""$project_name: A Flower / PyTorch app."""
diff --git a/src/py/flwr/cli/new/templates/app/code/client.tensorflow.py.tpl b/src/py/flwr/cli/new/templates/app/code/client.tensorflow.py.tpl
new file mode 100644
index 000000000000..cc00f8ff0b8c
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/client.tensorflow.py.tpl
@@ -0,0 +1 @@
+"""$project_name: A Flower / TensorFlow app."""
diff --git a/src/py/flwr/cli/new/templates/app/code/server.pytorch.py.tpl b/src/py/flwr/cli/new/templates/app/code/server.pytorch.py.tpl
new file mode 100644
index 000000000000..006d00f75e40
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/server.pytorch.py.tpl
@@ -0,0 +1 @@
+"""$project_name: A Flower / PyTorch app."""
diff --git a/src/py/flwr/cli/new/templates/app/code/server.tensorflow.py.tpl b/src/py/flwr/cli/new/templates/app/code/server.tensorflow.py.tpl
new file mode 100644
index 000000000000..cc00f8ff0b8c
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/server.tensorflow.py.tpl
@@ -0,0 +1 @@
+"""$project_name: A Flower / TensorFlow app."""
diff --git a/src/py/flwr/cli/new/templates/app/flower.toml.tpl b/src/py/flwr/cli/new/templates/app/flower.toml.tpl
new file mode 100644
index 000000000000..4dd7117bc3a3
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/flower.toml.tpl
@@ -0,0 +1,10 @@
+[flower]
+name = "$project_name"
+version = "1.0.0"
+description = ""
+license = "Apache-2.0"
+authors = ["The Flower Authors <hello@flower.ai>"]
+
+[components]
+serverapp = "$project_name.server:app"
+clientapp = "$project_name.client:app"
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
new file mode 100644
index 000000000000..d9426e0b62c0
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/requirements.pytorch.txt.tpl
@@ -0,0 +1,4 @@
+flwr>=1.8, <2.0
+flwr-datasets[vision]>=0.0.2, <1.0.0
+torch==1.13.1
+torchvision==0.14.1
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
new file mode 100644
index 000000000000..4fe7bfdc1e89
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/requirements.tensorflow.txt.tpl
@@ -0,0 +1,4 @@
+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
new file mode 100644
index 000000000000..d61189ffc4e3
--- /dev/null
+++ b/src/py/flwr/cli/utils.py
@@ -0,0 +1,54 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Flower command line interface utils."""
+
+from typing import List
+
+import typer
+
+
+def prompt_options(text: str, options: List[str]) -> str:
+    """Ask user to select one of the given options and return the selected item."""
+    # Turn options into a list with index as in " [ 0] quickstart-pytorch"
+    options_formatted = [
+        " [ "
+        + typer.style(index, fg=typer.colors.GREEN, bold=True)
+        + "]"
+        + f" {typer.style(name, fg=typer.colors.WHITE, bold=True)}"
+        for index, name in enumerate(options)
+    ]
+
+    while True:
+        index = typer.prompt(
+            "\n"
+            + typer.style(f"💬 {text}", fg=typer.colors.MAGENTA, bold=True)
+            + "\n\n"
+            + "\n".join(options_formatted)
+            + "\n\n\n"
+        )
+        try:
+            options[int(index)]  # pylint: disable=expression-not-assigned
+            break
+        except IndexError:
+            print(typer.style("❌ Index out of range", fg=typer.colors.RED, bold=True))
+            continue
+        except ValueError:
+            print(
+                typer.style("❌ Please choose a number", fg=typer.colors.RED, bold=True)
+            )
+            continue
+
+    result = options[int(index)]
+    return result

From 7056c7eccff6aa69001ff1a692e88ceb887cf7d9 Mon Sep 17 00:00:00 2001
From: Robert Steiner <robert@flower.dev>
Date: Tue, 20 Feb 2024 13:49:09 +0100
Subject: [PATCH 034/102] pin virtualenv version to 20.21.0 in baselines
 (#2981)

---
 baselines/dasha/pyproject.toml       | 1 +
 baselines/depthfl/pyproject.toml     | 4 ++--
 baselines/fedavgm/pyproject.toml     | 3 ++-
 baselines/fedmeta/pyproject.toml     | 2 +-
 baselines/fedmlb/pyproject.toml      | 1 +
 baselines/fedper/pyproject.toml      | 2 +-
 baselines/fedprox/pyproject.toml     | 1 +
 baselines/fedwav2vec2/pyproject.toml | 1 +
 baselines/fjord/pyproject.toml       | 3 +--
 baselines/moon/pyproject.toml        | 1 +
 baselines/niid_bench/pyproject.toml  | 1 +
 baselines/tamuna/pyproject.toml      | 1 +
 12 files changed, 14 insertions(+), 7 deletions(-)

diff --git a/baselines/dasha/pyproject.toml b/baselines/dasha/pyproject.toml
index 3ef24e4b985a..6fa6eb028b51 100644
--- a/baselines/dasha/pyproject.toml
+++ b/baselines/dasha/pyproject.toml
@@ -60,6 +60,7 @@ pytest-watch = "==4.2.0"
 types-requests = "==2.27.7"
 py-spy = "==0.3.14"
 ruff = "==0.0.272"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
diff --git a/baselines/depthfl/pyproject.toml b/baselines/depthfl/pyproject.toml
index 2f928c2d3553..bd223ba78341 100644
--- a/baselines/depthfl/pyproject.toml
+++ b/baselines/depthfl/pyproject.toml
@@ -37,14 +37,13 @@ classifiers = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.10.0, <3.11.0" 
+python = ">=3.10.0, <3.11.0"
 flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 matplotlib = "3.7.1"
 torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.13.1%2Bcu116-cp310-cp310-linux_x86_64.whl"}
 torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.14.1%2Bcu116-cp310-cp310-linux_x86_64.whl"}
 
-
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
 black = "==23.1.0"
@@ -56,6 +55,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
diff --git a/baselines/fedavgm/pyproject.toml b/baselines/fedavgm/pyproject.toml
index cfd55a5b1fba..22a5f3a53847 100644
--- a/baselines/fedavgm/pyproject.toml
+++ b/baselines/fedavgm/pyproject.toml
@@ -3,7 +3,7 @@ requires = ["poetry-core>=1.4.0"]
 build-backend = "poetry.masonry.api"
 
 [tool.poetry]
-name = "fedavgm" 
+name = "fedavgm"
 version = "1.0.0"
 description = "FedAvgM: Measuring the effects of non-identical data distribution for federated visual classification"
 license = "Apache-2.0"
@@ -56,6 +56,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
diff --git a/baselines/fedmeta/pyproject.toml b/baselines/fedmeta/pyproject.toml
index cbaa9bb5d110..8f6bbe3f7fa1 100644
--- a/baselines/fedmeta/pyproject.toml
+++ b/baselines/fedmeta/pyproject.toml
@@ -46,7 +46,6 @@ torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp31
 torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"}
 pillow = "9.5.0" # needed <10.0.0 for LEAF repo scripts
 
-
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
 black = "==23.1.0"
@@ -58,6 +57,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
diff --git a/baselines/fedmlb/pyproject.toml b/baselines/fedmlb/pyproject.toml
index 005803e1891e..c76b2b1548ce 100644
--- a/baselines/fedmlb/pyproject.toml
+++ b/baselines/fedmlb/pyproject.toml
@@ -55,6 +55,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
diff --git a/baselines/fedper/pyproject.toml b/baselines/fedper/pyproject.toml
index efcdf25eface..04aef92a8c06 100644
--- a/baselines/fedper/pyproject.toml
+++ b/baselines/fedper/pyproject.toml
@@ -46,7 +46,6 @@ tqdm = "^4.66.1"
 torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl"}
 torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"}
 
-
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
 black = "==23.1.0"
@@ -58,6 +57,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
diff --git a/baselines/fedprox/pyproject.toml b/baselines/fedprox/pyproject.toml
index ee127ac19fe0..a8a3513d8ddf 100644
--- a/baselines/fedprox/pyproject.toml
+++ b/baselines/fedprox/pyproject.toml
@@ -57,6 +57,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
diff --git a/baselines/fedwav2vec2/pyproject.toml b/baselines/fedwav2vec2/pyproject.toml
index 1e7dbf55154b..87cfb44e0a5a 100644
--- a/baselines/fedwav2vec2/pyproject.toml
+++ b/baselines/fedwav2vec2/pyproject.toml
@@ -57,6 +57,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
diff --git a/baselines/fjord/pyproject.toml b/baselines/fjord/pyproject.toml
index d8a9ae307d7c..6d7466029263 100644
--- a/baselines/fjord/pyproject.toml
+++ b/baselines/fjord/pyproject.toml
@@ -47,8 +47,6 @@ tqdm = "4.65.0"
 torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl"}
 torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"}
 
-
-
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
 black = "==23.1.0"
@@ -60,6 +58,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
diff --git a/baselines/moon/pyproject.toml b/baselines/moon/pyproject.toml
index e9f826abb2ea..b385cd90300c 100644
--- a/baselines/moon/pyproject.toml
+++ b/baselines/moon/pyproject.toml
@@ -56,6 +56,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
diff --git a/baselines/niid_bench/pyproject.toml b/baselines/niid_bench/pyproject.toml
index adb001e031ff..6a7086806e40 100644
--- a/baselines/niid_bench/pyproject.toml
+++ b/baselines/niid_bench/pyproject.toml
@@ -55,6 +55,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
diff --git a/baselines/tamuna/pyproject.toml b/baselines/tamuna/pyproject.toml
index fedcde9eb1c2..d004cfbd6f90 100644
--- a/baselines/tamuna/pyproject.toml
+++ b/baselines/tamuna/pyproject.toml
@@ -56,6 +56,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88

From e97f782574f4333c104c25969e5ce7e568ff31ff Mon Sep 17 00:00:00 2001
From: mohammadnaseri <mohammad@flower.dev>
Date: Tue, 20 Feb 2024 14:21:05 +0000
Subject: [PATCH 035/102] Introduce central DP (server-side fixed clipping)
 (#2798)

---
 src/py/flwr/common/differential_privacy.py    |  87 ++++++++
 .../common/differential_privacy_constants.py  |  22 +++
 .../flwr/common/differential_privacy_test.py  | 138 +++++++++++++
 src/py/flwr/server/strategy/__init__.py       |   2 +
 .../flwr/server/strategy/dp_fixed_clipping.py | 187 ++++++++++++++++++
 5 files changed, 436 insertions(+)
 create mode 100644 src/py/flwr/common/differential_privacy.py
 create mode 100644 src/py/flwr/common/differential_privacy_constants.py
 create mode 100644 src/py/flwr/common/differential_privacy_test.py
 create mode 100644 src/py/flwr/server/strategy/dp_fixed_clipping.py

diff --git a/src/py/flwr/common/differential_privacy.py b/src/py/flwr/common/differential_privacy.py
new file mode 100644
index 000000000000..c6ec45d30f8e
--- /dev/null
+++ b/src/py/flwr/common/differential_privacy.py
@@ -0,0 +1,87 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utility functions for differential privacy."""
+
+
+import numpy as np
+
+from flwr.common import (
+    NDArrays,
+    Parameters,
+    ndarrays_to_parameters,
+    parameters_to_ndarrays,
+)
+
+
+def get_norm(input_arrays: NDArrays) -> float:
+    """Compute the L2 norm of the flattened input."""
+    array_norms = [np.linalg.norm(array.flat) for array in input_arrays]
+    # pylint: disable=consider-using-generator
+    return float(np.sqrt(sum([norm**2 for norm in array_norms])))
+
+
+def add_gaussian_noise_inplace(input_arrays: NDArrays, std_dev: float) -> None:
+    """Add Gaussian noise to each element of the input arrays."""
+    for array in input_arrays:
+        array += np.random.normal(0, std_dev, array.shape)
+
+
+def clip_inputs_inplace(input_arrays: NDArrays, clipping_norm: float) -> None:
+    """Clip model update based on the clipping norm in-place.
+
+    FlatClip method of the paper: https://arxiv.org/abs/1710.06963
+    """
+    input_norm = get_norm(input_arrays)
+    scaling_factor = min(1, clipping_norm / input_norm)
+    for array in input_arrays:
+        array *= scaling_factor
+
+
+def compute_stdv(
+    noise_multiplier: float, clipping_norm: float, num_sampled_clients: int
+) -> float:
+    """Compute standard deviation for noise addition.
+
+    Paper: https://arxiv.org/abs/1710.06963
+    """
+    return float((noise_multiplier * clipping_norm) / num_sampled_clients)
+
+
+def compute_clip_model_update(
+    param1: NDArrays, param2: NDArrays, clipping_norm: float
+) -> None:
+    """Compute model update (param1 - param2) and clip it.
+
+    Then add the clipped value to param1."""
+    model_update = [np.subtract(x, y) for (x, y) in zip(param1, param2)]
+    clip_inputs_inplace(model_update, clipping_norm)
+
+    for i, _ in enumerate(param2):
+        param1[i] = param2[i] + model_update[i]
+
+
+def add_gaussian_noise_to_params(
+    model_params: Parameters,
+    noise_multiplier: float,
+    clipping_norm: float,
+    num_sampled_clients: int,
+) -> Parameters:
+    """Add gaussian noise to model parameters."""
+    model_params_ndarrays = parameters_to_ndarrays(model_params)
+    add_gaussian_noise_inplace(
+        model_params_ndarrays,
+        compute_stdv(noise_multiplier, clipping_norm, num_sampled_clients),
+    )
+    return ndarrays_to_parameters(model_params_ndarrays)
diff --git a/src/py/flwr/common/differential_privacy_constants.py b/src/py/flwr/common/differential_privacy_constants.py
new file mode 100644
index 000000000000..9ec080975aa3
--- /dev/null
+++ b/src/py/flwr/common/differential_privacy_constants.py
@@ -0,0 +1,22 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Constants for differential privacy."""
+
+CLIENTS_DISCREPANCY_WARNING = (
+    "The number of clients returning parameters (%s)"
+    " differs from the number of sampled clients (%s)."
+    " This could impact the differential privacy guarantees,"
+    " potentially leading to privacy leakage or inadequate noise calibration."
+)
diff --git a/src/py/flwr/common/differential_privacy_test.py b/src/py/flwr/common/differential_privacy_test.py
new file mode 100644
index 000000000000..32b7bc9e4b36
--- /dev/null
+++ b/src/py/flwr/common/differential_privacy_test.py
@@ -0,0 +1,138 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Differential Privacy (DP) utility functions tests."""
+
+
+import numpy as np
+
+from .differential_privacy import (
+    add_gaussian_noise_inplace,
+    clip_inputs_inplace,
+    compute_clip_model_update,
+    compute_stdv,
+    get_norm,
+)
+
+
+def test_add_gaussian_noise_inplace() -> None:
+    """Test add_gaussian_noise_inplace function."""
+    # Prepare
+    update = [np.array([[1.0, 2.0], [3.0, 4.0]]), np.array([[5.0, 6.0], [7.0, 8.0]])]
+    std_dev = 0.1
+
+    # Execute
+    add_gaussian_noise_inplace(update, std_dev)
+
+    # Assert
+    # Check that the shape of the result is the same as the input
+    for layer in update:
+        assert layer.shape == (2, 2)
+
+    # Check that the values have been changed and are not equal to the original update
+    for layer in update:
+        assert not np.array_equal(
+            layer, [[1.0, 2.0], [3.0, 4.0]]
+        ) and not np.array_equal(layer, [[5.0, 6.0], [7.0, 8.0]])
+
+    # Check that the noise has been added
+    for layer in update:
+        noise_added = (
+            layer - np.array([[1.0, 2.0], [3.0, 4.0]])
+            if np.array_equal(layer, [[1.0, 2.0], [3.0, 4.0]])
+            else layer - np.array([[5.0, 6.0], [7.0, 8.0]])
+        )
+        assert np.any(np.abs(noise_added) > 0)
+
+
+def test_get_norm() -> None:
+    """Test get_norm function."""
+    # Prepare
+    update = [np.array([[1, 2], [3, 4]]), np.array([[5, 6], [7, 8]])]
+
+    # Execute
+    result = get_norm(update)
+
+    expected = float(
+        np.linalg.norm(np.concatenate([sub_update.flatten() for sub_update in update]))
+    )
+
+    # Assert
+    assert expected == result
+
+
+def test_clip_inputs_inplace() -> None:
+    """Test clip_inputs_inplace function."""
+    # Prepare
+    updates = [
+        np.array([[1.5, -0.5], [2.0, -1.0]]),
+        np.array([0.5, -0.5]),
+        np.array([[-0.5, 1.5], [-1.0, 2.0]]),
+        np.array([-0.5, 0.5]),
+    ]
+    clipping_norm = 1.5
+
+    original_updates = [np.copy(update) for update in updates]
+
+    # Execute
+    clip_inputs_inplace(updates, clipping_norm)
+
+    # Assert
+    for updated, original_update in zip(updates, original_updates):
+        clip_norm = np.linalg.norm(original_update)
+        assert np.all(updated <= clip_norm) and np.all(updated >= -clip_norm)
+
+
+def test_compute_stdv() -> None:
+    """Test compute_stdv function."""
+    # Prepare
+    noise_multiplier = 1.0
+    clipping_norm = 0.5
+    num_sampled_clients = 10
+
+    # Execute
+    stdv = compute_stdv(noise_multiplier, clipping_norm, num_sampled_clients)
+
+    # Assert
+    expected_stdv = float((noise_multiplier * clipping_norm) / num_sampled_clients)
+    assert stdv == expected_stdv
+
+
+def test_compute_clip_model_update() -> None:
+    """Test compute_clip_model_update function."""
+    # Prepare
+    param1 = [
+        np.array([0.5, 1.5, 2.5]),
+        np.array([3.5, 4.5, 5.5]),
+        np.array([6.5, 7.5, 8.5]),
+    ]
+    param2 = [
+        np.array([1.0, 2.0, 3.0]),
+        np.array([4.0, 5.0, 6.0]),
+        np.array([7.0, 8.0, 9.0]),
+    ]
+    clipping_norm = 4
+
+    expected_result = [
+        np.array([0.5, 1.5, 2.5]),
+        np.array([3.5, 4.5, 5.5]),
+        np.array([6.5, 7.5, 8.5]),
+    ]
+
+    # Execute
+    compute_clip_model_update(param1, param2, clipping_norm)
+
+    # Verify
+    for i, param in enumerate(param1):
+        np.testing.assert_array_almost_equal(param, expected_result[i])
diff --git a/src/py/flwr/server/strategy/__init__.py b/src/py/flwr/server/strategy/__init__.py
index 1750a7522379..a31f5d48b77c 100644
--- a/src/py/flwr/server/strategy/__init__.py
+++ b/src/py/flwr/server/strategy/__init__.py
@@ -16,6 +16,7 @@
 
 
 from .bulyan import Bulyan as Bulyan
+from .dp_fixed_clipping import DifferentialPrivacyServerSideFixedClipping
 from .dpfedavg_adaptive import DPFedAvgAdaptive as DPFedAvgAdaptive
 from .dpfedavg_fixed import DPFedAvgFixed as DPFedAvgFixed
 from .fault_tolerant_fedavg import FaultTolerantFedAvg as FaultTolerantFedAvg
@@ -57,4 +58,5 @@
     "DPFedAvgAdaptive",
     "DPFedAvgFixed",
     "Strategy",
+    "DifferentialPrivacyServerSideFixedClipping",
 ]
diff --git a/src/py/flwr/server/strategy/dp_fixed_clipping.py b/src/py/flwr/server/strategy/dp_fixed_clipping.py
new file mode 100644
index 000000000000..d18a6b2079d9
--- /dev/null
+++ b/src/py/flwr/server/strategy/dp_fixed_clipping.py
@@ -0,0 +1,187 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Central differential privacy with fixed clipping.
+
+Papers: https://arxiv.org/abs/1712.07557, https://arxiv.org/abs/1710.06963
+"""
+
+
+from logging import WARNING
+from typing import Dict, List, Optional, Tuple, Union
+
+from flwr.common import (
+    EvaluateIns,
+    EvaluateRes,
+    FitIns,
+    FitRes,
+    NDArrays,
+    Parameters,
+    Scalar,
+    ndarrays_to_parameters,
+    parameters_to_ndarrays,
+)
+from flwr.common.differential_privacy import (
+    add_gaussian_noise_to_params,
+    compute_clip_model_update,
+)
+from flwr.common.differential_privacy_constants import CLIENTS_DISCREPANCY_WARNING
+from flwr.common.logger import log
+from flwr.server.client_manager import ClientManager
+from flwr.server.client_proxy import ClientProxy
+from flwr.server.strategy.strategy import Strategy
+
+
+class DifferentialPrivacyServerSideFixedClipping(Strategy):
+    """Wrapper for Central DP with Server Side Fixed Clipping.
+
+    Parameters
+    ----------
+    strategy : Strategy
+        The strategy to which DP functionalities will be added by this wrapper.
+    noise_multiplier : float
+        The noise multiplier for the Gaussian mechanism for model updates.
+        A value of 1.0 or higher is recommended for strong privacy.
+    clipping_norm : float
+        The value of the clipping norm.
+    num_sampled_clients : int
+        The number of clients that are sampled on each round.
+
+    Examples
+    --------
+    Create a strategy:
+
+    >>> strategy = fl.server.strategy.FedAvg( ... )
+
+    Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping wrapper
+
+    >>> dp_strategy = DifferentialPrivacyServerSideFixedClipping(
+    >>>     strategy, cfg.noise_multiplier, cfg.clipping_norm, cfg.num_sampled_clients
+    >>> )
+    """
+
+    # pylint: disable=too-many-arguments,too-many-instance-attributes
+    def __init__(
+        self,
+        strategy: Strategy,
+        noise_multiplier: float,
+        clipping_norm: float,
+        num_sampled_clients: int,
+    ) -> None:
+        super().__init__()
+
+        self.strategy = strategy
+
+        if noise_multiplier < 0:
+            raise ValueError("The noise multiplier should be a non-negative value.")
+
+        if clipping_norm <= 0:
+            raise ValueError("The clipping norm should be a positive value.")
+
+        if num_sampled_clients <= 0:
+            raise ValueError(
+                "The number of sampled clients should be a positive value."
+            )
+
+        self.noise_multiplier = noise_multiplier
+        self.clipping_norm = clipping_norm
+        self.num_sampled_clients = num_sampled_clients
+
+        self.current_round_params: NDArrays = []
+
+    def __repr__(self) -> str:
+        """Compute a string representation of the strategy."""
+        rep = "Differential Privacy Strategy Wrapper (Server-Side Fixed Clipping)"
+        return rep
+
+    def initialize_parameters(
+        self, client_manager: ClientManager
+    ) -> Optional[Parameters]:
+        """Initialize global model parameters using given strategy."""
+        return self.strategy.initialize_parameters(client_manager)
+
+    def configure_fit(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, FitIns]]:
+        """Configure the next round of training."""
+        self.current_round_params = parameters_to_ndarrays(parameters)
+        return self.strategy.configure_fit(server_round, parameters, client_manager)
+
+    def configure_evaluate(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, EvaluateIns]]:
+        """Configure the next round of evaluation."""
+        return self.strategy.configure_evaluate(
+            server_round, parameters, client_manager
+        )
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Compute the updates, clip, and pass them for aggregation.
+
+        Afterward, add noise to the aggregated parameters.
+        """
+        if failures:
+            return None, {}
+
+        if len(results) != self.num_sampled_clients:
+            log(
+                WARNING,
+                CLIENTS_DISCREPANCY_WARNING,
+                len(results),
+                self.num_sampled_clients,
+            )
+        for _, res in results:
+            param = parameters_to_ndarrays(res.parameters)
+            # Compute and clip update
+            compute_clip_model_update(
+                param, self.current_round_params, self.clipping_norm
+            )
+            # Convert back to parameters
+            res.parameters = ndarrays_to_parameters(param)
+
+        # Pass the new parameters for aggregation
+        aggregated_params, metrics = self.strategy.aggregate_fit(
+            server_round, results, failures
+        )
+
+        # Add Gaussian noise to the aggregated parameters
+        if aggregated_params:
+            aggregated_params = add_gaussian_noise_to_params(
+                aggregated_params,
+                self.noise_multiplier,
+                self.clipping_norm,
+                self.num_sampled_clients,
+            )
+
+        return aggregated_params, metrics
+
+    def aggregate_evaluate(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, EvaluateRes]],
+        failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+    ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+        """Aggregate evaluation losses using the given strategy."""
+        return self.strategy.aggregate_evaluate(server_round, results, failures)
+
+    def evaluate(
+        self, server_round: int, parameters: Parameters
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """Evaluate model parameters using an evaluation function from the strategy."""
+        return self.strategy.evaluate(server_round, parameters)

From 19a7e0d79792f6d8a7a9bee8643f7e17786d42a3 Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Tue, 20 Feb 2024 14:34:07 +0000
Subject: [PATCH 036/102] Improve `*Record` (#2982)

---
 .../mod/secure_aggregation/secaggplus_mod.py  |  18 +--
 .../secure_aggregation/secaggplus_mod_test.py |  19 ++-
 src/py/flwr/common/__init__.py                |   2 +
 src/py/flwr/common/configsrecord.py           | 119 +++++++-----------
 src/py/flwr/common/metricsrecord.py           | 118 +++++++----------
 src/py/flwr/common/parametersrecord.py        |  59 ++++-----
 src/py/flwr/common/recordset_compat.py        |  35 +++---
 src/py/flwr/common/recordset_test.py          |  42 +++----
 src/py/flwr/common/serde.py                   |  23 ++--
 src/py/flwr/common/serde_test.py              |  12 +-
 src/py/flwr/common/typeddict.py               | 113 +++++++++++++++++
 11 files changed, 294 insertions(+), 266 deletions(-)
 create mode 100644 src/py/flwr/common/typeddict.py

diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
index 68cafb3e9825..7ac311108383 100644
--- a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
@@ -22,6 +22,7 @@
 
 from flwr.client.typing import ClientAppCallable
 from flwr.common import (
+    ConfigsRecord,
     Context,
     Message,
     RecordSet,
@@ -29,7 +30,6 @@
     parameters_to_ndarrays,
 )
 from flwr.common import recordset_compat as compat
-from flwr.common.configsrecord import ConfigsRecord
 from flwr.common.constant import MESSAGE_TYPE_FIT
 from flwr.common.logger import log
 from flwr.common.secure_aggregation.crypto.shamir import create_shares
@@ -177,11 +177,11 @@ def secaggplus_mod(
     # Retrieve local state
     if RECORD_KEY_STATE not in ctxt.state.configs:
         ctxt.state.set_configs(RECORD_KEY_STATE, ConfigsRecord({}))
-    state_dict = ctxt.state.get_configs(RECORD_KEY_STATE).data
+    state_dict = ctxt.state.get_configs(RECORD_KEY_STATE)
     state = SecAggPlusState(**state_dict)
 
     # Retrieve incoming configs
-    configs = msg.content.get_configs(RECORD_KEY_CONFIGS).data
+    configs = msg.content.get_configs(RECORD_KEY_CONFIGS)
 
     # Check the validity of the next stage
     check_stage(state.current_stage, configs)
@@ -213,7 +213,7 @@ def secaggplus_mod(
     return msg.create_reply(content, ttl="")
 
 
-def check_stage(current_stage: str, configs: Dict[str, ConfigsRecordValues]) -> None:
+def check_stage(current_stage: str, configs: ConfigsRecord) -> None:
     """Check the validity of the next stage."""
     # Check the existence of KEY_STAGE
     if KEY_STAGE not in configs:
@@ -245,7 +245,7 @@ def check_stage(current_stage: str, configs: Dict[str, ConfigsRecordValues]) ->
 
 
 # pylint: disable-next=too-many-branches
-def check_configs(stage: str, configs: Dict[str, ConfigsRecordValues]) -> None:
+def check_configs(stage: str, configs: ConfigsRecord) -> None:
     """Check the validity of the configs."""
     # Check `named_values` for the setup stage
     if stage == STAGE_SETUP:
@@ -336,7 +336,7 @@ def check_configs(stage: str, configs: Dict[str, ConfigsRecordValues]) -> None:
 
 
 def _setup(
-    state: SecAggPlusState, configs: Dict[str, ConfigsRecordValues]
+    state: SecAggPlusState, configs: ConfigsRecord
 ) -> Dict[str, ConfigsRecordValues]:
     # Assigning parameter values to object fields
     sec_agg_param_dict = configs
@@ -372,7 +372,7 @@ def _setup(
 
 # pylint: disable-next=too-many-locals
 def _share_keys(
-    state: SecAggPlusState, configs: Dict[str, ConfigsRecordValues]
+    state: SecAggPlusState, configs: ConfigsRecord
 ) -> Dict[str, ConfigsRecordValues]:
     named_bytes_tuples = cast(Dict[str, Tuple[bytes, bytes]], configs)
     key_dict = {int(sid): (pk1, pk2) for sid, (pk1, pk2) in named_bytes_tuples.items()}
@@ -435,7 +435,7 @@ def _share_keys(
 # pylint: disable-next=too-many-locals
 def _collect_masked_input(
     state: SecAggPlusState,
-    configs: Dict[str, ConfigsRecordValues],
+    configs: ConfigsRecord,
     fit: Callable[[], FitRes],
 ) -> Dict[str, ConfigsRecordValues]:
     log(INFO, "Client %d: starting stage 2...", state.sid)
@@ -510,7 +510,7 @@ def _collect_masked_input(
 
 
 def _unmask(
-    state: SecAggPlusState, configs: Dict[str, ConfigsRecordValues]
+    state: SecAggPlusState, configs: ConfigsRecord
 ) -> Dict[str, ConfigsRecordValues]:
     log(INFO, "Client %d: starting stage 3...", state.sid)
 
diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
index bbb7df1eb7f0..3a888e6cd87d 100644
--- a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
@@ -19,8 +19,7 @@
 from typing import Callable, Dict, List
 
 from flwr.client.mod import make_ffn
-from flwr.common import Context, Message, Metadata, RecordSet
-from flwr.common.configsrecord import ConfigsRecord
+from flwr.common import ConfigsRecord, Context, Message, Metadata, RecordSet
 from flwr.common.constant import MESSAGE_TYPE_FIT
 from flwr.common.secure_aggregation.secaggplus_constants import (
     KEY_ACTIVE_SECURE_ID_LIST,
@@ -50,7 +49,7 @@
 
 def get_test_handler(
     ctxt: Context,
-) -> Callable[[Dict[str, ConfigsRecordValues]], Dict[str, ConfigsRecordValues]]:
+) -> Callable[[Dict[str, ConfigsRecordValues]], ConfigsRecord]:
     """."""
 
     def empty_ffn(_msg: Message, _2: Context) -> Message:
@@ -58,7 +57,7 @@ def empty_ffn(_msg: Message, _2: Context) -> Message:
 
     app = make_ffn(empty_ffn, [secaggplus_mod])
 
-    def func(configs: Dict[str, ConfigsRecordValues]) -> Dict[str, ConfigsRecordValues]:
+    def func(configs: Dict[str, ConfigsRecordValues]) -> ConfigsRecord:
         in_msg = Message(
             metadata=Metadata(
                 run_id=0,
@@ -73,7 +72,7 @@ def func(configs: Dict[str, ConfigsRecordValues]) -> Dict[str, ConfigsRecordValu
             content=RecordSet(configs={RECORD_KEY_CONFIGS: ConfigsRecord(configs)}),
         )
         out_msg = app(in_msg, ctxt)
-        return out_msg.content.get_configs(RECORD_KEY_CONFIGS).data
+        return out_msg.content.get_configs(RECORD_KEY_CONFIGS)
 
     return func
 
@@ -87,7 +86,7 @@ def _make_set_state_fn(
     ctxt: Context,
 ) -> Callable[[str], None]:
     def set_stage(stage: str) -> None:
-        state_dict = ctxt.state.get_configs(RECORD_KEY_STATE).data
+        state_dict = ctxt.state.get_configs(RECORD_KEY_STATE)
         state = SecAggPlusState(**state_dict)
         state.current_stage = stage
         ctxt.state.set_configs(RECORD_KEY_STATE, ConfigsRecord(state.to_dict()))
@@ -174,7 +173,7 @@ def test_stage_setup_check(self) -> None:
 
         # Test valid `named_values`
         try:
-            check_configs(STAGE_SETUP, valid_configs.copy())
+            check_configs(STAGE_SETUP, ConfigsRecord(valid_configs))
         # pylint: disable-next=broad-except
         except Exception as exc:
             self.fail(f"check_named_values() raised {type(exc)} unexpectedly!")
@@ -217,7 +216,7 @@ def test_stage_share_keys_check(self) -> None:
 
         # Test valid `named_values`
         try:
-            check_configs(STAGE_SHARE_KEYS, valid_configs.copy())
+            check_configs(STAGE_SHARE_KEYS, ConfigsRecord(valid_configs))
         # pylint: disable-next=broad-except
         except Exception as exc:
             self.fail(f"check_named_values() raised {type(exc)} unexpectedly!")
@@ -253,7 +252,7 @@ def test_stage_collect_masked_input_check(self) -> None:
 
         # Test valid `named_values`
         try:
-            check_configs(STAGE_COLLECT_MASKED_INPUT, valid_configs.copy())
+            check_configs(STAGE_COLLECT_MASKED_INPUT, ConfigsRecord(valid_configs))
         # pylint: disable-next=broad-except
         except Exception as exc:
             self.fail(f"check_named_values() raised {type(exc)} unexpectedly!")
@@ -297,7 +296,7 @@ def test_stage_unmask_check(self) -> None:
 
         # Test valid `named_values`
         try:
-            check_configs(STAGE_UNMASK, valid_configs.copy())
+            check_configs(STAGE_UNMASK, ConfigsRecord(valid_configs))
         # pylint: disable-next=broad-except
         except Exception as exc:
             self.fail(f"check_named_values() raised {type(exc)} unexpectedly!")
diff --git a/src/py/flwr/common/__init__.py b/src/py/flwr/common/__init__.py
index 3ee5c1b14500..a6e4d6cb5cc0 100644
--- a/src/py/flwr/common/__init__.py
+++ b/src/py/flwr/common/__init__.py
@@ -28,6 +28,7 @@
 from .parameter import ndarray_to_bytes as ndarray_to_bytes
 from .parameter import ndarrays_to_parameters as ndarrays_to_parameters
 from .parameter import parameters_to_ndarrays as parameters_to_ndarrays
+from .parametersrecord import Array as Array
 from .parametersrecord import ParametersRecord as ParametersRecord
 from .recordset import RecordSet as RecordSet
 from .telemetry import EventType as EventType
@@ -56,6 +57,7 @@
 from .typing import Status as Status
 
 __all__ = [
+    "Array",
     "bytes_to_ndarray",
     "ClientMessage",
     "Code",
diff --git a/src/py/flwr/common/configsrecord.py b/src/py/flwr/common/configsrecord.py
index b0480841e06c..704657601f50 100644
--- a/src/py/flwr/common/configsrecord.py
+++ b/src/py/flwr/common/configsrecord.py
@@ -15,23 +15,56 @@
 """ConfigsRecord."""
 
 
-from dataclasses import dataclass, field
 from typing import Dict, Optional, get_args
 
-from .typing import ConfigsRecordValues, ConfigsScalar
+from flwr.common.typing import ConfigsRecordValues, ConfigsScalar
 
+from .typeddict import TypedDict
 
-@dataclass
-class ConfigsRecord:
-    """Configs record."""
 
-    data: Dict[str, ConfigsRecordValues] = field(default_factory=dict)
+def _check_key(key: str) -> None:
+    """Check if key is of expected type."""
+    if not isinstance(key, str):
+        raise TypeError(f"Key must be of type `str` but `{type(key)}` was passed.")
+
+
+def _check_value(value: ConfigsRecordValues) -> None:
+    def is_valid(__v: ConfigsScalar) -> None:
+        """Check if value is of expected type."""
+        if not isinstance(__v, get_args(ConfigsScalar)):
+            raise TypeError(
+                "Not all values are of valid type."
+                f" Expected `{ConfigsRecordValues}` but `{type(__v)}` was passed."
+            )
+
+    if isinstance(value, list):
+        # If your lists are large (e.g. 1M+ elements) this will be slow
+        # 1s to check 10M element list on a M2 Pro
+        # In such settings, you'd be better of treating such config as
+        # an array and pass it to a ParametersRecord.
+        # Empty lists are valid
+        if len(value) > 0:
+            is_valid(value[0])
+            # all elements in the list must be of the same valid type
+            # this is needed for protobuf
+            value_type = type(value[0])
+            if not all(isinstance(v, value_type) for v in value):
+                raise TypeError(
+                    "All values in a list must be of the same valid type. "
+                    f"One of {ConfigsScalar}."
+                )
+    else:
+        is_valid(value)
+
+
+class ConfigsRecord(TypedDict[str, ConfigsRecordValues]):
+    """Configs record."""
 
     def __init__(
         self,
         configs_dict: Optional[Dict[str, ConfigsRecordValues]] = None,
         keep_input: bool = True,
-    ):
+    ) -> None:
         """Construct a ConfigsRecord object.
 
         Parameters
@@ -46,71 +79,9 @@ def __init__(
             to True, the data is duplicated in memory. If memory is a concern, set
             it to False.
         """
-        self.data = {}
+        super().__init__(_check_key, _check_value)
         if configs_dict:
-            self.set_configs(configs_dict, keep_input=keep_input)
-
-    def set_configs(
-        self, configs_dict: Dict[str, ConfigsRecordValues], keep_input: bool = True
-    ) -> None:
-        """Add configs to the record.
-
-        Parameters
-        ----------
-        configs_dict : Dict[str, ConfigsRecordValues]
-            A dictionary that stores basic types (i.e. `str`,`int`, `float`, `bytes` as
-            defined in `ConfigsRecordValues`) and list of such types (see
-            `ConfigsScalarList`).
-        keep_input : bool (default: True)
-            A boolean indicating whether config passed should be deleted from the input
-            dictionary immediately after adding them to the record. When set
-            to True, the data is duplicated in memory. If memory is a concern, set
-            it to False.
-        """
-        if any(not isinstance(k, str) for k in configs_dict.keys()):
-            raise TypeError(f"Not all keys are of valid type. Expected {str}")
-
-        def is_valid(value: ConfigsScalar) -> None:
-            """Check if value is of expected type."""
-            if not isinstance(value, get_args(ConfigsScalar)):
-                raise TypeError(
-                    "Not all values are of valid type."
-                    f" Expected {ConfigsRecordValues} but you passed {type(value)}."
-                )
-
-        # Check types of values
-        # Split between those values that are list and those that aren't
-        # then process in the same way
-        for value in configs_dict.values():
-            if isinstance(value, list):
-                # If your lists are large (e.g. 1M+ elements) this will be slow
-                # 1s to check 10M element list on a M2 Pro
-                # In such settings, you'd be better of treating such config as
-                # an array and pass it to a ParametersRecord.
-                # Empty lists are valid
-                if len(value) > 0:
-                    is_valid(value[0])
-                    # all elements in the list must be of the same valid type
-                    # this is needed for protobuf
-                    value_type = type(value[0])
-                    if not all(isinstance(v, value_type) for v in value):
-                        raise TypeError(
-                            "All values in a list must be of the same valid type. "
-                            f"One of {ConfigsScalar}."
-                        )
-            else:
-                is_valid(value)
-
-        # Add configs to record
-        if keep_input:
-            # Copy
-            self.data = configs_dict.copy()
-        else:
-            # Add entries to dataclass without duplicating memory
-            for key in list(configs_dict.keys()):
-                self.data[key] = configs_dict[key]
-                del configs_dict[key]
-
-    def __getitem__(self, key: str) -> ConfigsRecordValues:
-        """Retrieve an element stored in record."""
-        return self.data[key]
+            for k in list(configs_dict.keys()):
+                self[k] = configs_dict[k]
+                if not keep_input:
+                    del configs_dict[k]
diff --git a/src/py/flwr/common/metricsrecord.py b/src/py/flwr/common/metricsrecord.py
index e70b0cb31d55..81b02303421b 100644
--- a/src/py/flwr/common/metricsrecord.py
+++ b/src/py/flwr/common/metricsrecord.py
@@ -15,17 +15,50 @@
 """MetricsRecord."""
 
 
-from dataclasses import dataclass, field
 from typing import Dict, Optional, get_args
 
-from .typing import MetricsRecordValues, MetricsScalar
+from flwr.common.typing import MetricsRecordValues, MetricsScalar
 
+from .typeddict import TypedDict
 
-@dataclass
-class MetricsRecord:
-    """Metrics record."""
 
-    data: Dict[str, MetricsRecordValues] = field(default_factory=dict)
+def _check_key(key: str) -> None:
+    """Check if key is of expected type."""
+    if not isinstance(key, str):
+        raise TypeError(f"Key must be of type `str` but `{type(key)}` was passed.")
+
+
+def _check_value(value: MetricsRecordValues) -> None:
+    def is_valid(__v: MetricsScalar) -> None:
+        """Check if value is of expected type."""
+        if not isinstance(__v, get_args(MetricsScalar)) or isinstance(__v, bool):
+            raise TypeError(
+                "Not all values are of valid type."
+                f" Expected `{MetricsRecordValues}` but `{type(__v)}` was passed."
+            )
+
+    if isinstance(value, list):
+        # If your lists are large (e.g. 1M+ elements) this will be slow
+        # 1s to check 10M element list on a M2 Pro
+        # In such settings, you'd be better of treating such metric as
+        # an array and pass it to a ParametersRecord.
+        # Empty lists are valid
+        if len(value) > 0:
+            is_valid(value[0])
+            # all elements in the list must be of the same valid type
+            # this is needed for protobuf
+            value_type = type(value[0])
+            if not all(isinstance(v, value_type) for v in value):
+                raise TypeError(
+                    "All values in a list must be of the same valid type. "
+                    f"One of {MetricsScalar}."
+                )
+    else:
+        is_valid(value)
+
+
+class MetricsRecord(TypedDict[str, MetricsRecordValues]):
+    """Metrics record."""
 
     def __init__(
         self,
@@ -45,72 +78,9 @@ def __init__(
             to True, the data is duplicated in memory. If memory is a concern, set
             it to False.
         """
-        self.data = {}
+        super().__init__(_check_key, _check_value)
         if metrics_dict:
-            self.set_metrics(metrics_dict, keep_input=keep_input)
-
-    def set_metrics(
-        self, metrics_dict: Dict[str, MetricsRecordValues], keep_input: bool = True
-    ) -> None:
-        """Add metrics to the record.
-
-        Parameters
-        ----------
-        metrics_dict : Dict[str, MetricsRecordValues]
-            A dictionary that stores basic types (i.e. `int`, `float` as defined
-            in `MetricsScalar`) and list of such types (see `MetricsScalarList`).
-        keep_input : bool (default: True)
-            A boolean indicating whether metrics should be deleted from the input
-            dictionary immediately after adding them to the record. When set
-            to True, the data is duplicated in memory. If memory is a concern, set
-            it to False.
-        """
-        if any(not isinstance(k, str) for k in metrics_dict.keys()):
-            raise TypeError(f"Not all keys are of valid type. Expected {str}.")
-
-        def is_valid(value: MetricsScalar) -> None:
-            """Check if value is of expected type."""
-            if not isinstance(value, get_args(MetricsScalar)) or isinstance(
-                value, bool
-            ):
-                raise TypeError(
-                    "Not all values are of valid type."
-                    f" Expected {MetricsRecordValues} but you passed {type(value)}."
-                )
-
-        # Check types of values
-        # Split between those values that are list and those that aren't
-        # then process in the same way
-        for value in metrics_dict.values():
-            if isinstance(value, list):
-                # If your lists are large (e.g. 1M+ elements) this will be slow
-                # 1s to check 10M element list on a M2 Pro
-                # In such settings, you'd be better of treating such metric as
-                # an array and pass it to a ParametersRecord.
-                # Empty lists are valid
-                if len(value) > 0:
-                    is_valid(value[0])
-                    # all elements in the list must be of the same valid type
-                    # this is needed for protobuf
-                    value_type = type(value[0])
-                    if not all(isinstance(v, value_type) for v in value):
-                        raise TypeError(
-                            "All values in a list must be of the same valid type. "
-                            f"One of {MetricsScalar}."
-                        )
-            else:
-                is_valid(value)
-
-        # Add metrics to record
-        if keep_input:
-            # Copy
-            self.data = metrics_dict.copy()
-        else:
-            # Add entries to dataclass without duplicating memory
-            for key in list(metrics_dict.keys()):
-                self.data[key] = metrics_dict[key]
-                del metrics_dict[key]
-
-    def __getitem__(self, key: str) -> MetricsRecordValues:
-        """Retrieve an element stored in record."""
-        return self.data[key]
+            for k in list(metrics_dict.keys()):
+                self[k] = metrics_dict[k]
+                if not keep_input:
+                    del metrics_dict[k]
diff --git a/src/py/flwr/common/parametersrecord.py b/src/py/flwr/common/parametersrecord.py
index ef02a0789ddf..aaa76c88ae09 100644
--- a/src/py/flwr/common/parametersrecord.py
+++ b/src/py/flwr/common/parametersrecord.py
@@ -15,9 +15,11 @@
 """ParametersRecord and Array."""
 
 
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import List, Optional, OrderedDict
 
+from .typeddict import TypedDict
+
 
 @dataclass
 class Array:
@@ -50,8 +52,21 @@ class Array:
     data: bytes
 
 
+def _check_key(key: str) -> None:
+    """Check if key is of expected type."""
+    if not isinstance(key, str):
+        raise TypeError(f"Key must be of type `str` but `{type(key)}` was passed.")
+
+
+def _check_value(value: Array) -> None:
+    if not isinstance(value, Array):
+        raise TypeError(
+            f"Value must be of type `{Array}` but `{type(value)}` was passed."
+        )
+
+
 @dataclass
-class ParametersRecord:
+class ParametersRecord(TypedDict[str, Array]):
     """Parameters record.
 
     A dataclass storing named Arrays in order. This means that it holds entries as an
@@ -59,8 +74,6 @@ class ParametersRecord:
     PyTorch's state_dict, but holding serialised tensors instead.
     """
 
-    data: OrderedDict[str, Array] = field(default_factory=OrderedDict[str, Array])
-
     def __init__(
         self,
         array_dict: Optional[OrderedDict[str, Array]] = None,
@@ -81,37 +94,9 @@ def __init__(
             parameters after adding it to the record, set this flag to True. When set
             to True, the data is duplicated in memory.
         """
-        self.data = OrderedDict()
+        super().__init__(_check_key, _check_value)
         if array_dict:
-            self.set_parameters(array_dict, keep_input=keep_input)
-
-    def set_parameters(
-        self, array_dict: OrderedDict[str, Array], keep_input: bool = False
-    ) -> None:
-        """Add parameters to record.
-
-        Parameters
-        ----------
-        array_dict : OrderedDict[str, Array]
-            A dictionary that stores serialized array-like or tensor-like objects.
-        keep_input : bool (default: False)
-            A boolean indicating whether parameters should be deleted from the input
-            dictionary immediately after adding them to the record.
-        """
-        if any(not isinstance(k, str) for k in array_dict.keys()):
-            raise TypeError(f"Not all keys are of valid type. Expected {str}")
-        if any(not isinstance(v, Array) for v in array_dict.values()):
-            raise TypeError(f"Not all values are of valid type. Expected {Array}")
-
-        if keep_input:
-            # Copy
-            self.data = OrderedDict(array_dict)
-        else:
-            # Add entries to dataclass without duplicating memory
-            for key in list(array_dict.keys()):
-                self.data[key] = array_dict[key]
-                del array_dict[key]
-
-    def __getitem__(self, key: str) -> Array:
-        """Retrieve an element stored in record."""
-        return self.data[key]
+            for k in list(array_dict.keys()):
+                self[k] = array_dict[k]
+                if not keep_input:
+                    del array_dict[k]
diff --git a/src/py/flwr/common/recordset_compat.py b/src/py/flwr/common/recordset_compat.py
index e0e591048820..4e90ed79a425 100644
--- a/src/py/flwr/common/recordset_compat.py
+++ b/src/py/flwr/common/recordset_compat.py
@@ -17,10 +17,7 @@
 
 from typing import Dict, Mapping, OrderedDict, Tuple, Union, cast, get_args
 
-from .configsrecord import ConfigsRecord
-from .metricsrecord import MetricsRecord
-from .parametersrecord import Array, ParametersRecord
-from .recordset import RecordSet
+from . import Array, ConfigsRecord, MetricsRecord, ParametersRecord, RecordSet
 from .typing import (
     Code,
     ConfigsRecordValues,
@@ -61,7 +58,7 @@ def parametersrecord_to_parameters(
     """
     parameters = Parameters(tensors=[], tensor_type="")
 
-    for key in list(record.data.keys()):
+    for key in list(record.keys()):
         parameters.tensors.append(record[key].data)
 
         if not parameters.tensor_type:
@@ -70,7 +67,7 @@ def parametersrecord_to_parameters(
             parameters.tensor_type = record[key].stype
 
         if not keep_input:
-            del record.data[key]
+            del record[key]
 
     return parameters
 
@@ -95,8 +92,6 @@ def parameters_to_parametersrecord(
     """
     tensor_type = parameters.tensor_type
 
-    p_record = ParametersRecord()
-
     num_arrays = len(parameters.tensors)
     ordered_dict = OrderedDict()
     for idx in range(num_arrays):
@@ -108,8 +103,7 @@ def parameters_to_parametersrecord(
             data=tensor, dtype="", stype=tensor_type, shape=[]
         )
 
-    p_record.set_parameters(ordered_dict, keep_input=keep_input)
-    return p_record
+    return ParametersRecord(ordered_dict, keep_input=keep_input)
 
 
 def _check_mapping_from_recordscalartype_to_scalar(
@@ -143,8 +137,8 @@ def _recordset_to_fit_or_evaluate_ins_components(
 
     # get config dict
     config_record = recordset.get_configs(f"{ins_str}.config")
-
-    config_dict = _check_mapping_from_recordscalartype_to_scalar(config_record.data)
+    # pylint: disable-next=protected-access
+    config_dict = _check_mapping_from_recordscalartype_to_scalar(config_record._data)
 
     return parameters, config_dict
 
@@ -213,8 +207,8 @@ def recordset_to_fitres(recordset: RecordSet, keep_input: bool) -> FitRes:
         int, recordset.get_metrics(f"{ins_str}.num_examples")["num_examples"]
     )
     configs_record = recordset.get_configs(f"{ins_str}.metrics")
-
-    metrics = _check_mapping_from_recordscalartype_to_scalar(configs_record.data)
+    # pylint: disable-next=protected-access
+    metrics = _check_mapping_from_recordscalartype_to_scalar(configs_record._data)
     status = _extract_status_from_recordset(ins_str, recordset)
 
     return FitRes(
@@ -273,7 +267,8 @@ def recordset_to_evaluateres(recordset: RecordSet) -> EvaluateRes:
     )
     configs_record = recordset.get_configs(f"{ins_str}.metrics")
 
-    metrics = _check_mapping_from_recordscalartype_to_scalar(configs_record.data)
+    # pylint: disable-next=protected-access
+    metrics = _check_mapping_from_recordscalartype_to_scalar(configs_record._data)
     status = _extract_status_from_recordset(ins_str, recordset)
 
     return EvaluateRes(
@@ -315,8 +310,8 @@ def evaluateres_to_recordset(evaluateres: EvaluateRes) -> RecordSet:
 def recordset_to_getparametersins(recordset: RecordSet) -> GetParametersIns:
     """Derive GetParametersIns from a RecordSet object."""
     config_record = recordset.get_configs("getparametersins.config")
-
-    config_dict = _check_mapping_from_recordscalartype_to_scalar(config_record.data)
+    # pylint: disable-next=protected-access
+    config_dict = _check_mapping_from_recordscalartype_to_scalar(config_record._data)
 
     return GetParametersIns(config=config_dict)
 
@@ -367,7 +362,8 @@ def recordset_to_getparametersres(
 def recordset_to_getpropertiesins(recordset: RecordSet) -> GetPropertiesIns:
     """Derive GetPropertiesIns from a RecordSet object."""
     config_record = recordset.get_configs("getpropertiesins.config")
-    config_dict = _check_mapping_from_recordscalartype_to_scalar(config_record.data)
+    # pylint: disable-next=protected-access
+    config_dict = _check_mapping_from_recordscalartype_to_scalar(config_record._data)
 
     return GetPropertiesIns(config=config_dict)
 
@@ -386,7 +382,8 @@ def recordset_to_getpropertiesres(recordset: RecordSet) -> GetPropertiesRes:
     """Derive GetPropertiesRes from a RecordSet object."""
     res_str = "getpropertiesres"
     config_record = recordset.get_configs(f"{res_str}.properties")
-    properties = _check_mapping_from_recordscalartype_to_scalar(config_record.data)
+    # pylint: disable-next=protected-access
+    properties = _check_mapping_from_recordscalartype_to_scalar(config_record._data)
 
     status = _extract_status_from_recordset(res_str, recordset=recordset)
 
diff --git a/src/py/flwr/common/recordset_test.py b/src/py/flwr/common/recordset_test.py
index cb199813f450..66560d8337f9 100644
--- a/src/py/flwr/common/recordset_test.py
+++ b/src/py/flwr/common/recordset_test.py
@@ -20,15 +20,12 @@
 import numpy as np
 import pytest
 
-from .configsrecord import ConfigsRecord
-from .metricsrecord import MetricsRecord
-from .parameter import ndarrays_to_parameters, parameters_to_ndarrays
-from .parametersrecord import Array, ParametersRecord
-from .recordset_compat import (
+from flwr.common.parameter import ndarrays_to_parameters, parameters_to_ndarrays
+from flwr.common.recordset_compat import (
     parameters_to_parametersrecord,
     parametersrecord_to_parameters,
 )
-from .typing import (
+from flwr.common.typing import (
     ConfigsRecordValues,
     MetricsRecordValues,
     NDArray,
@@ -36,6 +33,8 @@
     Parameters,
 )
 
+from . import Array, ConfigsRecord, MetricsRecord, ParametersRecord
+
 
 def get_ndarrays() -> NDArrays:
     """Return list of NumPy arrays."""
@@ -124,15 +123,14 @@ def test_parameters_to_parametersrecord_and_back(
 def test_set_parameters_while_keeping_intputs() -> None:
     """Tests keep_input functionality in ParametersRecord."""
     # Adding parameters to a record that doesn't erase entries in the input `array_dict`
-    p_record = ParametersRecord(keep_input=True)
     array_dict = OrderedDict(
         {str(i): ndarray_to_array(ndarray) for i, ndarray in enumerate(get_ndarrays())}
     )
-    p_record.set_parameters(array_dict, keep_input=True)
+    p_record = ParametersRecord(array_dict, keep_input=True)
 
     # Creating a second parametersrecord passing the same `array_dict` (not erased)
     p_record_2 = ParametersRecord(array_dict)
-    assert p_record.data == p_record_2.data
+    assert p_record == p_record_2
 
     # Now it should be empty (the second ParametersRecord wasn't flagged to keep it)
     assert len(array_dict) == 0
@@ -144,7 +142,7 @@ def test_set_parameters_with_correct_types() -> None:
     array_dict = OrderedDict(
         {str(i): ndarray_to_array(ndarray) for i, ndarray in enumerate(get_ndarrays())}
     )
-    p_record.set_parameters(array_dict)
+    p_record.update(array_dict)
 
 
 @pytest.mark.parametrize(
@@ -169,7 +167,7 @@ def test_set_parameters_with_incorrect_types(
     }
 
     with pytest.raises(TypeError):
-        p_record.set_parameters(array_dict)  # type: ignore
+        p_record.update(array_dict)
 
 
 @pytest.mark.parametrize(
@@ -197,10 +195,10 @@ def test_set_metrics_to_metricsrecord_with_correct_types(
     )
 
     # Add metric
-    m_record.set_metrics(my_metrics)
+    m_record.update(my_metrics)
 
     # Check metrics are actually added
-    assert my_metrics == m_record.data
+    assert my_metrics == m_record
 
 
 @pytest.mark.parametrize(
@@ -250,7 +248,7 @@ def test_set_metrics_to_metricsrecord_with_incorrect_types(
     )
 
     with pytest.raises(TypeError):
-        m_record.set_metrics(my_metrics)  # type: ignore
+        m_record.update(my_metrics)
 
 
 @pytest.mark.parametrize(
@@ -264,8 +262,6 @@ def test_set_metrics_to_metricsrecord_with_and_without_keeping_input(
     keep_input: bool,
 ) -> None:
     """Test keep_input functionality for MetricsRecord."""
-    m_record = MetricsRecord(keep_input=keep_input)
-
     # constructing a valid input
     labels = [1, 2.0]
     arrays = get_ndarrays()
@@ -276,14 +272,14 @@ def test_set_metrics_to_metricsrecord_with_and_without_keeping_input(
     my_metrics_copy = my_metrics.copy()
 
     # Add metric
-    m_record.set_metrics(my_metrics, keep_input=keep_input)
+    m_record = MetricsRecord(my_metrics, keep_input=keep_input)
 
     # Check metrics are actually added
     # Check that input dict has been emptied when enabled such behaviour
     if keep_input:
-        assert my_metrics == m_record.data
+        assert my_metrics == m_record
     else:
-        assert my_metrics_copy == m_record.data
+        assert my_metrics_copy == m_record
         assert len(my_metrics) == 0
 
 
@@ -318,7 +314,7 @@ def test_set_configs_to_configsrecord_with_correct_types(
     c_record = ConfigsRecord(my_configs)
 
     # check values are actually there
-    assert c_record.data == my_configs
+    assert c_record == my_configs
 
 
 @pytest.mark.parametrize(
@@ -352,14 +348,14 @@ def test_set_configs_to_configsrecord_with_incorrect_types(
     value_fn: Callable[[NDArray], Union[NDArray, Dict[str, NDArray], List[float]]],
 ) -> None:
     """Test adding configs of various unsupported types to a ConfigsRecord."""
-    m_record = ConfigsRecord()
+    c_record = ConfigsRecord()
 
     labels = [1, 2.0]
     arrays = get_ndarrays()
 
-    my_metrics = OrderedDict(
+    my_configs = OrderedDict(
         {key_type(label): value_fn(arr) for label, arr in zip(labels, arrays)}
     )
 
     with pytest.raises(TypeError):
-        m_record.set_configs(my_metrics)  # type: ignore
+        c_record.update(my_configs)
diff --git a/src/py/flwr/common/serde.py b/src/py/flwr/common/serde.py
index 530597d89807..eb2d34511d4e 100644
--- a/src/py/flwr/common/serde.py
+++ b/src/py/flwr/common/serde.py
@@ -43,12 +43,9 @@
 )
 
 # pylint: enable=E0611
-from . import typing
-from .configsrecord import ConfigsRecord
+from . import Array, ConfigsRecord, MetricsRecord, ParametersRecord, RecordSet, typing
 from .message import Message, Metadata
-from .metricsrecord import MetricsRecord
-from .parametersrecord import Array, ParametersRecord
-from .recordset import RecordSet
+from .typeddict import TypedDict
 
 #  === Parameters message ===
 
@@ -414,7 +411,9 @@ def _record_value_from_proto(value_proto: GrpcMessage) -> Any:
 
 
 def _record_value_dict_to_proto(
-    value_dict: Dict[str, Any], allowed_types: List[type], value_proto_class: Type[T]
+    value_dict: TypedDict[str, Any],
+    allowed_types: List[type],
+    value_proto_class: Type[T],
 ) -> Dict[str, T]:
     """Serialize the record value dict to ProtoBuf.
 
@@ -456,8 +455,8 @@ def array_from_proto(array_proto: ProtoArray) -> Array:
 def parameters_record_to_proto(record: ParametersRecord) -> ProtoParametersRecord:
     """Serialize ParametersRecord to ProtoBuf."""
     return ProtoParametersRecord(
-        data_keys=record.data.keys(),
-        data_values=map(array_to_proto, record.data.values()),
+        data_keys=record.keys(),
+        data_values=map(array_to_proto, record.values()),
     )
 
 
@@ -476,9 +475,7 @@ def parameters_record_from_proto(
 def metrics_record_to_proto(record: MetricsRecord) -> ProtoMetricsRecord:
     """Serialize MetricsRecord to ProtoBuf."""
     return ProtoMetricsRecord(
-        data=_record_value_dict_to_proto(
-            record.data, [float, int], ProtoMetricsRecordValue
-        )
+        data=_record_value_dict_to_proto(record, [float, int], ProtoMetricsRecordValue)
     )
 
 
@@ -497,7 +494,9 @@ def configs_record_to_proto(record: ConfigsRecord) -> ProtoConfigsRecord:
     """Serialize ConfigsRecord to ProtoBuf."""
     return ProtoConfigsRecord(
         data=_record_value_dict_to_proto(
-            record.data, [bool, int, float, str, bytes], ProtoConfigsRecordValue
+            record,
+            [bool, int, float, str, bytes],
+            ProtoConfigsRecordValue,
         )
     )
 
diff --git a/src/py/flwr/common/serde_test.py b/src/py/flwr/common/serde_test.py
index 9a38d7e5ecee..1c548b4f9e24 100644
--- a/src/py/flwr/common/serde_test.py
+++ b/src/py/flwr/common/serde_test.py
@@ -28,12 +28,8 @@
 from flwr.proto.recordset_pb2 import RecordSet as ProtoRecordSet
 
 # pylint: enable=E0611
-from . import typing
-from .configsrecord import ConfigsRecord
+from . import Array, ConfigsRecord, MetricsRecord, ParametersRecord, RecordSet, typing
 from .message import Message, Metadata
-from .metricsrecord import MetricsRecord
-from .parametersrecord import Array, ParametersRecord
-from .recordset import RecordSet
 from .serde import (
     array_from_proto,
     array_to_proto,
@@ -254,7 +250,7 @@ def test_parameters_record_serialization_deserialization() -> None:
 
     # Assert
     assert isinstance(proto, ProtoParametersRecord)
-    assert original.data == deserialized.data
+    assert original == deserialized
 
 
 def test_metrics_record_serialization_deserialization() -> None:
@@ -269,7 +265,7 @@ def test_metrics_record_serialization_deserialization() -> None:
 
     # Assert
     assert isinstance(proto, ProtoMetricsRecord)
-    assert original.data == deserialized.data
+    assert original == deserialized
 
 
 def test_configs_record_serialization_deserialization() -> None:
@@ -284,7 +280,7 @@ def test_configs_record_serialization_deserialization() -> None:
 
     # Assert
     assert isinstance(proto, ProtoConfigsRecord)
-    assert original.data == deserialized.data
+    assert original == deserialized
 
 
 def test_recordset_serialization_deserialization() -> None:
diff --git a/src/py/flwr/common/typeddict.py b/src/py/flwr/common/typeddict.py
new file mode 100644
index 000000000000..23d70dc4f7e8
--- /dev/null
+++ b/src/py/flwr/common/typeddict.py
@@ -0,0 +1,113 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Typed dict base class for *Records."""
+
+
+from typing import Any, Callable, Dict, Generic, Iterator, Tuple, TypeVar, cast
+
+K = TypeVar("K")  # Key type
+V = TypeVar("V")  # Value type
+
+
+class TypedDict(Generic[K, V]):
+    """Typed dictionary."""
+
+    def __init__(
+        self, check_key_fn: Callable[[K], None], check_value_fn: Callable[[V], None]
+    ):
+        self._data: Dict[K, V] = {}
+        self._check_key_fn = check_key_fn
+        self._check_value_fn = check_value_fn
+
+    def __setitem__(self, key: K, value: V) -> None:
+        """Set the given key to the given value after type checking."""
+        # Check the types of key and value
+        self._check_key_fn(key)
+        self._check_value_fn(value)
+        # Set key-value pair
+        self._data[key] = value
+
+    def __delitem__(self, key: K) -> None:
+        """Remove the item with the specified key."""
+        del self._data[key]
+
+    def __getitem__(self, item: K) -> V:
+        """Return the value for the specified key."""
+        return self._data[item]
+
+    def __iter__(self) -> Iterator[K]:
+        """Yield an iterator over the keys of the dictionary."""
+        return iter(self._data)
+
+    def __repr__(self) -> str:
+        """Return a string representation of the dictionary."""
+        return self._data.__repr__()
+
+    def __len__(self) -> int:
+        """Return the number of items in the dictionary."""
+        return len(self._data)
+
+    def __contains__(self, key: K) -> bool:
+        """Check if the dictionary contains the specified key."""
+        return key in self._data
+
+    def __eq__(self, other: object) -> bool:
+        """Compare this instance to another dictionary or TypedDict."""
+        if isinstance(other, TypedDict):
+            return self._data == other._data
+        if isinstance(other, dict):
+            return self._data == other
+        return NotImplemented
+
+    def items(self) -> Iterator[Tuple[K, V]]:
+        """R.items() -> a set-like object providing a view on R's items."""
+        return cast(Iterator[Tuple[K, V]], self._data.items())
+
+    def keys(self) -> Iterator[K]:
+        """R.keys() -> a set-like object providing a view on R's keys."""
+        return cast(Iterator[K], self._data.keys())
+
+    def values(self) -> Iterator[V]:
+        """R.values() -> an object providing a view on R's values."""
+        return cast(Iterator[V], self._data.values())
+
+    def update(self, *args: Any, **kwargs: Any) -> None:
+        """R.update([E, ]**F) -> None.
+
+        Update R from dict/iterable E and F.
+        """
+        for key, value in dict(*args, **kwargs).items():
+            self[key] = value
+
+    def pop(self, key: K) -> V:
+        """R.pop(k[,d]) -> v, remove specified key and return the corresponding value.
+
+        If key is not found, d is returned if given, otherwise KeyError is raised.
+        """
+        return self._data.pop(key)
+
+    def get(self, key: K, default: V) -> V:
+        """R.get(k[,d]) -> R[k] if k in R, else d.
+
+        d defaults to None.
+        """
+        return self._data.get(key, default)
+
+    def clear(self) -> None:
+        """R.clear() -> None.
+
+        Remove all items from R.
+        """
+        self._data.clear()

From 42557a10d574ff019bd0070ca1680096af39575b Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Tue, 20 Feb 2024 15:38:47 +0000
Subject: [PATCH 037/102] Move `*Record` and `RecordSet` to `record` package
 (#2984)

---
 e2e/bare/client.py                            |  2 +-
 e2e/pytorch/client.py                         |  2 +-
 examples/secaggplus-mt/workflows.py           |  2 +-
 src/py/flwr/client/grpc_client/connection.py  |  9 ++++--
 .../client/grpc_client/connection_test.py     |  3 +-
 .../client/message_handler/message_handler.py |  3 +-
 .../message_handler/task_handler_test.py      |  3 +-
 src/py/flwr/client/node_state_tests.py        |  3 +-
 src/py/flwr/common/__init__.py                | 10 +++----
 src/py/flwr/common/context.py                 |  2 +-
 src/py/flwr/common/message.py                 |  2 +-
 src/py/flwr/common/record/__init__.py         | 28 +++++++++++++++++++
 .../flwr/common/{ => record}/configsrecord.py |  0
 .../flwr/common/{ => record}/metricsrecord.py |  0
 .../common/{ => record}/parametersrecord.py   |  0
 src/py/flwr/common/{ => record}/recordset.py  |  0
 .../common/{ => record}/recordset_test.py     |  0
 src/py/flwr/common/{ => record}/typeddict.py  |  0
 src/py/flwr/common/serde.py                   |  2 +-
 src/py/flwr/server/driver/driver.py           |  3 +-
 src/py/flwr/server/server_app_test.py         |  3 +-
 .../ray_transport/ray_client_proxy_test.py    | 11 ++++++--
 22 files changed, 61 insertions(+), 27 deletions(-)
 create mode 100644 src/py/flwr/common/record/__init__.py
 rename src/py/flwr/common/{ => record}/configsrecord.py (100%)
 rename src/py/flwr/common/{ => record}/metricsrecord.py (100%)
 rename src/py/flwr/common/{ => record}/parametersrecord.py (100%)
 rename src/py/flwr/common/{ => record}/recordset.py (100%)
 rename src/py/flwr/common/{ => record}/recordset_test.py (100%)
 rename src/py/flwr/common/{ => record}/typeddict.py (100%)

diff --git a/e2e/bare/client.py b/e2e/bare/client.py
index a9425b39778a..f044f33b0a75 100644
--- a/e2e/bare/client.py
+++ b/e2e/bare/client.py
@@ -3,7 +3,7 @@
 import flwr as fl
 import numpy as np
 
-from flwr.common.configsrecord import ConfigsRecord
+from flwr.common import ConfigsRecord
 
 SUBSET_SIZE = 1000
 STATE_VAR = 'timestamp'
diff --git a/e2e/pytorch/client.py b/e2e/pytorch/client.py
index 0f1b8e159f7d..6ac9acf92505 100644
--- a/e2e/pytorch/client.py
+++ b/e2e/pytorch/client.py
@@ -11,7 +11,7 @@
 from tqdm import tqdm
 
 import flwr as fl
-from flwr.common.configsrecord import ConfigsRecord
+from flwr.common import ConfigsRecord
 
 # #############################################################################
 # 1. Regular PyTorch pipeline: nn.Module, train, test, and DataLoader
diff --git a/examples/secaggplus-mt/workflows.py b/examples/secaggplus-mt/workflows.py
index c9d0190555eb..877a5b136485 100644
--- a/examples/secaggplus-mt/workflows.py
+++ b/examples/secaggplus-mt/workflows.py
@@ -60,7 +60,7 @@
 from flwr.common.constant import TASK_TYPE_FIT
 from flwr.common import RecordSet
 from flwr.common import recordset_compat as compat
-from flwr.common.configsrecord import ConfigsRecord
+from flwr.common import ConfigsRecord
 
 
 LOG_EXPLAIN = True
diff --git a/src/py/flwr/client/grpc_client/connection.py b/src/py/flwr/client/grpc_client/connection.py
index c59c33d7ae8e..850090d44daf 100644
--- a/src/py/flwr/client/grpc_client/connection.py
+++ b/src/py/flwr/client/grpc_client/connection.py
@@ -22,10 +22,15 @@
 from queue import Queue
 from typing import Callable, Iterator, Optional, Tuple, Union, cast
 
-from flwr.common import GRPC_MAX_MESSAGE_LENGTH, Message, Metadata, RecordSet
+from flwr.common import (
+    GRPC_MAX_MESSAGE_LENGTH,
+    ConfigsRecord,
+    Message,
+    Metadata,
+    RecordSet,
+)
 from flwr.common import recordset_compat as compat
 from flwr.common import serde
-from flwr.common.configsrecord import ConfigsRecord
 from flwr.common.constant import (
     MESSAGE_TYPE_EVALUATE,
     MESSAGE_TYPE_FIT,
diff --git a/src/py/flwr/client/grpc_client/connection_test.py b/src/py/flwr/client/grpc_client/connection_test.py
index 9f0aa3b8980c..82dd0fcdd997 100644
--- a/src/py/flwr/client/grpc_client/connection_test.py
+++ b/src/py/flwr/client/grpc_client/connection_test.py
@@ -23,9 +23,8 @@
 
 import grpc
 
-from flwr.common import Message, Metadata, RecordSet
+from flwr.common import ConfigsRecord, Message, Metadata, RecordSet
 from flwr.common import recordset_compat as compat
-from flwr.common.configsrecord import ConfigsRecord
 from flwr.common.constant import MESSAGE_TYPE_GET_PROPERTIES
 from flwr.common.typing import Code, GetPropertiesRes, Status
 from flwr.proto.transport_pb2 import (  # pylint: disable=E0611
diff --git a/src/py/flwr/client/message_handler/message_handler.py b/src/py/flwr/client/message_handler/message_handler.py
index 643128f31061..0f98ade24225 100644
--- a/src/py/flwr/client/message_handler/message_handler.py
+++ b/src/py/flwr/client/message_handler/message_handler.py
@@ -24,8 +24,7 @@
     maybe_call_get_properties,
 )
 from flwr.client.typing import ClientFn
-from flwr.common import Context, Message, Metadata, RecordSet
-from flwr.common.configsrecord import ConfigsRecord
+from flwr.common import ConfigsRecord, Context, Message, Metadata, RecordSet
 from flwr.common.constant import (
     MESSAGE_TYPE_EVALUATE,
     MESSAGE_TYPE_FIT,
diff --git a/src/py/flwr/client/message_handler/task_handler_test.py b/src/py/flwr/client/message_handler/task_handler_test.py
index fa204198e6b6..c8b9e14737ff 100644
--- a/src/py/flwr/client/message_handler/task_handler_test.py
+++ b/src/py/flwr/client/message_handler/task_handler_test.py
@@ -16,8 +16,7 @@
 
 
 from flwr.client.message_handler.task_handler import get_task_ins, validate_task_ins
-from flwr.common import serde
-from flwr.common.recordset import RecordSet
+from flwr.common import RecordSet, serde
 from flwr.proto.fleet_pb2 import PullTaskInsResponse  # pylint: disable=E0611
 from flwr.proto.task_pb2 import Task, TaskIns  # pylint: disable=E0611
 
diff --git a/src/py/flwr/client/node_state_tests.py b/src/py/flwr/client/node_state_tests.py
index 20885880983f..fddcd8ed4755 100644
--- a/src/py/flwr/client/node_state_tests.py
+++ b/src/py/flwr/client/node_state_tests.py
@@ -16,8 +16,7 @@
 
 
 from flwr.client.node_state import NodeState
-from flwr.common import Context
-from flwr.common.configsrecord import ConfigsRecord
+from flwr.common import ConfigsRecord, Context
 from flwr.proto.task_pb2 import TaskIns  # pylint: disable=E0611
 
 
diff --git a/src/py/flwr/common/__init__.py b/src/py/flwr/common/__init__.py
index a6e4d6cb5cc0..13d168b68232 100644
--- a/src/py/flwr/common/__init__.py
+++ b/src/py/flwr/common/__init__.py
@@ -15,7 +15,6 @@
 """Common components shared between server and client."""
 
 
-from .configsrecord import ConfigsRecord as ConfigsRecord
 from .context import Context as Context
 from .date import now as now
 from .grpc import GRPC_MAX_MESSAGE_LENGTH
@@ -23,14 +22,15 @@
 from .logger import log as log
 from .message import Message as Message
 from .message import Metadata as Metadata
-from .metricsrecord import MetricsRecord as MetricsRecord
 from .parameter import bytes_to_ndarray as bytes_to_ndarray
 from .parameter import ndarray_to_bytes as ndarray_to_bytes
 from .parameter import ndarrays_to_parameters as ndarrays_to_parameters
 from .parameter import parameters_to_ndarrays as parameters_to_ndarrays
-from .parametersrecord import Array as Array
-from .parametersrecord import ParametersRecord as ParametersRecord
-from .recordset import RecordSet as RecordSet
+from .record import Array as Array
+from .record import ConfigsRecord as ConfigsRecord
+from .record import MetricsRecord as MetricsRecord
+from .record import ParametersRecord as ParametersRecord
+from .record import RecordSet as RecordSet
 from .telemetry import EventType as EventType
 from .telemetry import event as event
 from .typing import ClientMessage as ClientMessage
diff --git a/src/py/flwr/common/context.py b/src/py/flwr/common/context.py
index 30c1131a206f..b6349307d150 100644
--- a/src/py/flwr/common/context.py
+++ b/src/py/flwr/common/context.py
@@ -17,7 +17,7 @@
 
 from dataclasses import dataclass
 
-from .recordset import RecordSet
+from .record import RecordSet
 
 
 @dataclass
diff --git a/src/py/flwr/common/message.py b/src/py/flwr/common/message.py
index 7735ad85323b..14dae0f6ee57 100644
--- a/src/py/flwr/common/message.py
+++ b/src/py/flwr/common/message.py
@@ -19,7 +19,7 @@
 
 from dataclasses import dataclass
 
-from .recordset import RecordSet
+from .record import RecordSet
 
 
 @dataclass
diff --git a/src/py/flwr/common/record/__init__.py b/src/py/flwr/common/record/__init__.py
new file mode 100644
index 000000000000..4aa79a4b041c
--- /dev/null
+++ b/src/py/flwr/common/record/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Record APIs."""
+
+from .configsrecord import ConfigsRecord
+from .metricsrecord import MetricsRecord
+from .parametersrecord import Array, ParametersRecord
+from .recordset import RecordSet
+
+__all__ = [
+    "Array",
+    "ConfigsRecord",
+    "MetricsRecord",
+    "ParametersRecord",
+    "RecordSet",
+]
diff --git a/src/py/flwr/common/configsrecord.py b/src/py/flwr/common/record/configsrecord.py
similarity index 100%
rename from src/py/flwr/common/configsrecord.py
rename to src/py/flwr/common/record/configsrecord.py
diff --git a/src/py/flwr/common/metricsrecord.py b/src/py/flwr/common/record/metricsrecord.py
similarity index 100%
rename from src/py/flwr/common/metricsrecord.py
rename to src/py/flwr/common/record/metricsrecord.py
diff --git a/src/py/flwr/common/parametersrecord.py b/src/py/flwr/common/record/parametersrecord.py
similarity index 100%
rename from src/py/flwr/common/parametersrecord.py
rename to src/py/flwr/common/record/parametersrecord.py
diff --git a/src/py/flwr/common/recordset.py b/src/py/flwr/common/record/recordset.py
similarity index 100%
rename from src/py/flwr/common/recordset.py
rename to src/py/flwr/common/record/recordset.py
diff --git a/src/py/flwr/common/recordset_test.py b/src/py/flwr/common/record/recordset_test.py
similarity index 100%
rename from src/py/flwr/common/recordset_test.py
rename to src/py/flwr/common/record/recordset_test.py
diff --git a/src/py/flwr/common/typeddict.py b/src/py/flwr/common/record/typeddict.py
similarity index 100%
rename from src/py/flwr/common/typeddict.py
rename to src/py/flwr/common/record/typeddict.py
diff --git a/src/py/flwr/common/serde.py b/src/py/flwr/common/serde.py
index eb2d34511d4e..8911cbe690de 100644
--- a/src/py/flwr/common/serde.py
+++ b/src/py/flwr/common/serde.py
@@ -45,7 +45,7 @@
 # pylint: enable=E0611
 from . import Array, ConfigsRecord, MetricsRecord, ParametersRecord, RecordSet, typing
 from .message import Message, Metadata
-from .typeddict import TypedDict
+from .record.typeddict import TypedDict
 
 #  === Parameters message ===
 
diff --git a/src/py/flwr/server/driver/driver.py b/src/py/flwr/server/driver/driver.py
index 932ec617d7b3..aa8dc1ddd6c0 100644
--- a/src/py/flwr/server/driver/driver.py
+++ b/src/py/flwr/server/driver/driver.py
@@ -18,8 +18,7 @@
 import time
 from typing import Iterable, List, Optional, Tuple
 
-from flwr.common.message import Message, Metadata
-from flwr.common.recordset import RecordSet
+from flwr.common import Message, Metadata, RecordSet
 from flwr.common.serde import message_from_taskres, message_to_taskins
 from flwr.proto.driver_pb2 import (  # pylint: disable=E0611
     CreateRunRequest,
diff --git a/src/py/flwr/server/server_app_test.py b/src/py/flwr/server/server_app_test.py
index 011b22b0d60d..38c0d6240d90 100644
--- a/src/py/flwr/server/server_app_test.py
+++ b/src/py/flwr/server/server_app_test.py
@@ -19,8 +19,7 @@
 
 import pytest
 
-from flwr.common.context import Context
-from flwr.common.recordset import RecordSet
+from flwr.common import Context, RecordSet
 from flwr.server import ServerApp, ServerConfig
 from flwr.server.driver import Driver
 
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
index 7cc6de0c7315..0db78e0e3bed 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
@@ -23,8 +23,15 @@
 
 from flwr.client import Client, NumPyClient
 from flwr.client.clientapp import ClientApp
-from flwr.common import Config, Context, Message, Metadata, RecordSet, Scalar
-from flwr.common.configsrecord import ConfigsRecord
+from flwr.common import (
+    Config,
+    ConfigsRecord,
+    Context,
+    Message,
+    Metadata,
+    RecordSet,
+    Scalar,
+)
 from flwr.common.constant import MESSAGE_TYPE_GET_PROPERTIES
 from flwr.common.recordset_compat import (
     getpropertiesins_to_recordset,

From 9a96202ff3d3598ca28b1d7aa72ae1837843b63b Mon Sep 17 00:00:00 2001
From: Ikko Eltociear Ashimine <eltociear@gmail.com>
Date: Wed, 21 Feb 2024 01:24:59 +0900
Subject: [PATCH 038/102] Update models.py (#2985)

---
 baselines/heterofl/heterofl/models.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/baselines/heterofl/heterofl/models.py b/baselines/heterofl/heterofl/models.py
index 9426ee8b2789..1ba040bbb0b9 100644
--- a/baselines/heterofl/heterofl/models.py
+++ b/baselines/heterofl/heterofl/models.py
@@ -62,7 +62,7 @@ def __init__(
         self.blocks = nn.Sequential(*blocks)
 
     def _get_norm(self, j: int):
-        """Return the relavant norm."""
+        """Return the relevant norm."""
         if self.model_config["norm"] == "bn":
             norm = nn.BatchNorm2d(
                 self.model_config["hidden_size"][j],
@@ -85,7 +85,7 @@ def _get_norm(self, j: int):
         return norm
 
     def _get_scale(self):
-        """Return the relavant scaler."""
+        """Return the relevant scaler."""
         if self.model_config["scale"]:
             scaler = _Scaler(self.model_config["rate"])
         else:

From 88f8e434489e7b99030664429d88bf66e0ecae66 Mon Sep 17 00:00:00 2001
From: Jack Cook <jbcook418@gmail.com>
Date: Tue, 20 Feb 2024 11:36:13 -0500
Subject: [PATCH 039/102] Fix small typos (#2986)

---
 examples/flower-in-30-minutes/tutorial.ipynb | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/examples/flower-in-30-minutes/tutorial.ipynb b/examples/flower-in-30-minutes/tutorial.ipynb
index 8f9eccf65b74..5a1209f1cbb5 100644
--- a/examples/flower-in-30-minutes/tutorial.ipynb
+++ b/examples/flower-in-30-minutes/tutorial.ipynb
@@ -631,11 +631,11 @@
     "\n",
     "\n",
     "class FlowerClient(fl.client.NumPyClient):\n",
-    "    def __init__(self, trainloader, vallodaer) -> None:\n",
+    "    def __init__(self, trainloader, valloader) -> None:\n",
     "        super().__init__()\n",
     "\n",
     "        self.trainloader = trainloader\n",
-    "        self.valloader = vallodaer\n",
+    "        self.valloader = valloader\n",
     "        self.model = Net(num_classes=10)\n",
     "\n",
     "    def set_parameters(self, parameters):\n",
@@ -714,7 +714,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_evalulate_fn(testloader):\n",
+    "def get_evaluate_fn(testloader):\n",
     "    \"\"\"This is a function that returns a function. The returned\n",
     "    function (i.e. `evaluate_fn`) will be executed by the strategy\n",
     "    at the end of each round to evaluate the stat of the global\n",
@@ -747,7 +747,7 @@
     "    fraction_fit=0.1,  # let's sample 10% of the client each round to do local training\n",
     "    fraction_evaluate=0.1,  # after each round, let's sample 20% of the clients to asses how well the global model is doing\n",
     "    min_available_clients=100,  # total number of clients available in the experiment\n",
-    "    evaluate_fn=get_evalulate_fn(testloader),\n",
+    "    evaluate_fn=get_evaluate_fn(testloader),\n",
     ")  # a callback to a function that the strategy can execute to evaluate the state of the global model on a centralised dataset"
    ]
   },
@@ -775,7 +775,7 @@
     "        \"\"\"Returns a FlowerClient containing the cid-th data partition\"\"\"\n",
     "\n",
     "        return FlowerClient(\n",
-    "            trainloader=trainloaders[int(cid)], vallodaer=valloaders[int(cid)]\n",
+    "            trainloader=trainloaders[int(cid)], valloader=valloaders[int(cid)]\n",
     "        ).to_client()\n",
     "\n",
     "    return client_fn\n",

From f4672b74dcafdbfe699f2e8dd302e47af4d05233 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Wed, 21 Feb 2024 09:15:11 +0100
Subject: [PATCH 040/102] Add Federated KaplanMeierFitter example (#2930)

Co-authored-by: jafermarq <javier@flower.ai>
Co-authored-by: Charles Beauville <charles@flower.ai>
---
 README.md                                     |   1 +
 .../federated-kaplan-meier-fitter/README.md   |  97 ++++++++++++
 .../_static/survival_function_centralized.png | Bin 0 -> 51741 bytes
 .../_static/survival_function_federated.png   | Bin 0 -> 47262 bytes
 .../centralized.py                            |  16 ++
 .../federated-kaplan-meier-fitter/client.py   |  68 ++++++++
 .../pyproject.toml                            |  18 +++
 .../requirements.txt                          |   5 +
 examples/federated-kaplan-meier-fitter/run.sh |  17 ++
 .../federated-kaplan-meier-fitter/server.py   | 147 ++++++++++++++++++
 10 files changed, 369 insertions(+)
 create mode 100644 examples/federated-kaplan-meier-fitter/README.md
 create mode 100644 examples/federated-kaplan-meier-fitter/_static/survival_function_centralized.png
 create mode 100644 examples/federated-kaplan-meier-fitter/_static/survival_function_federated.png
 create mode 100644 examples/federated-kaplan-meier-fitter/centralized.py
 create mode 100644 examples/federated-kaplan-meier-fitter/client.py
 create mode 100644 examples/federated-kaplan-meier-fitter/pyproject.toml
 create mode 100644 examples/federated-kaplan-meier-fitter/requirements.txt
 create mode 100755 examples/federated-kaplan-meier-fitter/run.sh
 create mode 100644 examples/federated-kaplan-meier-fitter/server.py

diff --git a/README.md b/README.md
index 00e0a277104a..b5c8bfdaa4b9 100644
--- a/README.md
+++ b/README.md
@@ -149,6 +149,7 @@ Other [examples](https://github.com/adap/flower/tree/main/examples):
 - Single-Machine Simulation of Federated Learning Systems ([PyTorch](https://github.com/adap/flower/tree/main/examples/simulation-pytorch)) ([Tensorflow](https://github.com/adap/flower/tree/main/examples/simulation-tensorflow))
 - [Comprehensive Flower+XGBoost](https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive)
 - [Flower through Docker Compose and with Grafana dashboard](https://github.com/adap/flower/tree/main/examples/flower-via-docker-compose)
+- [Flower with KaplanMeierFitter from the lifelines library](https://github.com/adap/flower/tree/main/examples/federated-kaplna-meier-fitter)
 
 ## Community
 
diff --git a/examples/federated-kaplan-meier-fitter/README.md b/examples/federated-kaplan-meier-fitter/README.md
new file mode 100644
index 000000000000..0c97c5bacc22
--- /dev/null
+++ b/examples/federated-kaplan-meier-fitter/README.md
@@ -0,0 +1,97 @@
+# Flower Example using KaplanMeierFitter
+
+This is an introductory example on **federated survival analysis** using [Flower](https://flower.dev/)
+and [lifelines](https://lifelines.readthedocs.io/en/stable/index.html) library.
+
+The aim of this example is to estimate the survival function using the
+[Kaplan-Meier Estimate](https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator) implemented in
+lifelines library (see [KaplanMeierFitter](https://lifelines.readthedocs.io/en/stable/fitters/univariate/KaplanMeierFitter.html#lifelines.fitters.kaplan_meier_fitter.KaplanMeierFitter)). The distributed/federated aspect of this example
+is the data sending to the server. You can think of it as a federated analytics example. However, it's worth noting that this procedure violates privacy since the raw data is exchanged.
+
+Finally, many other estimators beyond KaplanMeierFitter can be used with the provided strategy:
+AalenJohansenFitter, GeneralizedGammaFitter, LogLogisticFitter,
+SplineFitter, and WeibullFitter.
+
+We also use the [NatualPartitioner](https://flower.dev/docs/datasets/ref-api/flwr_datasets.partitioner.NaturalIdPartitioner.html#flwr_datasets.partitioner.NaturalIdPartitioner) from [Flower Datasets](https://flower.dev/docs/datasets/) to divide the data according to
+the group it comes from therefore to simulate the division that might occur.
+
+<p style="text-align:center;">
+<img src="_static/survival_function_federated.png" alt="Survival Function" width="600"/>
+</p>
+
+## Project Setup
+
+Start by cloning the example project. We prepared a single-line command that you can copy into your shell which will checkout the example for you:
+
+```shell
+$ git clone --depth=1 https://github.com/adap/flower.git _tmp && mv _tmp/examples/federated-kaplan-meier-fitter . && rm -rf _tmp && cd federated-kaplan-meier-fitter
+```
+
+This will create a new directory called `federated-kaplan-meier-fitter` containing the following files:
+
+```shell
+-- pyproject.toml
+-- requirements.txt
+-- client.py
+-- server.py
+-- centralized.py
+-- run.sh
+-- README.md
+```
+
+### Installing Dependencies
+
+Project dependencies (such as `lifelines` and `flwr`) are defined in `pyproject.toml` and `requirements.txt`. We recommend [Poetry](https://python-poetry.org/docs/) to install those dependencies and manage your virtual environment ([Poetry installation](https://python-poetry.org/docs/#installation)) or [pip](https://pip.pypa.io/en/latest/development/), but feel free to use a different way of installing dependencies and managing virtual environments if you have other preferences.
+
+#### Poetry
+
+```shell
+poetry install
+poetry shell
+```
+
+Poetry will install all your dependencies in a newly created virtual environment. To verify that everything works correctly you can run the following command:
+
+```shell
+poetry run python3 -c "import flwr"
+```
+
+If you don't see any errors you're good to go!
+
+#### pip
+
+Write the command below in your terminal to install the dependencies according to the configuration file requirements.txt.
+
+```shell
+pip install -r requirements.txt
+```
+
+## Run Federated Survival Analysis with Flower and lifelines's KaplanMeierFitter
+
+Afterwards you are ready to start the Flower server as well as the clients. You can simply start the server in a terminal as follows:
+
+```shell
+$ python3 server.py
+```
+
+Now you are ready to start the Flower clients which will participate in the learning. To do so simply open two more terminal windows and run the following commands.
+
+Start client 1 in the first terminal:
+
+```shell
+$ python3 client.py --node-id 0
+```
+
+Start client 2 in the second terminal:
+
+```shell
+$ python3 client.py --node-id 1
+```
+
+You will see that the server is printing survival function, median survival time and saves the plot with the survival function.
+
+You can also check that the results match the centralized version.
+
+```shell
+$ python3 centralized.py
+```
diff --git a/examples/federated-kaplan-meier-fitter/_static/survival_function_centralized.png b/examples/federated-kaplan-meier-fitter/_static/survival_function_centralized.png
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diff --git a/examples/federated-kaplan-meier-fitter/centralized.py b/examples/federated-kaplan-meier-fitter/centralized.py
new file mode 100644
index 000000000000..4d0aef4890cf
--- /dev/null
+++ b/examples/federated-kaplan-meier-fitter/centralized.py
@@ -0,0 +1,16 @@
+import matplotlib.pyplot as plt
+from lifelines import KaplanMeierFitter
+from lifelines.datasets import load_waltons
+
+if __name__ == "__main__":
+    X = load_waltons()
+    fitter = KaplanMeierFitter()
+    fitter.fit(X["T"], X["E"])
+    print("Survival function")
+    print(fitter.survival_function_)
+    print("Mean survival time:")
+    print(fitter.median_survival_time_)
+    fitter.plot_survival_function()
+    plt.title("Survival function of fruit flies (Walton's data)", fontsize=16)
+    plt.savefig("./survival_function_centralized.png", dpi=200)
+    print("Centralized survival function saved.")
diff --git a/examples/federated-kaplan-meier-fitter/client.py b/examples/federated-kaplan-meier-fitter/client.py
new file mode 100644
index 000000000000..6f3040757481
--- /dev/null
+++ b/examples/federated-kaplan-meier-fitter/client.py
@@ -0,0 +1,68 @@
+import argparse
+from typing import Dict, List, Tuple
+
+import flwr as fl
+import numpy as np
+from datasets import Dataset
+from flwr.common import NDArray, NDArrays
+from flwr_datasets.partitioner import NaturalIdPartitioner
+from lifelines.datasets import load_waltons
+
+
+class FlowerClient(fl.client.NumPyClient):
+    """Flower client that holds and sends the events and times data.
+
+    Parameters
+    ----------
+    times: NDArray
+        Times of the `events`.
+    events: NDArray
+        Events represented by 0 - no event, 1 - event occurred.
+
+    Raises
+    ------
+    ValueError
+        If the `times` and `events` are not the same shape.
+    """
+
+    def __init__(self, times: NDArray, events: NDArray):
+        if len(times) != len(events):
+            raise ValueError("The times and events arrays have to be same shape.")
+        self._times = times
+        self._events = events
+
+    def fit(
+        self, parameters: List[np.ndarray], config: Dict[str, str]
+    ) -> Tuple[NDArrays, int, Dict]:
+        return (
+            [self._times, self._events],
+            len(self._times),
+            {},
+        )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Flower")
+    parser.add_argument(
+        "--node-id",
+        type=int,
+        required=True,
+        help="Node id. Each node holds different part of the dataset.",
+    )
+    args = parser.parse_args()
+    partition_id = args.node_id
+
+    # Prepare data
+    X = load_waltons()
+    partitioner = NaturalIdPartitioner(partition_by="group")
+    partitioner.dataset = Dataset.from_pandas(X)
+    partition = partitioner.load_partition(partition_id).to_pandas()
+    events = partition["E"].values
+    times = partition["T"].values
+
+    # Start Flower client
+    client = FlowerClient(times=times, events=events).to_client()
+    fl.client.start_client(
+        server_address="127.0.0.1:8080",
+        client=client,
+    )
diff --git a/examples/federated-kaplan-meier-fitter/pyproject.toml b/examples/federated-kaplan-meier-fitter/pyproject.toml
new file mode 100644
index 000000000000..0a194f84d211
--- /dev/null
+++ b/examples/federated-kaplan-meier-fitter/pyproject.toml
@@ -0,0 +1,18 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "federated-kaplan-meier-fitter"
+version = "0.1.0"
+description = "Federated Kaplan Meier Fitter with Flower"
+authors = ["The Flower Authors <hello@flower.dev>"]
+maintainers = ["The Flower Authors <hello@flower.dev>"]
+
+[tool.poetry.dependencies]
+python = ">=3.9,<3.11"
+flwr = ">=1.0,<2.0"
+flwr-datasets = ">=0.0.2,<1.0.0"
+numpy = ">=1.23.2"
+pandas = ">=2.0.0"
+lifelines = ">=0.28.0"
diff --git a/examples/federated-kaplan-meier-fitter/requirements.txt b/examples/federated-kaplan-meier-fitter/requirements.txt
new file mode 100644
index 000000000000..1cad920ca048
--- /dev/null
+++ b/examples/federated-kaplan-meier-fitter/requirements.txt
@@ -0,0 +1,5 @@
+flwr>=1.0, <2.0
+flwr-datasets>=0.0.2, <1.0.0
+numpy>=1.23.2
+pandas>=2.0.0
+lifelines>=0.28.0
diff --git a/examples/federated-kaplan-meier-fitter/run.sh b/examples/federated-kaplan-meier-fitter/run.sh
new file mode 100755
index 000000000000..b82ea3c58fbe
--- /dev/null
+++ b/examples/federated-kaplan-meier-fitter/run.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+set -e
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
+
+echo "Starting server"
+python server.py &
+sleep 3  # Sleep for 3s to give the server enough time to start
+
+for i in $(seq 0 1); do
+    echo "Starting client $i"
+    python client.py --node-id "${i}" &
+done
+
+# This will allow you to use CTRL+C to stop all background processes
+trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM
+# Wait for all background processes to complete
+wait
diff --git a/examples/federated-kaplan-meier-fitter/server.py b/examples/federated-kaplan-meier-fitter/server.py
new file mode 100644
index 000000000000..72f0e8f70429
--- /dev/null
+++ b/examples/federated-kaplan-meier-fitter/server.py
@@ -0,0 +1,147 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Strategy that supports many univariate fitters from lifelines library."""
+
+from typing import Dict, List, Optional, Tuple, Union, Any
+
+import numpy as np
+import flwr as fl
+import matplotlib.pyplot as plt
+from flwr.common import (
+    FitIns,
+    Parameters,
+    Scalar,
+    EvaluateRes,
+    EvaluateIns,
+    FitRes,
+    parameters_to_ndarrays,
+)
+from flwr.server.client_manager import ClientManager
+from flwr.server.client_proxy import ClientProxy
+from flwr.server.strategy import Strategy
+from lifelines import KaplanMeierFitter
+
+
+class EventTimeFitterStrategy(Strategy):
+    """Federated strategy to aggregate the data that consist of events and times.
+
+    It works with the following uni-variate fitters from the lifelines library:
+    AalenJohansenFitter, GeneralizedGammaFitter, KaplanMeierFitter, LogLogisticFitter,
+    SplineFitter, WeibullFitter. Note that each of them might require slightly different
+    initialization but constructed fitter object are required to be passed.
+
+    This strategy recreates the event and time data based on the data received from the
+    nodes.
+
+    Parameters
+    ----------
+    min_num_clients: int
+        pass
+    fitter: Any
+        uni-variate fitter from lifelines library that works with event, time data e.g.
+        KaplanMeierFitter
+    """
+
+    def __init__(self, min_num_clients: int, fitter: Any):
+        # Fitter can be access after the federated training ends
+        self._min_num_clients = min_num_clients
+        self.fitter = fitter
+
+    def configure_fit(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, FitIns]]:
+        """Configure the fit method."""
+        config = {}
+        fit_ins = FitIns(parameters, config)
+        clients = client_manager.sample(
+            num_clients=client_manager.num_available(),
+            min_num_clients=self._min_num_clients,
+        )
+        return [(client, fit_ins) for client in clients]
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Merge data and perform the fitting of the fitter from lifelines library.
+
+        Assume just a single federated learning round. Assume the data comes as a list
+        with two elements of 1-dim numpy arrays of events and times.
+        """
+        remote_data = [
+            (parameters_to_ndarrays(fit_res.parameters)) for _, fit_res in results
+        ]
+
+        combined_times = remote_data[0][0]
+        combined_events = remote_data[0][1]
+
+        for te_list in remote_data[1:]:
+            combined_times = np.concatenate((combined_times, te_list[0]))
+            combined_events = np.concatenate((combined_events, te_list[1]))
+
+        args_sorted = np.argsort(combined_times)
+        sorted_times = combined_times[args_sorted]
+        sorted_events = combined_events[args_sorted]
+        self.fitter.fit(sorted_times, sorted_events)
+        return None, {}
+
+    # The methods below return None or empty results.
+    # They need to be implemented to since the methods are abstract in the parent class
+    def initialize_parameters(
+        self, client_manager: Optional[ClientManager] = None
+    ) -> Optional[Parameters]:
+        """No parameter initialization is needed."""
+        return None
+
+    def evaluate(
+        self, server_round: int, parameters: Parameters
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """No centralized evaluation."""
+        return None
+
+    def aggregate_evaluate(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, EvaluateRes]],
+        failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+    ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+        """No federated evaluation."""
+        return None, {}
+
+    def configure_evaluate(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, EvaluateIns]]:
+        """No federated evaluation."""
+        return []
+
+
+if __name__ == "__main__":
+    fitter = KaplanMeierFitter()  # You can choose other method that work on E, T data
+    strategy = EventTimeFitterStrategy(min_num_clients=2, fitter=fitter)
+    # Start Flower server
+    fl.server.start_server(
+        server_address="127.0.0.1:8080",
+        config=fl.server.ServerConfig(num_rounds=1),
+        strategy=strategy,
+    )
+    print("Survival function:")
+    print(strategy.fitter.survival_function_)
+    strategy.fitter.plot_survival_function()
+    plt.title("Survival function of fruit flies (Walton's data)", fontsize=16)
+    plt.savefig("./survival_function_federated.png", dpi=200)
+    print("Mean survival time:")
+    print(strategy.fitter.median_survival_time_)

From 2f1751b2c2933e692cc78c3ea8f2719ebf3fdbea Mon Sep 17 00:00:00 2001
From: Robert Steiner <robert@flower.dev>
Date: Wed, 21 Feb 2024 10:30:15 +0100
Subject: [PATCH 041/102] Update the top level domain in the codebase (#2952)

---
 .github/ISSUE_TEMPLATE/baseline_request.yml    |  8 ++++----
 .github/ISSUE_TEMPLATE/config.yml              |  8 ++++----
 .github/PULL_REQUEST_TEMPLATE.md               |  6 +++---
 CHANGELOG.md                                   |  2 +-
 baselines/baseline_template/pyproject.toml     | 10 +++++-----
 baselines/dasha/pyproject.toml                 |  8 ++++----
 baselines/depthfl/pyproject.toml               | 12 ++++++------
 baselines/fedavgm/pyproject.toml               |  6 +++---
 baselines/fedbn/pyproject.toml                 | 10 +++++-----
 baselines/fedmeta/pyproject.toml               | 14 +++++++-------
 baselines/fedmlb/pyproject.toml                |  8 ++++----
 baselines/fednova/pyproject.toml               | 14 +++++++-------
 baselines/fedpara/pyproject.toml               | 12 ++++++------
 baselines/fedper/pyproject.toml                | 18 +++++++++---------
 baselines/fedprox/pyproject.toml               | 14 +++++++-------
 baselines/fedstar/pyproject.toml               |  6 +++---
 baselines/fedvssl/pyproject.toml               | 12 ++++++------
 baselines/fedwav2vec2/pyproject.toml           | 14 +++++++-------
 baselines/fjord/pyproject.toml                 | 12 ++++++------
 baselines/flwr_baselines/pyproject.toml        | 10 +++++-----
 baselines/flwr_baselines/requirements.txt      |  6 +++---
 baselines/heterofl/pyproject.toml              | 16 ++++++++--------
 baselines/hfedxgboost/pyproject.toml           | 10 +++++-----
 baselines/moon/pyproject.toml                  | 14 +++++++-------
 baselines/niid_bench/pyproject.toml            | 12 ++++++------
 baselines/tamuna/pyproject.toml                | 14 +++++++-------
 datasets/e2e/pytorch/pyproject.toml            |  2 +-
 datasets/e2e/scikit-learn/pyproject.toml       |  2 +-
 datasets/e2e/tensorflow/pyproject.toml         |  2 +-
 datasets/pyproject.toml                        |  8 ++++----
 e2e/bare-https/pyproject.toml                  |  2 +-
 e2e/bare/pyproject.toml                        |  2 +-
 e2e/fastai/pyproject.toml                      |  2 +-
 e2e/jax/pyproject.toml                         |  2 +-
 e2e/opacus/pyproject.toml                      |  2 +-
 e2e/pandas/pyproject.toml                      |  2 +-
 e2e/pytorch-lightning/pyproject.toml           |  2 +-
 e2e/pytorch/pyproject.toml                     |  2 +-
 e2e/scikit-learn/pyproject.toml                |  4 ++--
 e2e/strategies/pyproject.toml                  |  2 +-
 e2e/tabnet/pyproject.toml                      |  6 +++---
 e2e/tensorflow/pyproject.toml                  |  2 +-
 examples/advanced-pytorch/pyproject.toml       |  4 ++--
 examples/advanced-tensorflow/pyproject.toml    |  6 +++---
 examples/android/pyproject.toml                |  6 +++---
 examples/app-pytorch/pyproject.toml            |  2 +-
 examples/custom-metrics/pyproject.toml         |  6 +++---
 examples/dp-sgd-mnist/pyproject.toml           |  8 ++++----
 examples/flower-in-30-minutes/tutorial.ipynb   | 12 ++++++------
 examples/ios/pyproject.toml                    |  2 +-
 .../pyproject.toml                             |  2 +-
 examples/opacus/pyproject.toml                 |  2 +-
 .../pyproject.toml                             |  2 +-
 .../pyproject.toml                             |  2 +-
 examples/quickstart-fastai/pyproject.toml      |  2 +-
 examples/quickstart-huggingface/pyproject.toml |  4 ++--
 examples/quickstart-jax/pyproject.toml         |  2 +-
 examples/quickstart-mlcube/pyproject.toml      |  6 +++---
 examples/quickstart-mlx/pyproject.toml         |  4 ++--
 examples/quickstart-mxnet/pyproject.toml       |  2 +-
 examples/quickstart-pandas/pyproject.toml      |  2 +-
 .../pyproject.toml                             |  2 +-
 examples/quickstart-pytorch/pyproject.toml     |  2 +-
 .../quickstart-sklearn-tabular/pyproject.toml  |  4 ++--
 examples/quickstart-tabnet/pyproject.toml      |  6 +++---
 examples/quickstart-tensorflow/pyproject.toml  |  6 +++---
 examples/secaggplus-mt/pyproject.toml          |  2 +-
 examples/simulation-pytorch/pyproject.toml     |  3 +--
 examples/simulation-pytorch/sim.ipynb          |  6 +++---
 examples/simulation-tensorflow/pyproject.toml  |  6 +++---
 examples/simulation-tensorflow/sim.ipynb       |  6 +++---
 examples/sklearn-logreg-mnist/pyproject.toml   |  4 ++--
 examples/vertical-fl/pyproject.toml            |  2 +-
 .../pyproject.toml                             |  4 ++--
 examples/xgboost-comprehensive/pyproject.toml  |  2 +-
 examples/xgboost-quickstart/pyproject.toml     |  2 +-
 pyproject.toml                                 |  6 +++---
 src/py/flwr/common/address_test.py             | 12 ++++++------
 src/py/flwr/simulation/app.py                  |  4 ++--
 79 files changed, 237 insertions(+), 238 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE/baseline_request.yml b/.github/ISSUE_TEMPLATE/baseline_request.yml
index 49ae922a94ad..8df8eec22a21 100644
--- a/.github/ISSUE_TEMPLATE/baseline_request.yml
+++ b/.github/ISSUE_TEMPLATE/baseline_request.yml
@@ -38,18 +38,18 @@ body:
     attributes:
       label: For first time contributors
       value: |
-        - [ ]  Read the [`first contribution` doc](https://flower.dev/docs/first-time-contributors.html)
+        - [ ]  Read the [`first contribution` doc](https://flower.ai/docs/first-time-contributors.html)
         - [ ]  Complete the Flower tutorial
         - [ ]  Read the Flower Baselines docs to get an overview:
-            - [ ]  [How to use Flower Baselines](https://flower.dev/docs/baselines/how-to-use-baselines.html)
-            - [ ]  [How to contribute a Flower Baseline](https://flower.dev/docs/baselines/how-to-contribute-baselines.html)
+            - [ ]  [How to use Flower Baselines](https://flower.ai/docs/baselines/how-to-use-baselines.html)
+            - [ ]  [How to contribute a Flower Baseline](https://flower.ai/docs/baselines/how-to-contribute-baselines.html)
   - type: checkboxes
     attributes:
       label: Prepare - understand the scope
       options:
         - label: Read the paper linked above
         - label: Decide which experiments you'd like to reproduce. The more the better!
-        - label: Follow the steps outlined in [Add a new Flower Baseline](https://flower.dev/docs/baselines/how-to-contribute-baselines.html#add-a-new-flower-baseline).
+        - label: Follow the steps outlined in [Add a new Flower Baseline](https://flower.ai/docs/baselines/how-to-contribute-baselines.html#add-a-new-flower-baseline).
         - label: You can use as reference [other baselines](https://github.com/adap/flower/tree/main/baselines) that the community merged following those steps.
   - type: checkboxes
     attributes:
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
index d0ab46d60b57..9e0ed23d4dbb 100644
--- a/.github/ISSUE_TEMPLATE/config.yml
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -1,14 +1,14 @@
 blank_issues_enabled: false
 contact_links:
   - name: Slack Channel
-    url: https://flower.dev/join-slack
+    url: https://flower.ai/join-slack
     about: Connect with other Flower users and contributors and discuss with them or ask them questions.
   - name: Discussion
     url: https://github.com/adap/flower/discussions
-    about: Ask about new features or general questions. Please use the discussion area in most of the cases instead of the issues. 
+    about: Ask about new features or general questions. Please use the discussion area in most of the cases instead of the issues.
   - name: Flower Issues
     url: https://github.com/adap/flower/issues
     about: Contribute new features/enhancements, report bugs, or improve the documentation.
   - name: Flower Mail
-    url: https://flower.dev/
-    about: If your project needs professional support please contact the Flower team (hello@flower.dev).
+    url: https://flower.ai/
+    about: If your project needs professional support please contact the Flower team (hello@flower.ai).
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index b9d4a0a23e23..0077bbab0909 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -36,10 +36,10 @@ Example: The variable `rnd` was renamed to `server_round` to improve readability
 
 - [ ] Implement proposed change
 - [ ] Write tests
-- [ ] Update [documentation](https://flower.dev/docs/writing-documentation.html)
+- [ ] Update [documentation](https://flower.ai/docs/writing-documentation.html)
 - [ ] Update the changelog entry below
 - [ ] Make CI checks pass
-- [ ] Ping maintainers on [Slack](https://flower.dev/join-slack/) (channel `#contributions`)
+- [ ] Ping maintainers on [Slack](https://flower.ai/join-slack/) (channel `#contributions`)
 
 <!--
 Inside the following 'Changelog entry' section, you should put the description of your changes that will be added to the changelog alongside your PR title.
@@ -68,7 +68,7 @@ Smaller PRs with good descriptions can be considered much more easily.
 
 If you have an urgent request or question, please use the Flower Slack:
 
-    https://flower.dev/join-slack/ (channel: #contributions)
+    https://flower.ai/join-slack/ (channel: #contributions)
 
 Thank you for contributing to Flower!
 -->
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 264548c87669..1f01c0e9717d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,5 @@
 # Changelog
 
-Flower changes are tracked as part of the documentation: [Flower Changelog](https://flower.dev/docs/changelog.html).
+Flower changes are tracked as part of the documentation: [Flower Changelog](https://flower.ai/docs/changelog.html).
 
 The changelog source can be edited here: `doc/source/changelog.rst`
diff --git a/baselines/baseline_template/pyproject.toml b/baselines/baseline_template/pyproject.toml
index da7516437f09..ba079ddf6573 100644
--- a/baselines/baseline_template/pyproject.toml
+++ b/baselines/baseline_template/pyproject.toml
@@ -7,11 +7,11 @@ name = "<BASELINE_NAME>" # <----- Ensure it matches the name of your baseline di
 version = "1.0.0"
 description = "Flower Baselines"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -80,10 +80,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/dasha/pyproject.toml b/baselines/dasha/pyproject.toml
index 6fa6eb028b51..f227293acc9d 100644
--- a/baselines/dasha/pyproject.toml
+++ b/baselines/dasha/pyproject.toml
@@ -9,9 +9,9 @@ description = "DASHA: Distributed nonconvex optimization with communication comp
 license = "Apache-2.0"
 authors = ["Alexander Tyurin <alexandertiurin@gmail.com>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -89,8 +89,8 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias,no-self-use,too-many-locals,too-many-instance-attributes"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
-generated-members="numpy.*, torch.*"
+signature-mutators = "hydra.main.main"
+generated-members = "numpy.*, torch.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/depthfl/pyproject.toml b/baselines/depthfl/pyproject.toml
index bd223ba78341..a1394c89434c 100644
--- a/baselines/depthfl/pyproject.toml
+++ b/baselines/depthfl/pyproject.toml
@@ -9,9 +9,9 @@ description = "DepthFL: Depthwise Federated Learning for Heterogeneous Clients"
 license = "Apache-2.0"
 authors = ["Minjae Kim <mjkim@snu.ac.kr>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -41,8 +41,8 @@ python = ">=3.10.0, <3.11.0"
 flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 matplotlib = "3.7.1"
-torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.13.1%2Bcu116-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.14.1%2Bcu116-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.13.1%2Bcu116-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.14.1%2Bcu116-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
@@ -84,10 +84,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/fedavgm/pyproject.toml b/baselines/fedavgm/pyproject.toml
index 22a5f3a53847..b1f50cdc4a3a 100644
--- a/baselines/fedavgm/pyproject.toml
+++ b/baselines/fedavgm/pyproject.toml
@@ -9,9 +9,9 @@ description = "FedAvgM: Measuring the effects of non-identical data distribution
 license = "Apache-2.0"
 authors = ["Gustavo Bertoli"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -85,7 +85,7 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/fedbn/pyproject.toml b/baselines/fedbn/pyproject.toml
index 0e46a33360db..ff142c6b2435 100644
--- a/baselines/fedbn/pyproject.toml
+++ b/baselines/fedbn/pyproject.toml
@@ -7,11 +7,11 @@ name = "fedbn"
 version = "1.0.0"
 description = "Flower Baselines"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -86,10 +86,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/fedmeta/pyproject.toml b/baselines/fedmeta/pyproject.toml
index 8f6bbe3f7fa1..aa5d2f3db618 100644
--- a/baselines/fedmeta/pyproject.toml
+++ b/baselines/fedmeta/pyproject.toml
@@ -9,9 +9,9 @@ description = "Implementation of FedMeta (Fei Chen et al. 2018)"
 license = "Apache-2.0"
 authors = ["Jinsoo Kim <wlstn25092303@gmail.com>", "Kangyoon Lee <keylee@gachon.ac.kr>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -37,13 +37,13 @@ classifiers = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.10.0, <3.11.0" 
+python = ">=3.10.0, <3.11.0"
 flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 matplotlib = "3.7.1"
 scikit-learn = "1.3.1"
-torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
 pillow = "9.5.0" # needed <10.0.0 for LEAF repo scripts
 
 [tool.poetry.dev-dependencies]
@@ -86,10 +86,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/fedmlb/pyproject.toml b/baselines/fedmlb/pyproject.toml
index c76b2b1548ce..2b21172f4679 100644
--- a/baselines/fedmlb/pyproject.toml
+++ b/baselines/fedmlb/pyproject.toml
@@ -9,9 +9,9 @@ description = "FedMLB Flower Baseline"
 license = "Apache-2.0"
 authors = ["Alessio Mora <alessio.mora@unibo.it>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -84,10 +84,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/fednova/pyproject.toml b/baselines/fednova/pyproject.toml
index c9cc257239e6..5856fbe262ba 100644
--- a/baselines/fednova/pyproject.toml
+++ b/baselines/fednova/pyproject.toml
@@ -7,11 +7,11 @@ name = "fednova" # <----- Ensure it matches the name of your baseline directory
 version = "1.0.0"
 description = "Flower Baselines"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -41,8 +41,8 @@ classifiers = [
 python = ">=3.10.0, <3.11.0"
 flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
-torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
 numpy = "1.21.6"
 matplotlib = "3.5.3"
 pandas = "1.3.5"
@@ -87,10 +87,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y,lr,K,N,m,n,v,mu,p,g,df"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/fedpara/pyproject.toml b/baselines/fedpara/pyproject.toml
index c5751c536043..f802b27a7af1 100644
--- a/baselines/fedpara/pyproject.toml
+++ b/baselines/fedpara/pyproject.toml
@@ -9,9 +9,9 @@ description = "Flower Baselines"
 license = "Apache-2.0"
 authors = ["Yahia Salaheldin Shaaban <yehia.salah.ms@alexu.edu.eg>", "Omar Mokhtar < >", "Roeia Amr < >"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -42,8 +42,8 @@ flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 matplotlib = "^3.7.2"
 tqdm = "^4.66.1"
-torch = {url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
@@ -85,10 +85,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y,m,w1,w2,W1,W2,n,r1,r2,r3,W,w,v,lr,f,r,T,a,b,c"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/fedper/pyproject.toml b/baselines/fedper/pyproject.toml
index 04aef92a8c06..84086d1f3660 100644
--- a/baselines/fedper/pyproject.toml
+++ b/baselines/fedper/pyproject.toml
@@ -7,11 +7,11 @@ name = "fedper" # <----- Ensure it matches the name of your baseline directory c
 version = "1.0.0"
 description = "Federated Learning with Personalization Layers"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>", "William Lindskog <williamlindskog@protonmail.com>"]
+authors = ["The Flower Authors <hello@flower.ai>", "William Lindskog <williamlindskog@protonmail.com>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -38,13 +38,13 @@ classifiers = [
 
 [tool.poetry.dependencies]
 python = ">=3.10.0, <3.11.0"  # don't change this
-flwr = {extras = ["simulation"], version = "1.5.0" }
+flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 pandas = "^2.0.3"
 matplotlib = "^3.7.2"
 tqdm = "^4.66.1"
-torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
@@ -86,10 +86,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint."TYPECHECK"]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
@@ -140,4 +140,4 @@ exclude = [
 ]
 
 [tool.ruff.pydocstyle]
-convention = "numpy"
\ No newline at end of file
+convention = "numpy"
diff --git a/baselines/fedprox/pyproject.toml b/baselines/fedprox/pyproject.toml
index a8a3513d8ddf..941a85443c84 100644
--- a/baselines/fedprox/pyproject.toml
+++ b/baselines/fedprox/pyproject.toml
@@ -7,11 +7,11 @@ name = "fedprox"
 version = "1.0.0"
 description = "Implementation of FedProx (Lin et al. 2020)"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -43,8 +43,8 @@ hydra-core = "1.3.2"
 matplotlib = "3.7.1"
 jupyter = "^1.0.0"
 pandas = "^2.0.3"
-torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
@@ -86,10 +86,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/fedstar/pyproject.toml b/baselines/fedstar/pyproject.toml
index b7109cdc7232..93d1eb2e042d 100644
--- a/baselines/fedstar/pyproject.toml
+++ b/baselines/fedstar/pyproject.toml
@@ -7,11 +7,11 @@ name = "fedstar" # <----- Ensure it matches the name of your baseline directory
 version = "1.0.0"
 description = "Federated Self-training for Semi-supervised Audio Recognition"
 license = "Apache-2.0"
-authors = ["Raj Parekh <rajparekhwc@gmail.com>", "The Flower Authors <hello@flower.dev>>"]
+authors = ["Raj Parekh <rajparekhwc@gmail.com>", "The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
diff --git a/baselines/fedvssl/pyproject.toml b/baselines/fedvssl/pyproject.toml
index 11d5372cafd2..fc359409dbe8 100644
--- a/baselines/fedvssl/pyproject.toml
+++ b/baselines/fedvssl/pyproject.toml
@@ -7,11 +7,11 @@ name = "fedvssl" # <----- Ensure it matches the name of your baseline directory
 version = "1.0.0"
 description = "Federated Self-supervised Learning for Video Understanding"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -46,7 +46,7 @@ mmengine = "0.10.0"
 yapf = "0.40.1"
 tensorboard = "2.15.1"
 numpy = "1.23.0"
-mmcv-full = { url = "https://download.openmmlab.com/mmcv/dist/cu117/torch1.13.0/mmcv_full-1.7.1-cp310-cp310-manylinux1_x86_64.whl"}
+mmcv-full = { url = "https://download.openmmlab.com/mmcv/dist/cu117/torch1.13.0/mmcv_full-1.7.1-cp310-cp310-manylinux1_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
@@ -88,7 +88,7 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,f,_,x,y,X,Y,mu,v"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [[tool.mypy.overrides]]
 module = [
@@ -100,7 +100,7 @@ follow_imports_for_stubs = true
 disallow_untyped_calls = false
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 # ignore-paths="fedvssl/CtP/"
 
 [[tool.mypy.overrides]]
diff --git a/baselines/fedwav2vec2/pyproject.toml b/baselines/fedwav2vec2/pyproject.toml
index 87cfb44e0a5a..551c1c697650 100644
--- a/baselines/fedwav2vec2/pyproject.toml
+++ b/baselines/fedwav2vec2/pyproject.toml
@@ -7,11 +7,11 @@ name = "fedwav2vec2" # <----- Ensure it matches the name of your baseline direct
 version = "1.0.0"
 description = "Federated Learning for ASR Based on wav2vec 2.0"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>", "Tuan Nguyen <tuanmanh.nguyen167@gmail.com>"]
+authors = ["The Flower Authors <hello@flower.ai>", "Tuan Nguyen <tuanmanh.nguyen167@gmail.com>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -42,8 +42,8 @@ flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 speechbrain = "0.5.15"
 pandas = "2.1.1"
-torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.13.1%2Bcu116-cp310-cp310-linux_x86_64.whl"}
-torchaudio = { url = "https://download.pytorch.org/whl/cu116/torchaudio-0.13.1%2Bcu116-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.13.1%2Bcu116-cp310-cp310-linux_x86_64.whl" }
+torchaudio = { url = "https://download.pytorch.org/whl/cu116/torchaudio-0.13.1%2Bcu116-cp310-cp310-linux_x86_64.whl" }
 transformers = "4.33.2"
 
 [tool.poetry.dev-dependencies]
@@ -86,10 +86,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/fjord/pyproject.toml b/baselines/fjord/pyproject.toml
index 6d7466029263..197bb53caf89 100644
--- a/baselines/fjord/pyproject.toml
+++ b/baselines/fjord/pyproject.toml
@@ -9,9 +9,9 @@ description = "FjORD implementation of Federated Ordered Dropout in Flower"
 license = "Apache-2.0"
 authors = ["Steve Laskaridis <stevelaskaridis@gmail.com>", "Samuel Horvath <samohorvath11@gmail.com>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -44,8 +44,8 @@ matplotlib = "3.7.1"
 coloredlogs = "15.0.1"
 omegaconf = "2.3.0"
 tqdm = "4.65.0"
-torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
@@ -87,10 +87,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y,fl,lr,p,p_,bn,NUM_CLIENTS,od,m,g,ResNet18,FJORD_CONFIG_TYPE"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 
 [[tool.mypy.overrides]]
diff --git a/baselines/flwr_baselines/pyproject.toml b/baselines/flwr_baselines/pyproject.toml
index f5fb64744ff8..2d64b55981d7 100644
--- a/baselines/flwr_baselines/pyproject.toml
+++ b/baselines/flwr_baselines/pyproject.toml
@@ -7,11 +7,11 @@ name = "flwr_baselines"
 version = "1.0.0"
 description = "Flower Baselines"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -103,8 +103,8 @@ follow_imports = "skip"
 follow_imports_for_stubs = true
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*"
+generated-members = "numpy.*, torch.*"
 
 [tool.pylint.messages_control]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
diff --git a/baselines/flwr_baselines/requirements.txt b/baselines/flwr_baselines/requirements.txt
index 7b90b8a9bf1f..8d151e697532 100644
--- a/baselines/flwr_baselines/requirements.txt
+++ b/baselines/flwr_baselines/requirements.txt
@@ -1,9 +1,9 @@
 ##### Flower Baseline Dependencies
 # Under Apache-2.0 license
-# Authors: The Flower Authors <hello@flower.dev>
-# Website: https://flower.dev
+# Authors: The Flower Authors <hello@flower.ai>
+# Website: https://flower.ai
 # Repository: https://github.com/adap/flower
-# Documentation: https://flower.dev
+# Documentation: https://flower.ai
 
 ##### dependencies
 flwr >= 0.18.0
diff --git a/baselines/heterofl/pyproject.toml b/baselines/heterofl/pyproject.toml
index 0f72edf20345..dc03d2380fcb 100644
--- a/baselines/heterofl/pyproject.toml
+++ b/baselines/heterofl/pyproject.toml
@@ -7,11 +7,11 @@ name = "heterofl" # <----- Ensure it matches the name of your baseline directory
 version = "1.0.0"
 description = "HeteroFL : Computation And Communication Efficient Federated Learning For Heterogeneous Clients"
 license = "Apache-2.0"
-authors = ["M S Chaitanya Kumar <chaitanya.mangipudi2002@gmail.com>", "The Flower Authors <hello@flower.dev>"]
+authors = ["M S Chaitanya Kumar <chaitanya.mangipudi2002@gmail.com>", "The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -37,11 +37,11 @@ classifiers = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.10.0, <3.11.0" 
+python = ">=3.10.0, <3.11.0"
 flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
-torch = { url = "https://download.pytorch.org/whl/cu118/torch-2.1.0%2Bcu118-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu118/torchvision-0.16.0%2Bcu118-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu118/torch-2.1.0%2Bcu118-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu118/torchvision-0.16.0%2Bcu118-cp310-cp310-linux_x86_64.whl" }
 anytree = "^2.12.1"
 types-six = "^1.16.21.9"
 tqdm = "4.66.1"
@@ -86,11 +86,11 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 
 [[tool.mypy.overrides]]
diff --git a/baselines/hfedxgboost/pyproject.toml b/baselines/hfedxgboost/pyproject.toml
index 1fbbb85ac36b..11064c90b704 100644
--- a/baselines/hfedxgboost/pyproject.toml
+++ b/baselines/hfedxgboost/pyproject.toml
@@ -7,11 +7,11 @@ name = "hfedxgboost" # <----- Ensure it matches the name of your baseline direct
 version = "1.0.0"
 description = "HFedXgboost: Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>", "Aml Hassan Esmil  <aml.hassan.esmil@gmail.com>"]
+authors = ["The Flower Authors <hello@flower.ai>", "Aml Hassan Esmil  <aml.hassan.esmil@gmail.com>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -88,10 +88,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/moon/pyproject.toml b/baselines/moon/pyproject.toml
index b385cd90300c..f25ca6213dbe 100644
--- a/baselines/moon/pyproject.toml
+++ b/baselines/moon/pyproject.toml
@@ -7,11 +7,11 @@ name = "moon" # <----- Ensure it matches the name of your baseline directory con
 version = "1.0.0"
 description = "Model-Contrastive Federated Learning"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>", "Qinbin Li <liqinbin1998@gmail.com>"]
+authors = ["The Flower Authors <hello@flower.ai>", "Qinbin Li <liqinbin1998@gmail.com>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -42,8 +42,8 @@ flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 scikit-learn = "1.3.0"
 matplotlib = "3.8.0"
-torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.12.0%2Bcu116-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.13.0%2Bcu116-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.12.0%2Bcu116-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.13.0%2Bcu116-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
@@ -90,10 +90,10 @@ max-attributes = 15
 max-locals = 36
 max-branches = 20
 max-statements = 55
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/niid_bench/pyproject.toml b/baselines/niid_bench/pyproject.toml
index 6a7086806e40..8260ced95788 100644
--- a/baselines/niid_bench/pyproject.toml
+++ b/baselines/niid_bench/pyproject.toml
@@ -9,9 +9,9 @@ description = "Federated Learning on Non-IID Data Silos: An Experimental Study"
 license = "Apache-2.0"
 authors = ["Aashish Kolluri <aashishkolluri6@gmail.com>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -40,8 +40,8 @@ classifiers = [
 python = ">=3.10.0, <3.11.0"  # don't change this
 flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
-torch = { url = "https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp310-cp310-linux_x86_64.whl" }
 tqdm = "4.66.1"
 
 [tool.poetry.dev-dependencies]
@@ -84,10 +84,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/tamuna/pyproject.toml b/baselines/tamuna/pyproject.toml
index d004cfbd6f90..28fdaf0f2ca1 100644
--- a/baselines/tamuna/pyproject.toml
+++ b/baselines/tamuna/pyproject.toml
@@ -9,9 +9,9 @@ description = "TAMUNA: Doubly Accelerated Federated Learning with Local Training
 license = "Apache-2.0"
 authors = ["Ivan Agarský <agarskyivan@gmail.com>", "Grigory Malinovsky <grigorii.malinovskii@kaust.edu.sa>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -41,9 +41,9 @@ python = ">=3.10.0, <3.11.0"
 flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2"
 matplotlib = "3.7.1"
-torch = { url = "https://download.pytorch.org/whl/cu118/torch-2.0.1%2Bcu118-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu118/torchvision-0.15.2%2Bcu118-cp310-cp310-linux_x86_64.whl"}
-torchaudio = { url = "https://download.pytorch.org/whl/cu118/torchaudio-2.0.2%2Bcu118-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu118/torch-2.0.1%2Bcu118-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu118/torchvision-0.15.2%2Bcu118-cp310-cp310-linux_x86_64.whl" }
+torchaudio = { url = "https://download.pytorch.org/whl/cu118/torchaudio-2.0.2%2Bcu118-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
@@ -85,10 +85,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/datasets/e2e/pytorch/pyproject.toml b/datasets/e2e/pytorch/pyproject.toml
index 4565cce9f828..009ad2d74235 100644
--- a/datasets/e2e/pytorch/pyproject.toml
+++ b/datasets/e2e/pytorch/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "fds-e2e-pytorch"
 version = "0.1.0"
 description = "Flower Datasets with PyTorch"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/datasets/e2e/scikit-learn/pyproject.toml b/datasets/e2e/scikit-learn/pyproject.toml
index 7e22644566cf..48356e4a945f 100644
--- a/datasets/e2e/scikit-learn/pyproject.toml
+++ b/datasets/e2e/scikit-learn/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "fds-e2e-sklearn"
 version = "0.1.0"
 description = "Flower Datasets with scikit-learn"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/datasets/e2e/tensorflow/pyproject.toml b/datasets/e2e/tensorflow/pyproject.toml
index 64c67c7695a5..dbb6720219b2 100644
--- a/datasets/e2e/tensorflow/pyproject.toml
+++ b/datasets/e2e/tensorflow/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "fds-e2-tensorflow"
 version = "0.1.0"
 description = "Flower Datasets with TensorFlow"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/datasets/pyproject.toml b/datasets/pyproject.toml
index 20bd20d121e7..90a98c7c7a29 100644
--- a/datasets/pyproject.toml
+++ b/datasets/pyproject.toml
@@ -7,11 +7,11 @@ name = "flwr-datasets"
 version = "0.0.2"
 description = "Flower Datasets"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev/docs/datasets"
+documentation = "https://flower.ai/docs/datasets"
 keywords = [
     "flower",
     "fl",
@@ -56,7 +56,7 @@ python = "^3.8"
 numpy = "^1.21.0"
 datasets = "^2.14.3"
 pillow = { version = ">=6.2.1", optional = true }
-soundfile = {version = ">=0.12.1", optional = true}
+soundfile = { version = ">=0.12.1", optional = true }
 librosa = { version = ">=0.10.0.post2", optional = true }
 
 [tool.poetry.dev-dependencies]
diff --git a/e2e/bare-https/pyproject.toml b/e2e/bare-https/pyproject.toml
index 9489a43195f9..3afb7b57a084 100644
--- a/e2e/bare-https/pyproject.toml
+++ b/e2e/bare-https/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "bare_https_test"
 version = "0.1.0"
 description = "HTTPS-enabled bare Federated Learning test with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/e2e/bare/pyproject.toml b/e2e/bare/pyproject.toml
index cde8728f5c34..45ce7ea333af 100644
--- a/e2e/bare/pyproject.toml
+++ b/e2e/bare/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "bare_test"
 version = "0.1.0"
 description = "Bare Federated Learning test with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/e2e/fastai/pyproject.toml b/e2e/fastai/pyproject.toml
index 66fcb393d988..feed31f6d202 100644
--- a/e2e/fastai/pyproject.toml
+++ b/e2e/fastai/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-fastai"
 version = "0.1.0"
 description = "Fastai Federated Learning E2E test with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.10"
diff --git a/e2e/jax/pyproject.toml b/e2e/jax/pyproject.toml
index 3db32ea855eb..9a4af5dee59a 100644
--- a/e2e/jax/pyproject.toml
+++ b/e2e/jax/pyproject.toml
@@ -2,7 +2,7 @@
 name = "jax_example"
 version = "0.1.0"
 description = "JAX example training a linear regression model with federated learning"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/e2e/opacus/pyproject.toml b/e2e/opacus/pyproject.toml
index 1aee7c6ec6d9..ab4a727cc00b 100644
--- a/e2e/opacus/pyproject.toml
+++ b/e2e/opacus/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "opacus_e2e"
 version = "0.1.0"
 description = "Opacus E2E testing"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/e2e/pandas/pyproject.toml b/e2e/pandas/pyproject.toml
index b90037a31068..416dfeec3460 100644
--- a/e2e/pandas/pyproject.toml
+++ b/e2e/pandas/pyproject.toml
@@ -7,7 +7,7 @@ name = "quickstart-pandas"
 version = "0.1.0"
 description = "Pandas Federated Analytics Quickstart with Flower"
 authors = ["Ragy Haddad <ragy202@gmail.com>"]
-maintainers = ["The Flower Authors <hello@flower.dev>"]
+maintainers = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/e2e/pytorch-lightning/pyproject.toml b/e2e/pytorch-lightning/pyproject.toml
index 951349c03a04..88cddddf500f 100644
--- a/e2e/pytorch-lightning/pyproject.toml
+++ b/e2e/pytorch-lightning/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.masonry.api"
 name = "quickstart-pytorch-lightning"
 version = "0.1.0"
 description = "Federated Learning E2E test with Flower and PyTorch Lightning"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/e2e/pytorch/pyproject.toml b/e2e/pytorch/pyproject.toml
index 4aa326330c62..e538f1437df6 100644
--- a/e2e/pytorch/pyproject.toml
+++ b/e2e/pytorch/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-pytorch"
 version = "0.1.0"
 description = "PyTorch Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/e2e/scikit-learn/pyproject.toml b/e2e/scikit-learn/pyproject.toml
index 372ae1218bbe..50c07d31add7 100644
--- a/e2e/scikit-learn/pyproject.toml
+++ b/e2e/scikit-learn/pyproject.toml
@@ -7,8 +7,8 @@ name = "sklearn-mnist"
 version = "0.1.0"
 description = "Federated learning with scikit-learn and Flower"
 authors = [
-    "The Flower Authors <hello@flower.dev>",
-    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>"
+    "The Flower Authors <hello@flower.ai>",
+    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>",
 ]
 
 [tool.poetry.dependencies]
diff --git a/e2e/strategies/pyproject.toml b/e2e/strategies/pyproject.toml
index edfb16de59a6..5cc74b20fa24 100644
--- a/e2e/strategies/pyproject.toml
+++ b/e2e/strategies/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart_tensorflow"
 version = "0.1.0"
 description = "Keras Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/e2e/tabnet/pyproject.toml b/e2e/tabnet/pyproject.toml
index 7b47ffeb1470..b1abf382a24a 100644
--- a/e2e/tabnet/pyproject.toml
+++ b/e2e/tabnet/pyproject.toml
@@ -6,13 +6,13 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-tabnet"
 version = "0.1.0"
 description = "Tabnet Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = { path = "../../", develop = true, extras = ["simulation"] }
-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\""}
+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\"" }
 tensorflow_datasets = "4.9.2"
 tensorflow-io-gcs-filesystem = "<0.35.0"
 tabnet = "0.1.6"
diff --git a/e2e/tensorflow/pyproject.toml b/e2e/tensorflow/pyproject.toml
index 467e69a026b3..a7dbfe2305db 100644
--- a/e2e/tensorflow/pyproject.toml
+++ b/e2e/tensorflow/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-tensorflow"
 version = "0.1.0"
 description = "Keras Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/advanced-pytorch/pyproject.toml b/examples/advanced-pytorch/pyproject.toml
index 89fd5a32a89e..b846a6054cc8 100644
--- a/examples/advanced-pytorch/pyproject.toml
+++ b/examples/advanced-pytorch/pyproject.toml
@@ -7,8 +7,8 @@ name = "advanced-pytorch"
 version = "0.1.0"
 description = "Advanced Flower/PyTorch Example"
 authors = [
-    "The Flower Authors <hello@flower.dev>",
-    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>"
+    "The Flower Authors <hello@flower.ai>",
+    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>",
 ]
 
 [tool.poetry.dependencies]
diff --git a/examples/advanced-tensorflow/pyproject.toml b/examples/advanced-tensorflow/pyproject.toml
index 2f16d8a15584..02bd923129a4 100644
--- a/examples/advanced-tensorflow/pyproject.toml
+++ b/examples/advanced-tensorflow/pyproject.toml
@@ -6,11 +6,11 @@ build-backend = "poetry.core.masonry.api"
 name = "advanced-tensorflow"
 version = "0.1.0"
 description = "Advanced Flower/TensorFlow Example"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"
 flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
-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\""}
+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\"" }
diff --git a/examples/android/pyproject.toml b/examples/android/pyproject.toml
index 2b9cd8c978a7..0371f7208292 100644
--- a/examples/android/pyproject.toml
+++ b/examples/android/pyproject.toml
@@ -6,10 +6,10 @@ build-backend = "poetry.masonry.api"
 name = "android_flwr_tensorflow"
 version = "0.1.0"
 description = "Android Example"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"
-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\""}
+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\"" }
diff --git a/examples/app-pytorch/pyproject.toml b/examples/app-pytorch/pyproject.toml
index 3fd8d3170f5e..bc22d70e9075 100644
--- a/examples/app-pytorch/pyproject.toml
+++ b/examples/app-pytorch/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "app-pytorch"
 version = "0.1.0"
 description = "Multi-Tenant Federated Learning with Flower and PyTorch"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/custom-metrics/pyproject.toml b/examples/custom-metrics/pyproject.toml
index 8a2da6562018..51c29e213d81 100644
--- a/examples/custom-metrics/pyproject.toml
+++ b/examples/custom-metrics/pyproject.toml
@@ -7,8 +7,8 @@ name = "custom-metrics"
 version = "0.1.0"
 description = "Federated Learning with Flower and Custom Metrics"
 authors = [
-	"The Flower Authors <hello@flower.dev>",
-	"Gustavo Bertoli <gubertoli -at- gmail.com>"
+	"The Flower Authors <hello@flower.ai>",
+	"Gustavo Bertoli <gubertoli -at- gmail.com>",
 ]
 
 [tool.poetry.dependencies]
@@ -16,4 +16,4 @@ python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"
 flwr-datasets = { version = "*", extras = ["vision"] }
 scikit-learn = "^1.2.2"
-tensorflow = "==2.12.0"
\ No newline at end of file
+tensorflow = "==2.12.0"
diff --git a/examples/dp-sgd-mnist/pyproject.toml b/examples/dp-sgd-mnist/pyproject.toml
index 3158ab3d52f6..161952fd2aa4 100644
--- a/examples/dp-sgd-mnist/pyproject.toml
+++ b/examples/dp-sgd-mnist/pyproject.toml
@@ -7,14 +7,14 @@ name = "dp-sgd-mnist"
 version = "0.1.0"
 description = "Federated training with Tensorflow Privacy"
 authors = [
-    "The Flower Authors <hello@flower.dev>",
-    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>"
+    "The Flower Authors <hello@flower.ai>",
+    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>",
 ]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 # flwr = { path = "../../", develop = true }  # Development
 flwr = ">=1.0,<2.0"
-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\""}
+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\"" }
 tensorflow-privacy = "0.8.10"
diff --git a/examples/flower-in-30-minutes/tutorial.ipynb b/examples/flower-in-30-minutes/tutorial.ipynb
index 5a1209f1cbb5..f31f85be71e7 100644
--- a/examples/flower-in-30-minutes/tutorial.ipynb
+++ b/examples/flower-in-30-minutes/tutorial.ipynb
@@ -7,11 +7,11 @@
    "source": [
     "Welcome to the 30 minutes Flower federated learning tutorial!\n",
     "\n",
-    "In this tutorial you will implement your first Federated Learning project using [Flower](https://flower.dev/).\n",
+    "In this tutorial you will implement your first Federated Learning project using [Flower](https://flower.ai/).\n",
     "\n",
     "🧑‍🏫 This tutorial starts at zero and expects no familiarity with federated learning. Only a basic understanding of data science and Python programming is assumed. A minimal understanding of ML is not required but if you already know about it, nothing is stopping your from modifying this code as you see fit!\n",
     "\n",
-    "> Star Flower on [GitHub ⭐️](https://github.com/adap/flower) and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack 🌼](https://flower.dev/join-slack/). We'd love to hear from you in the #introductions channel! And if anything is unclear, head over to the #questions channel.\n",
+    "> Star Flower on [GitHub ⭐️](https://github.com/adap/flower) and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack 🌼](https://flower.ai/join-slack/). We'd love to hear from you in the #introductions channel! And if anything is unclear, head over to the #questions channel.\n",
     "\n",
     "Let's get stated!"
    ]
@@ -853,21 +853,21 @@
     "\n",
     "Well, if you enjoyed this content, consider giving us a ⭐️ on GitHub -> https://github.com/adap/flower\n",
     "\n",
-    "* **[DOCS]** How about running your Flower clients on the GPU? find out how to do it in the [Flower Simulation Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html)\n",
+    "* **[DOCS]** How about running your Flower clients on the GPU? find out how to do it in the [Flower Simulation Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html)\n",
     "\n",
     "* **[VIDEO]** You can follow our [detailed line-by-line 9-videos tutorial](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB) about everything you need to know to design your own Flower Simulation pipelines\n",
     "\n",
     "* Check more advanced simulation examples the Flower GitHub:\n",
     "\n",
     "    * Flower simulation with Tensorflow/Keras: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/adap/flower/tree/main/examples/simulation-tensorflow)\n",
-    "    \n",
+    "\n",
     "    * Flower simulation with Pytorch: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/adap/flower/tree/main/examples/simulation-pytorch)\n",
     "\n",
-    "* **[DOCS]** All Flower examples: https://flower.dev/docs/examples/\n",
+    "* **[DOCS]** All Flower examples: https://flower.ai/docs/examples/\n",
     "\n",
     "* **[VIDEO]** Our Youtube channel: https://www.youtube.com/@flowerlabs\n",
     "\n",
-    "Don't forget to join our Slack channel: https://flower.dev/join-slack/\n"
+    "Don't forget to join our Slack channel: https://flower.ai/join-slack/\n"
    ]
   },
   {
diff --git a/examples/ios/pyproject.toml b/examples/ios/pyproject.toml
index c1bdbb815bd5..2e55b14cf761 100644
--- a/examples/ios/pyproject.toml
+++ b/examples/ios/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "flwr_ios_coreml"
 version = "0.1.0"
 description = "Example Server for Flower iOS/CoreML"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/mxnet-from-centralized-to-federated/pyproject.toml b/examples/mxnet-from-centralized-to-federated/pyproject.toml
index 952683eb90f6..b00b3ddfe412 100644
--- a/examples/mxnet-from-centralized-to-federated/pyproject.toml
+++ b/examples/mxnet-from-centralized-to-federated/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "mxnet_example"
 version = "0.1.0"
 description = "MXNet example with MNIST and CNN"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/opacus/pyproject.toml b/examples/opacus/pyproject.toml
index af0eaf596fbf..26914fa27aa4 100644
--- a/examples/opacus/pyproject.toml
+++ b/examples/opacus/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "flwr_opacus"
 version = "0.1.0"
 description = "Differentially Private Federated Learning with Opacus and Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/pytorch-federated-variational-autoencoder/pyproject.toml b/examples/pytorch-federated-variational-autoencoder/pyproject.toml
index 116140306f62..bc1f85803682 100644
--- a/examples/pytorch-federated-variational-autoencoder/pyproject.toml
+++ b/examples/pytorch-federated-variational-autoencoder/pyproject.toml
@@ -2,7 +2,7 @@
 name = "pytorch_federated_variational_autoencoder"
 version = "0.1.0"
 description = "Federated Variational Autoencoder Example"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/pytorch-from-centralized-to-federated/pyproject.toml b/examples/pytorch-from-centralized-to-federated/pyproject.toml
index 6d6f138a0aea..3d1559e3a515 100644
--- a/examples/pytorch-from-centralized-to-federated/pyproject.toml
+++ b/examples/pytorch-from-centralized-to-federated/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.masonry.api"
 name = "quickstart-pytorch"
 version = "0.1.0"
 description = "PyTorch: From Centralized To Federated with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/quickstart-fastai/pyproject.toml b/examples/quickstart-fastai/pyproject.toml
index 7f458ef3431c..19a25291a6af 100644
--- a/examples/quickstart-fastai/pyproject.toml
+++ b/examples/quickstart-fastai/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-fastai"
 version = "0.1.0"
 description = "Fastai Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/quickstart-huggingface/pyproject.toml b/examples/quickstart-huggingface/pyproject.toml
index 50ba0b37f8d2..2b46804d7b45 100644
--- a/examples/quickstart-huggingface/pyproject.toml
+++ b/examples/quickstart-huggingface/pyproject.toml
@@ -7,8 +7,8 @@ name = "quickstart-huggingface"
 version = "0.1.0"
 description = "Hugging Face Transformers Federated Learning Quickstart with Flower"
 authors = [
-    "The Flower Authors <hello@flower.dev>",
-    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>"
+    "The Flower Authors <hello@flower.ai>",
+    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>",
 ]
 
 [tool.poetry.dependencies]
diff --git a/examples/quickstart-jax/pyproject.toml b/examples/quickstart-jax/pyproject.toml
index 41b4462d0a14..c956191369b5 100644
--- a/examples/quickstart-jax/pyproject.toml
+++ b/examples/quickstart-jax/pyproject.toml
@@ -2,7 +2,7 @@
 name = "jax_example"
 version = "0.1.0"
 description = "JAX example training a linear regression model with federated learning"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/quickstart-mlcube/pyproject.toml b/examples/quickstart-mlcube/pyproject.toml
index 0d42fc3b2898..a2862bd5ebb7 100644
--- a/examples/quickstart-mlcube/pyproject.toml
+++ b/examples/quickstart-mlcube/pyproject.toml
@@ -6,13 +6,13 @@ build-backend = "poetry.masonry.api"
 name = "quickstart-ml-cube"
 version = "0.1.0"
 description = "Keras Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"  # For development: { path = "../../", develop = true }
-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\""}
+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\"" }
 mlcube = "0.0.9"
 mlcube-docker = "0.0.9"
 tensorflow-estimator = ">=2.9.1,<2.11.1 || >2.11.1"
diff --git a/examples/quickstart-mlx/pyproject.toml b/examples/quickstart-mlx/pyproject.toml
index deb541c5ba9c..752040b6aaa9 100644
--- a/examples/quickstart-mlx/pyproject.toml
+++ b/examples/quickstart-mlx/pyproject.toml
@@ -6,11 +6,11 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-mlx"
 version = "0.1.0"
 description = "MLX Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"
 mlx = "==0.0.3"
 numpy = "==1.24.4"
-flwr-datasets = {extras = ["vision"], version = "^0.0.2"}
+flwr-datasets = { extras = ["vision"], version = "^0.0.2" }
diff --git a/examples/quickstart-mxnet/pyproject.toml b/examples/quickstart-mxnet/pyproject.toml
index 952683eb90f6..b00b3ddfe412 100644
--- a/examples/quickstart-mxnet/pyproject.toml
+++ b/examples/quickstart-mxnet/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "mxnet_example"
 version = "0.1.0"
 description = "MXNet example with MNIST and CNN"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/quickstart-pandas/pyproject.toml b/examples/quickstart-pandas/pyproject.toml
index 6229210d6488..2e6b1424bb54 100644
--- a/examples/quickstart-pandas/pyproject.toml
+++ b/examples/quickstart-pandas/pyproject.toml
@@ -7,7 +7,7 @@ name = "quickstart-pandas"
 version = "0.1.0"
 description = "Pandas Federated Analytics Quickstart with Flower"
 authors = ["Ragy Haddad <ragy202@gmail.com>"]
-maintainers = ["The Flower Authors <hello@flower.dev>"]
+maintainers = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/quickstart-pytorch-lightning/pyproject.toml b/examples/quickstart-pytorch-lightning/pyproject.toml
index 853ef9c1646f..a09aaa3d65b5 100644
--- a/examples/quickstart-pytorch-lightning/pyproject.toml
+++ b/examples/quickstart-pytorch-lightning/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.masonry.api"
 name = "quickstart-pytorch-lightning"
 version = "0.1.0"
 description = "Federated Learning Quickstart with Flower and PyTorch Lightning"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/examples/quickstart-pytorch/pyproject.toml b/examples/quickstart-pytorch/pyproject.toml
index ec6a3af8c5b4..d8e1503dd8a7 100644
--- a/examples/quickstart-pytorch/pyproject.toml
+++ b/examples/quickstart-pytorch/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-pytorch"
 version = "0.1.0"
 description = "PyTorch Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/quickstart-sklearn-tabular/pyproject.toml b/examples/quickstart-sklearn-tabular/pyproject.toml
index 34a78048d3b0..86eab5c38df0 100644
--- a/examples/quickstart-sklearn-tabular/pyproject.toml
+++ b/examples/quickstart-sklearn-tabular/pyproject.toml
@@ -7,8 +7,8 @@ name = "sklearn-mnist"
 version = "0.1.0"
 description = "Federated learning with scikit-learn and Flower"
 authors = [
-    "The Flower Authors <hello@flower.dev>",
-    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>"
+    "The Flower Authors <hello@flower.ai>",
+    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>",
 ]
 
 [tool.poetry.dependencies]
diff --git a/examples/quickstart-tabnet/pyproject.toml b/examples/quickstart-tabnet/pyproject.toml
index 948eaf085b86..18f1979791bd 100644
--- a/examples/quickstart-tabnet/pyproject.toml
+++ b/examples/quickstart-tabnet/pyproject.toml
@@ -6,12 +6,12 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-tabnet"
 version = "0.1.0"
 description = "Tabnet Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"
-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\""}
+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\"" }
 tensorflow_datasets = "4.8.3"
 tabnet = "0.1.6"
diff --git a/examples/quickstart-tensorflow/pyproject.toml b/examples/quickstart-tensorflow/pyproject.toml
index e027a7353181..98aeb932cab9 100644
--- a/examples/quickstart-tensorflow/pyproject.toml
+++ b/examples/quickstart-tensorflow/pyproject.toml
@@ -6,11 +6,11 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-tensorflow"
 version = "0.1.0"
 description = "Keras Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"
 flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
-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\""}
+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\"" }
diff --git a/examples/secaggplus-mt/pyproject.toml b/examples/secaggplus-mt/pyproject.toml
index 94d8defa3316..fe6fc67252b8 100644
--- a/examples/secaggplus-mt/pyproject.toml
+++ b/examples/secaggplus-mt/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "secaggplus-mt"
 version = "0.1.0"
 description = "Secure Aggregation with Driver API"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/simulation-pytorch/pyproject.toml b/examples/simulation-pytorch/pyproject.toml
index 07918c0cd17c..5978c17f2c60 100644
--- a/examples/simulation-pytorch/pyproject.toml
+++ b/examples/simulation-pytorch/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "simulation-pytorch"
 version = "0.1.0"
 description = "Federated Learning Simulation with Flower and PyTorch"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
@@ -17,4 +17,3 @@ torchvision = "0.16.1"
 
 [tool.poetry.group.dev.dependencies]
 ipykernel = "^6.27.0"
-
diff --git a/examples/simulation-pytorch/sim.ipynb b/examples/simulation-pytorch/sim.ipynb
index d1e7358566cc..e3a50f29b6bd 100644
--- a/examples/simulation-pytorch/sim.ipynb
+++ b/examples/simulation-pytorch/sim.ipynb
@@ -605,11 +605,11 @@
     "\n",
     "Get all resources you need!\n",
     "\n",
-    "* **[DOCS]** Our complete documenation: https://flower.dev/docs/\n",
-    "* **[Examples]** All Flower examples: https://flower.dev/docs/examples/\n",
+    "* **[DOCS]** Our complete documenation: https://flower.ai/docs/\n",
+    "* **[Examples]** All Flower examples: https://flower.ai/docs/examples/\n",
     "* **[VIDEO]** Our Youtube channel: https://www.youtube.com/@flowerlabs\n",
     "\n",
-    "Don't forget to join our Slack channel: https://flower.dev/join-slack/\n"
+    "Don't forget to join our Slack channel: https://flower.ai/join-slack/\n"
    ]
   }
  ],
diff --git a/examples/simulation-tensorflow/pyproject.toml b/examples/simulation-tensorflow/pyproject.toml
index f2e7bd3006c0..ad8cc2032b2d 100644
--- a/examples/simulation-tensorflow/pyproject.toml
+++ b/examples/simulation-tensorflow/pyproject.toml
@@ -6,11 +6,11 @@ build-backend = "poetry.core.masonry.api"
 name = "simulation-tensorflow"
 version = "0.1.0"
 description = "Federated Learning Simulation with Flower and Tensorflow"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = { extras = ["simulation"], version = ">=1.0,<2.0" }
 flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
-tensorflow = {version = "^2.9.1, !=2.11.1", markers="platform_machine == 'x86_64'"}
-tensorflow-macos = {version = "^2.9.1, !=2.11.1", markers="sys_platform == 'darwin' and platform_machine == 'arm64'"}
+tensorflow = { version = "^2.9.1, !=2.11.1", markers = "platform_machine == 'x86_64'" }
+tensorflow-macos = { version = "^2.9.1, !=2.11.1", markers = "sys_platform == 'darwin' and platform_machine == 'arm64'" }
diff --git a/examples/simulation-tensorflow/sim.ipynb b/examples/simulation-tensorflow/sim.ipynb
index 5ef1992bcc7e..72eba1703b8e 100644
--- a/examples/simulation-tensorflow/sim.ipynb
+++ b/examples/simulation-tensorflow/sim.ipynb
@@ -323,11 +323,11 @@
     "\n",
     "Get all resources you need!\n",
     "\n",
-    "* **[DOCS]** Our complete documenation: https://flower.dev/docs/\n",
-    "* **[Examples]** All Flower examples: https://flower.dev/docs/examples/\n",
+    "* **[DOCS]** Our complete documenation: https://flower.ai/docs/\n",
+    "* **[Examples]** All Flower examples: https://flower.ai/docs/examples/\n",
     "* **[VIDEO]** Our Youtube channel: https://www.youtube.com/@flowerlabs\n",
     "\n",
-    "Don't forget to join our Slack channel: https://flower.dev/join-slack/"
+    "Don't forget to join our Slack channel: https://flower.ai/join-slack/"
    ]
   }
  ],
diff --git a/examples/sklearn-logreg-mnist/pyproject.toml b/examples/sklearn-logreg-mnist/pyproject.toml
index 8ea49fe187a2..58cc5ca4a02e 100644
--- a/examples/sklearn-logreg-mnist/pyproject.toml
+++ b/examples/sklearn-logreg-mnist/pyproject.toml
@@ -7,8 +7,8 @@ name = "sklearn-mnist"
 version = "0.1.0"
 description = "Federated learning with scikit-learn and Flower"
 authors = [
-    "The Flower Authors <hello@flower.dev>",
-    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>"
+    "The Flower Authors <hello@flower.ai>",
+    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>",
 ]
 
 [tool.poetry.dependencies]
diff --git a/examples/vertical-fl/pyproject.toml b/examples/vertical-fl/pyproject.toml
index 14771c70062f..19dcd0e7a842 100644
--- a/examples/vertical-fl/pyproject.toml
+++ b/examples/vertical-fl/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "vertical-fl"
 version = "0.1.0"
 description = "PyTorch Vertical FL with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/whisper-federated-finetuning/pyproject.toml b/examples/whisper-federated-finetuning/pyproject.toml
index dd5578b8b3d0..27a89578c5a0 100644
--- a/examples/whisper-federated-finetuning/pyproject.toml
+++ b/examples/whisper-federated-finetuning/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "whisper-flower"
 version = "0.1.0"
 description = "On-device Federated Downstreaming for Speech Classification"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
@@ -16,4 +16,4 @@ tokenizers = "0.13.3"
 datasets = "2.14.6"
 soundfile = "0.12.1"
 librosa = "0.10.1"
-# this example was tested with pytorch 2.1.0
\ No newline at end of file
+# this example was tested with pytorch 2.1.0
diff --git a/examples/xgboost-comprehensive/pyproject.toml b/examples/xgboost-comprehensive/pyproject.toml
index e6495a98c969..b257801cb420 100644
--- a/examples/xgboost-comprehensive/pyproject.toml
+++ b/examples/xgboost-comprehensive/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "xgboost-comprehensive"
 version = "0.1.0"
 description = "Federated XGBoost with Flower (comprehensive)"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/xgboost-quickstart/pyproject.toml b/examples/xgboost-quickstart/pyproject.toml
index af0164514cf1..c16542ea7ffe 100644
--- a/examples/xgboost-quickstart/pyproject.toml
+++ b/examples/xgboost-quickstart/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "xgboost-quickstart"
 version = "0.1.0"
 description = "Federated XGBoost with Flower (quickstart)"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/pyproject.toml b/pyproject.toml
index 065d5bc1f216..3912a8410a74 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,11 +7,11 @@ name = "flwr"
 version = "1.8.0"
 description = "Flower: A Friendly Federated Learning Framework"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 keywords = [
     "flower",
     "fl",
diff --git a/src/py/flwr/common/address_test.py b/src/py/flwr/common/address_test.py
index c12dd5fd289e..420b89871d69 100644
--- a/src/py/flwr/common/address_test.py
+++ b/src/py/flwr/common/address_test.py
@@ -109,10 +109,10 @@ def test_domain_correct() -> None:
     """Test if a correct domain address is correctly parsed."""
     # Prepare
     addresses = [
-        ("flower.dev:123", ("flower.dev", 123, None)),
-        ("sub.flower.dev:123", ("sub.flower.dev", 123, None)),
-        ("sub2.sub1.flower.dev:123", ("sub2.sub1.flower.dev", 123, None)),
-        ("s5.s4.s3.s2.s1.flower.dev:123", ("s5.s4.s3.s2.s1.flower.dev", 123, None)),
+        ("flower.ai:123", ("flower.ai", 123, None)),
+        ("sub.flower.ai:123", ("sub.flower.ai", 123, None)),
+        ("sub2.sub1.flower.ai:123", ("sub2.sub1.flower.ai", 123, None)),
+        ("s5.s4.s3.s2.s1.flower.ai:123", ("s5.s4.s3.s2.s1.flower.ai", 123, None)),
         ("localhost:123", ("localhost", 123, None)),
         ("https://localhost:123", ("https://localhost", 123, None)),
         ("http://localhost:123", ("http://localhost", 123, None)),
@@ -130,8 +130,8 @@ def test_domain_incorrect() -> None:
     """Test if an incorrect domain address returns None."""
     # Prepare
     addresses = [
-        "flower.dev",
-        "flower.dev:65536",
+        "flower.ai",
+        "flower.ai:65536",
     ]
 
     for address in addresses:
diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py
index 9ee230942890..f3ffe632bbe8 100644
--- a/src/py/flwr/simulation/app.py
+++ b/src/py/flwr/simulation/app.py
@@ -220,7 +220,7 @@ def start_simulation(
     log(
         INFO,
         "Optimize your simulation with Flower VCE: "
-        "https://flower.dev/docs/framework/how-to-run-simulations.html",
+        "https://flower.ai/docs/framework/how-to-run-simulations.html",
     )
 
     # Log the resources that a single client will be able to use
@@ -338,7 +338,7 @@ def update_resources(f_stop: threading.Event) -> None:
             "disconnected. The head node might still be alive but cannot accommodate "
             "any actor with resources: %s."
             "\nTake a look at the Flower simulation examples for guidance "
-            "<https://flower.dev/docs/framework/how-to-run-simulations.html>.",
+            "<https://flower.ai/docs/framework/how-to-run-simulations.html>.",
             client_resources,
             client_resources,
         )

From 7269e480cbe98fd294e0404002497b495897d4b8 Mon Sep 17 00:00:00 2001
From: Robert Steiner <robert@flower.dev>
Date: Wed, 21 Feb 2024 14:00:01 +0100
Subject: [PATCH 042/102] update the top level domain in examples (#2990)

---
 examples/federated-kaplan-meier-fitter/README.md      | 4 ++--
 examples/federated-kaplan-meier-fitter/pyproject.toml | 4 ++--
 examples/flower-in-30-minutes/tutorial.ipynb          | 4 ++--
 examples/simulation-pytorch/sim.ipynb                 | 4 ++--
 examples/simulation-tensorflow/sim.ipynb              | 2 +-
 5 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/examples/federated-kaplan-meier-fitter/README.md b/examples/federated-kaplan-meier-fitter/README.md
index 0c97c5bacc22..29b2d2a0731d 100644
--- a/examples/federated-kaplan-meier-fitter/README.md
+++ b/examples/federated-kaplan-meier-fitter/README.md
@@ -1,6 +1,6 @@
 # Flower Example using KaplanMeierFitter
 
-This is an introductory example on **federated survival analysis** using [Flower](https://flower.dev/)
+This is an introductory example on **federated survival analysis** using [Flower](https://flower.ai/)
 and [lifelines](https://lifelines.readthedocs.io/en/stable/index.html) library.
 
 The aim of this example is to estimate the survival function using the
@@ -12,7 +12,7 @@ Finally, many other estimators beyond KaplanMeierFitter can be used with the pro
 AalenJohansenFitter, GeneralizedGammaFitter, LogLogisticFitter,
 SplineFitter, and WeibullFitter.
 
-We also use the [NatualPartitioner](https://flower.dev/docs/datasets/ref-api/flwr_datasets.partitioner.NaturalIdPartitioner.html#flwr_datasets.partitioner.NaturalIdPartitioner) from [Flower Datasets](https://flower.dev/docs/datasets/) to divide the data according to
+We also use the [NatualPartitioner](https://flower.ai/docs/datasets/ref-api/flwr_datasets.partitioner.NaturalIdPartitioner.html#flwr_datasets.partitioner.NaturalIdPartitioner) from [Flower Datasets](https://flower.ai/docs/datasets/) to divide the data according to
 the group it comes from therefore to simulate the division that might occur.
 
 <p style="text-align:center;">
diff --git a/examples/federated-kaplan-meier-fitter/pyproject.toml b/examples/federated-kaplan-meier-fitter/pyproject.toml
index 0a194f84d211..708ac3f482f8 100644
--- a/examples/federated-kaplan-meier-fitter/pyproject.toml
+++ b/examples/federated-kaplan-meier-fitter/pyproject.toml
@@ -6,8 +6,8 @@ build-backend = "poetry.core.masonry.api"
 name = "federated-kaplan-meier-fitter"
 version = "0.1.0"
 description = "Federated Kaplan Meier Fitter with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
-maintainers = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
+maintainers = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.9,<3.11"
diff --git a/examples/flower-in-30-minutes/tutorial.ipynb b/examples/flower-in-30-minutes/tutorial.ipynb
index f31f85be71e7..0e42cff924e8 100644
--- a/examples/flower-in-30-minutes/tutorial.ipynb
+++ b/examples/flower-in-30-minutes/tutorial.ipynb
@@ -59,7 +59,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will be using the _simulation_ model in Flower, which allows you to run a large number of clients without the overheads of manually managing devices. This is achieved via the `Virtual Client Engine`, the core component that runs [FL Simulations](https://flower.dev/docs/framework/how-to-run-simulations.html) with Flower. With simulation, you can dynamically scale your experiments whether you run the code on your laptop, a machine with a single GPU, a server with multiple GPUs os even on a cluster with multiple servers. The `Virtual Client Engine` handles everything transparently and it allows you to specify how many resources (e.g. CPU cores, GPU VRAM) should be assigned to each virtual client."
+    "We will be using the _simulation_ model in Flower, which allows you to run a large number of clients without the overheads of manually managing devices. This is achieved via the `Virtual Client Engine`, the core component that runs [FL Simulations](https://flower.ai/docs/framework/how-to-run-simulations.html) with Flower. With simulation, you can dynamically scale your experiments whether you run the code on your laptop, a machine with a single GPU, a server with multiple GPUs os even on a cluster with multiple servers. The `Virtual Client Engine` handles everything transparently and it allows you to specify how many resources (e.g. CPU cores, GPU VRAM) should be assigned to each virtual client."
    ]
   },
   {
@@ -69,7 +69,7 @@
    "source": [
     "## Install your ML framework\n",
     "\n",
-    "Flower is agnostic to your choice of ML Framework. Flower works with `PyTorch`, `Tensorflow`, `NumPy`, `🤗 Transformers`, `MXNet`, `JAX`, `scikit-learn`, `fastai`, `Pandas`. Flower also supports all major platforms: `iOS`, `Android` and plain `C++`. You can find a _quickstart- example for each of the above in the [Flower Repository](https://github.com/adap/flower/tree/main/examples) inside the `examples/` directory. And check the [Flower Documentation](https://flower.dev/docs/) for even more learning materials.\n",
+    "Flower is agnostic to your choice of ML Framework. Flower works with `PyTorch`, `Tensorflow`, `NumPy`, `🤗 Transformers`, `MXNet`, `JAX`, `scikit-learn`, `fastai`, `Pandas`. Flower also supports all major platforms: `iOS`, `Android` and plain `C++`. You can find a _quickstart- example for each of the above in the [Flower Repository](https://github.com/adap/flower/tree/main/examples) inside the `examples/` directory. And check the [Flower Documentation](https://flower.ai/docs/) for even more learning materials.\n",
     "\n",
     "In this tutorial we are going to use PyTorch, so let's install a recent version. In this tutorial we'll use a small model so using CPU only training will suffice (this will also prevent Colab from abruptly terminating your experiment if resource limits are exceeded)"
    ]
diff --git a/examples/simulation-pytorch/sim.ipynb b/examples/simulation-pytorch/sim.ipynb
index e3a50f29b6bd..93a79d2f0e0a 100644
--- a/examples/simulation-pytorch/sim.ipynb
+++ b/examples/simulation-pytorch/sim.ipynb
@@ -30,7 +30,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will be using the _simulation_ mode in Flower, which allows you to run a large number of clients without the overheads of manually managing devices. This is achieved via the [Virtual Client Engine](https://flower.dev/docs/framework/how-to-run-simulations.html) in Flower. With simulation, you can dynamically scale your experiments whether you run the code on your laptop, a machine with a single GPU, a server with multiple GPUs os even on a cluster with multiple servers. The `Virtual Client Engine` handles everything transparently and it allows you to specify how many resources (e.g. CPU cores, GPU VRAM) should be assigned to each virtual client."
+    "We will be using the _simulation_ mode in Flower, which allows you to run a large number of clients without the overheads of manually managing devices. This is achieved via the [Virtual Client Engine](https://flower.ai/docs/framework/how-to-run-simulations.html) in Flower. With simulation, you can dynamically scale your experiments whether you run the code on your laptop, a machine with a single GPU, a server with multiple GPUs os even on a cluster with multiple servers. The `Virtual Client Engine` handles everything transparently and it allows you to specify how many resources (e.g. CPU cores, GPU VRAM) should be assigned to each virtual client."
    ]
   },
   {
@@ -178,7 +178,7 @@
     "\n",
     "To start designing a Federated Learning pipeline we need to meet one of the key properties in FL: each client has its own data partition. To accomplish this with the MNIST dataset, we are going to generate N random partitions, where N is the total number of clients in our FL system.\n",
     "\n",
-    "We can use [Flower Datasets](https://flower.dev/docs/datasets/) to effortlessly obtain an off-the-shelf partitioned dataset or partition one that isn't pre-partitioned. Let's choose MNIST."
+    "We can use [Flower Datasets](https://flower.ai/docs/datasets/) to effortlessly obtain an off-the-shelf partitioned dataset or partition one that isn't pre-partitioned. Let's choose MNIST."
    ]
   },
   {
diff --git a/examples/simulation-tensorflow/sim.ipynb b/examples/simulation-tensorflow/sim.ipynb
index 72eba1703b8e..9acfba99237c 100644
--- a/examples/simulation-tensorflow/sim.ipynb
+++ b/examples/simulation-tensorflow/sim.ipynb
@@ -232,7 +232,7 @@
     "\n",
     "Flower comes with a number of built-in strategies, but we can also use our own strategy implementations to customize nearly all aspects of the federated learning approach. For this example, we use the built-in `FedAvg` implementation and customize it using a few basic parameters. The last step is the actual call to `start_simulation` which - you guessed it - actually starts the simulation.\n",
     "\n",
-    "We can use [Flower Datasets](https://flower.dev/docs/datasets/) to effortlessly obtain an off-the-shelf partitioned dataset or partition one that isn't pre-partitioned. Let's choose MNIST."
+    "We can use [Flower Datasets](https://flower.ai/docs/datasets/) to effortlessly obtain an off-the-shelf partitioned dataset or partition one that isn't pre-partitioned. Let's choose MNIST."
    ]
   },
   {

From b3602cc3e4c527aab6201d366567a7667cc3756b Mon Sep 17 00:00:00 2001
From: Heng Pan <134433891+panh99@users.noreply.github.com>
Date: Wed, 21 Feb 2024 16:30:01 +0000
Subject: [PATCH 043/102] Add conversion functions between `Array` and
 `NDArray` (#2991)

---
 src/py/flwr/common/__init__.py                |  2 +
 src/py/flwr/common/constant.py                | 12 ++++
 src/py/flwr/common/record/__init__.py         |  2 +
 src/py/flwr/common/record/conversion_utils.py | 40 ++++++++++++
 .../common/record/conversion_utils_test.py    | 44 +++++++++++++
 src/py/flwr/common/record/parametersrecord.py | 21 ++++++-
 .../common/record/parametersrecord_test.py    | 62 +++++++++++++++++++
 7 files changed, 181 insertions(+), 2 deletions(-)
 create mode 100644 src/py/flwr/common/record/conversion_utils.py
 create mode 100644 src/py/flwr/common/record/conversion_utils_test.py
 create mode 100644 src/py/flwr/common/record/parametersrecord_test.py

diff --git a/src/py/flwr/common/__init__.py b/src/py/flwr/common/__init__.py
index 13d168b68232..319c15900217 100644
--- a/src/py/flwr/common/__init__.py
+++ b/src/py/flwr/common/__init__.py
@@ -31,6 +31,7 @@
 from .record import MetricsRecord as MetricsRecord
 from .record import ParametersRecord as ParametersRecord
 from .record import RecordSet as RecordSet
+from .record import array_from_numpy as array_from_numpy
 from .telemetry import EventType as EventType
 from .telemetry import event as event
 from .typing import ClientMessage as ClientMessage
@@ -58,6 +59,7 @@
 
 __all__ = [
     "Array",
+    "array_from_numpy",
     "bytes_to_ndarray",
     "ClientMessage",
     "Code",
diff --git a/src/py/flwr/common/constant.py b/src/py/flwr/common/constant.py
index fcafd853a349..811fff73f06d 100644
--- a/src/py/flwr/common/constant.py
+++ b/src/py/flwr/common/constant.py
@@ -15,6 +15,8 @@
 """Flower constants."""
 
 
+from __future__ import annotations
+
 MISSING_EXTRA_REST = """
 Extra dependencies required for using the REST-based Fleet API are missing.
 
@@ -36,3 +38,13 @@
 MESSAGE_TYPE_GET_PARAMETERS = "get_parameters"
 MESSAGE_TYPE_FIT = "fit"
 MESSAGE_TYPE_EVALUATE = "evaluate"
+
+
+class SType:
+    """Serialisation type."""
+
+    NUMPY = "numpy.ndarray"
+
+    def __new__(cls) -> SType:
+        """Prevent instantiation."""
+        raise TypeError(f"{cls.__name__} cannot be instantiated.")
diff --git a/src/py/flwr/common/record/__init__.py b/src/py/flwr/common/record/__init__.py
index 4aa79a4b041c..60bc54b8552a 100644
--- a/src/py/flwr/common/record/__init__.py
+++ b/src/py/flwr/common/record/__init__.py
@@ -15,12 +15,14 @@
 """Record APIs."""
 
 from .configsrecord import ConfigsRecord
+from .conversion_utils import array_from_numpy
 from .metricsrecord import MetricsRecord
 from .parametersrecord import Array, ParametersRecord
 from .recordset import RecordSet
 
 __all__ = [
     "Array",
+    "array_from_numpy",
     "ConfigsRecord",
     "MetricsRecord",
     "ParametersRecord",
diff --git a/src/py/flwr/common/record/conversion_utils.py b/src/py/flwr/common/record/conversion_utils.py
new file mode 100644
index 000000000000..7cc0b04283e9
--- /dev/null
+++ b/src/py/flwr/common/record/conversion_utils.py
@@ -0,0 +1,40 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Conversion utility functions for Records."""
+
+
+from io import BytesIO
+
+import numpy as np
+
+from ..constant import SType
+from ..typing import NDArray
+from .parametersrecord import Array
+
+
+def array_from_numpy(ndarray: NDArray) -> Array:
+    """Create Array from NumPy ndarray."""
+    buffer = BytesIO()
+    # WARNING: NEVER set allow_pickle to true.
+    # Reason: loading pickled data can execute arbitrary code
+    # Source: https://numpy.org/doc/stable/reference/generated/numpy.save.html
+    np.save(buffer, ndarray, allow_pickle=False)
+    data = buffer.getvalue()
+    return Array(
+        dtype=str(ndarray.dtype),
+        shape=list(ndarray.shape),
+        stype=SType.NUMPY,
+        data=data,
+    )
diff --git a/src/py/flwr/common/record/conversion_utils_test.py b/src/py/flwr/common/record/conversion_utils_test.py
new file mode 100644
index 000000000000..84be37fda4a3
--- /dev/null
+++ b/src/py/flwr/common/record/conversion_utils_test.py
@@ -0,0 +1,44 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Unit tests for conversion utility functions."""
+
+
+import unittest
+from io import BytesIO
+
+import numpy as np
+
+from ..constant import SType
+from .conversion_utils import array_from_numpy
+
+
+class TestArrayFromNumpy(unittest.TestCase):
+    """Unit tests for array_from_numpy."""
+
+    def test_array_from_numpy(self) -> None:
+        """Test the array_from_numpy function."""
+        # Prepare
+        original_array = np.array([1, 2, 3], dtype=np.float32)
+
+        # Execute
+        array_instance = array_from_numpy(original_array)
+        buffer = BytesIO(array_instance.data)
+        deserialized_array = np.load(buffer, allow_pickle=False)
+
+        # Assert
+        self.assertEqual(array_instance.dtype, str(original_array.dtype))
+        self.assertEqual(array_instance.shape, list(original_array.shape))
+        self.assertEqual(array_instance.stype, SType.NUMPY)
+        np.testing.assert_array_equal(deserialized_array, original_array)
diff --git a/src/py/flwr/common/record/parametersrecord.py b/src/py/flwr/common/record/parametersrecord.py
index aaa76c88ae09..17bf3f608db7 100644
--- a/src/py/flwr/common/record/parametersrecord.py
+++ b/src/py/flwr/common/record/parametersrecord.py
@@ -14,10 +14,14 @@
 # ==============================================================================
 """ParametersRecord and Array."""
 
-
 from dataclasses import dataclass
-from typing import List, Optional, OrderedDict
+from io import BytesIO
+from typing import List, Optional, OrderedDict, cast
+
+import numpy as np
 
+from ..constant import SType
+from ..typing import NDArray
 from .typeddict import TypedDict
 
 
@@ -51,6 +55,19 @@ class Array:
     stype: str
     data: bytes
 
+    def numpy(self) -> NDArray:
+        """Return the array as a NumPy array."""
+        if self.stype != SType.NUMPY:
+            raise TypeError(
+                f"Unsupported serialization type for numpy conversion: '{self.stype}'"
+            )
+        bytes_io = BytesIO(self.data)
+        # WARNING: NEVER set allow_pickle to true.
+        # Reason: loading pickled data can execute arbitrary code
+        # Source: https://numpy.org/doc/stable/reference/generated/numpy.load.html
+        ndarray_deserialized = np.load(bytes_io, allow_pickle=False)
+        return cast(NDArray, ndarray_deserialized)
+
 
 def _check_key(key: str) -> None:
     """Check if key is of expected type."""
diff --git a/src/py/flwr/common/record/parametersrecord_test.py b/src/py/flwr/common/record/parametersrecord_test.py
new file mode 100644
index 000000000000..9633af7bda6d
--- /dev/null
+++ b/src/py/flwr/common/record/parametersrecord_test.py
@@ -0,0 +1,62 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Unit tests for ParametersRecord and Array."""
+
+
+import unittest
+from io import BytesIO
+
+import numpy as np
+
+from ..constant import SType
+from .parametersrecord import Array
+
+
+class TestArray(unittest.TestCase):
+    """Unit tests for Array."""
+
+    def test_numpy_conversion_valid(self) -> None:
+        """Test the numpy method with valid Array instance."""
+        # Prepare
+        original_array = np.array([1, 2, 3], dtype=np.float32)
+        buffer = BytesIO()
+        np.save(buffer, original_array, allow_pickle=False)
+        buffer.seek(0)
+
+        # Execute
+        array_instance = Array(
+            dtype=str(original_array.dtype),
+            shape=list(original_array.shape),
+            stype=SType.NUMPY,
+            data=buffer.read(),
+        )
+        converted_array = array_instance.numpy()
+
+        # Assert
+        np.testing.assert_array_equal(converted_array, original_array)
+
+    def test_numpy_conversion_invalid(self) -> None:
+        """Test the numpy method with invalid Array instance."""
+        # Prepare
+        array_instance = Array(
+            dtype="float32",
+            shape=[3],
+            stype="invalid_stype",  # Non-numpy stype
+            data=b"",
+        )
+
+        # Execute and assert
+        with self.assertRaises(TypeError):
+            array_instance.numpy()

From a1a6b9ec48506b103d0ddbdcb323bbb3f7c2356a Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.ai>
Date: Wed, 21 Feb 2024 21:35:55 +0100
Subject: [PATCH 044/102] Migrate driver example to use ServerApp (#2980)

Co-authored-by: Charles Beauville <charles@flower.ai>
---
 examples/app-pytorch/README.md        |   6 +
 examples/app-pytorch/driver.py        | 226 --------------------------
 examples/app-pytorch/server_custom.py | 154 ++++++++++++++++++
 3 files changed, 160 insertions(+), 226 deletions(-)
 delete mode 100644 examples/app-pytorch/driver.py
 create mode 100644 examples/app-pytorch/server_custom.py

diff --git a/examples/app-pytorch/README.md b/examples/app-pytorch/README.md
index 73d19ff11258..bcc7aa045e26 100644
--- a/examples/app-pytorch/README.md
+++ b/examples/app-pytorch/README.md
@@ -51,3 +51,9 @@ With both the long-running server (SuperLink) and two clients (SuperNode) up and
 ```bash
 flower-server-app server:app --insecure
 ```
+
+Or, to try the custom server function example, run:
+
+```bash
+flower-server-app server_custom:app --insecure
+```
diff --git a/examples/app-pytorch/driver.py b/examples/app-pytorch/driver.py
deleted file mode 100644
index 06091c954cef..000000000000
--- a/examples/app-pytorch/driver.py
+++ /dev/null
@@ -1,226 +0,0 @@
-from typing import List, Tuple
-import random
-import time
-
-from flwr.server.driver import GrpcDriver
-from flwr.common import (
-    ServerMessage,
-    FitIns,
-    ndarrays_to_parameters,
-    serde,
-    parameters_to_ndarrays,
-    ClientMessage,
-    NDArrays,
-    Code,
-)
-from flwr.proto import driver_pb2, task_pb2, node_pb2, transport_pb2
-from flwr.server.strategy.aggregate import aggregate
-from flwr.common import Metrics
-from flwr.server import History
-from flwr.common import serde
-from task import Net, get_parameters, set_parameters
-
-
-# Define metric aggregation function
-def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
-    examples = [num_examples for num_examples, _ in metrics]
-
-    # Multiply accuracy of each client by number of examples used
-    train_losses = [num_examples * m["train_loss"] for num_examples, m in metrics]
-    train_accuracies = [
-        num_examples * m["train_accuracy"] for num_examples, m in metrics
-    ]
-    val_losses = [num_examples * m["val_loss"] for num_examples, m in metrics]
-    val_accuracies = [num_examples * m["val_accuracy"] for num_examples, m in metrics]
-
-    # Aggregate and return custom metric (weighted average)
-    return {
-        "train_loss": sum(train_losses) / sum(examples),
-        "train_accuracy": sum(train_accuracies) / sum(examples),
-        "val_loss": sum(val_losses) / sum(examples),
-        "val_accuracy": sum(val_accuracies) / sum(examples),
-    }
-
-
-# -------------------------------------------------------------------------- Driver SDK
-driver = GrpcDriver(driver_service_address="0.0.0.0:9091", root_certificates=None)
-# -------------------------------------------------------------------------- Driver SDK
-
-anonymous_client_nodes = False
-num_client_nodes_per_round = 2
-sleep_time = 1
-num_rounds = 3
-parameters = ndarrays_to_parameters(get_parameters(net=Net()))
-
-# -------------------------------------------------------------------------- Driver SDK
-driver.connect()
-create_run_res: driver_pb2.CreateRunResponse = driver.create_run(
-    req=driver_pb2.CreateRunRequest()
-)
-# -------------------------------------------------------------------------- Driver SDK
-
-run_id = create_run_res.run_id
-print(f"Created run id {run_id}")
-
-history = History()
-for server_round in range(num_rounds):
-    print(f"Commencing server round {server_round + 1}")
-
-    # List of sampled node IDs in this round
-    sampled_nodes: List[node_pb2.Node] = []
-
-    # Sample node ids
-    if anonymous_client_nodes:
-        # If we're working with anonymous clients, we don't know their identities, and
-        # we don't know how many of them we have. We, therefore, have to assume that
-        # enough anonymous client nodes are available or become available over time.
-        #
-        # To schedule a TaskIns for an anonymous client node, we set the node_id to 0
-        # (and `anonymous` to True)
-        # Here, we create an array with only zeros in it:
-        sampled_node_ids: List[int] = [0] * num_client_nodes_per_round
-        sampled_nodes = [
-            node_pb2.Node(node_id=node_id, anonymous=False)
-            for node_id in sampled_node_ids
-        ]
-    else:
-        # If our client nodes have identiy (i.e., they are not anonymous), we can get
-        # those IDs from the Driver API using `get_nodes`. If enough clients are
-        # available via the Driver API, we can select a subset by taking a random
-        # sample.
-        #
-        # The Driver API might not immediately return enough client node IDs, so we
-        # loop and wait until enough client nodes are available.
-        while True:
-            # Get a list of node ID's from the server
-            get_nodes_req = driver_pb2.GetNodesRequest(run_id=run_id)
-
-            # ---------------------------------------------------------------------- Driver SDK
-            get_nodes_res: driver_pb2.GetNodesResponse = driver.get_nodes(
-                req=get_nodes_req
-            )
-            # ---------------------------------------------------------------------- Driver SDK
-
-            all_nodes: List[node_pb2.Node] = get_nodes_res.nodes
-            print(f"Got {len(all_nodes)} client nodes")
-
-            if len(all_nodes) >= num_client_nodes_per_round:
-                # Sample client nodes
-                sampled_nodes = random.sample(all_nodes, num_client_nodes_per_round)
-                break
-
-            time.sleep(3)
-
-    # Log sampled node IDs
-    print(f"Sampled {len(sampled_nodes)} node IDs: {sampled_nodes}")
-    time.sleep(sleep_time)
-
-    # Schedule a task for all sampled nodes
-    fit_ins: FitIns = FitIns(parameters=parameters, config={})
-    server_message_proto: transport_pb2.ServerMessage = serde.server_message_to_proto(
-        server_message=ServerMessage(fit_ins=fit_ins)
-    )
-    task_ins_list: List[task_pb2.TaskIns] = []
-    for sampled_node in sampled_nodes:
-        new_task_ins = task_pb2.TaskIns(
-            task_id="",  # Do not set, will be created and set by the DriverAPI
-            group_id="",
-            run_id=run_id,
-            task=task_pb2.Task(
-                producer=node_pb2.Node(
-                    node_id=0,
-                    anonymous=True,
-                ),
-                consumer=sampled_node,
-                legacy_server_message=server_message_proto,
-            ),
-        )
-        task_ins_list.append(new_task_ins)
-
-    push_task_ins_req = driver_pb2.PushTaskInsRequest(task_ins_list=task_ins_list)
-
-    # ---------------------------------------------------------------------- Driver SDK
-    push_task_ins_res: driver_pb2.PushTaskInsResponse = driver.push_task_ins(
-        req=push_task_ins_req
-    )
-    # ---------------------------------------------------------------------- Driver SDK
-
-    print(
-        f"Scheduled {len(push_task_ins_res.task_ids)} tasks: {push_task_ins_res.task_ids}"
-    )
-
-    time.sleep(sleep_time)
-
-    # Wait for results, ignore empty task_ids
-    task_ids: List[str] = [
-        task_id for task_id in push_task_ins_res.task_ids if task_id != ""
-    ]
-    all_task_res: List[task_pb2.TaskRes] = []
-    while True:
-        pull_task_res_req = driver_pb2.PullTaskResRequest(
-            node=node_pb2.Node(node_id=0, anonymous=True),
-            task_ids=task_ids,
-        )
-
-        # ------------------------------------------------------------------ Driver SDK
-        pull_task_res_res: driver_pb2.PullTaskResResponse = driver.pull_task_res(
-            req=pull_task_res_req
-        )
-        # ------------------------------------------------------------------ Driver SDK
-
-        task_res_list: List[task_pb2.TaskRes] = pull_task_res_res.task_res_list
-        print(f"Got {len(task_res_list)} results")
-
-        time.sleep(sleep_time)
-
-        all_task_res += task_res_list
-        if len(all_task_res) == len(task_ids):
-            break
-
-    # Collect correct results
-    node_messages: List[ClientMessage] = []
-    for task_res in all_task_res:
-        if task_res.task.HasField("legacy_client_message"):
-            node_messages.append(task_res.task.legacy_client_message)
-    print(f"Received {len(node_messages)} results")
-
-    weights_results: List[Tuple[NDArrays, int]] = []
-    metrics_results: List = []
-    for node_message in node_messages:
-        if not node_message.fit_res:
-            continue
-        fit_res = node_message.fit_res
-        # Aggregate only if the status is OK
-        if fit_res.status.code != Code.OK.value:
-            continue
-        weights_results.append(
-            (parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples)
-        )
-        metrics_results.append(
-            (fit_res.num_examples, serde.metrics_from_proto(fit_res.metrics))
-        )
-
-    # Aggregate parameters (FedAvg)
-    parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results))
-    parameters = parameters_aggregated
-
-    # Aggregate metrics
-    metrics_aggregated = weighted_average(metrics_results)
-    history.add_metrics_distributed_fit(
-        server_round=server_round, metrics=metrics_aggregated
-    )
-    print("Round ", server_round, " metrics: ", metrics_aggregated)
-
-    # Slow down the start of the next round
-    time.sleep(sleep_time)
-
-print("app_fit: losses_distributed %s", str(history.losses_distributed))
-print("app_fit: metrics_distributed_fit %s", str(history.metrics_distributed_fit))
-print("app_fit: metrics_distributed %s", str(history.metrics_distributed))
-print("app_fit: losses_centralized %s", str(history.losses_centralized))
-print("app_fit: metrics_centralized %s", str(history.metrics_centralized))
-
-# -------------------------------------------------------------------------- Driver SDK
-driver.disconnect()
-# -------------------------------------------------------------------------- Driver SDK
-print("Driver disconnected")
diff --git a/examples/app-pytorch/server_custom.py b/examples/app-pytorch/server_custom.py
new file mode 100644
index 000000000000..1f0cb0d26d93
--- /dev/null
+++ b/examples/app-pytorch/server_custom.py
@@ -0,0 +1,154 @@
+from typing import List, Tuple, Dict
+import random
+import time
+
+import flwr as fl
+from flwr.server import Driver
+from flwr.common import Context
+
+from flwr.common import (
+    ServerMessage,
+    FitIns,
+    ndarrays_to_parameters,
+    serde,
+    parameters_to_ndarrays,
+    ClientMessage,
+    NDArrays,
+    Code,
+)
+from flwr.proto import driver_pb2, task_pb2, node_pb2, transport_pb2
+from flwr.server.strategy.aggregate import aggregate
+from flwr.common import Metrics
+from flwr.server import History
+from flwr.common import serde
+from task import Net, get_parameters, set_parameters
+from flwr.common.recordset_compat import fitins_to_recordset, recordset_to_fitres
+from flwr.common import Message
+
+
+# Define metric aggregation function
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+    examples = [num_examples for num_examples, _ in metrics]
+
+    # Multiply accuracy of each client by number of examples used
+    train_losses = [num_examples * m["train_loss"] for num_examples, m in metrics]
+    train_accuracies = [
+        num_examples * m["train_accuracy"] for num_examples, m in metrics
+    ]
+    val_losses = [num_examples * m["val_loss"] for num_examples, m in metrics]
+    val_accuracies = [num_examples * m["val_accuracy"] for num_examples, m in metrics]
+
+    # Aggregate and return custom metric (weighted average)
+    return {
+        "train_loss": sum(train_losses) / sum(examples),
+        "train_accuracy": sum(train_accuracies) / sum(examples),
+        "val_loss": sum(val_losses) / sum(examples),
+        "val_accuracy": sum(val_accuracies) / sum(examples),
+    }
+
+
+# Run via `flower-server-app server:app`
+app = fl.server.ServerApp()
+
+
+@app.main()
+def main(driver: Driver, context: Context) -> None:
+    """."""
+    print("RUNNING!!!!!")
+
+    anonymous_client_nodes = False
+    num_client_nodes_per_round = 2
+    sleep_time = 1
+    num_rounds = 3
+    parameters = ndarrays_to_parameters(get_parameters(net=Net()))
+
+    history = History()
+    for server_round in range(num_rounds):
+        print(f"Commencing server round {server_round + 1}")
+
+        # List of sampled node IDs in this round
+        sampled_nodes: List[int] = []
+
+        # The Driver API might not immediately return enough client node IDs, so we
+        # loop and wait until enough client nodes are available.
+        while True:
+            all_node_ids = driver.get_node_ids()
+
+            print(f"Got {len(all_node_ids)} client nodes: {all_node_ids}")
+            if len(all_node_ids) >= num_client_nodes_per_round:
+                # Sample client nodes
+                sampled_nodes = random.sample(all_node_ids, num_client_nodes_per_round)
+                break
+            time.sleep(3)
+
+        # Log sampled node IDs
+        print(f"Sampled {len(sampled_nodes)} node IDs: {sampled_nodes}")
+
+        # Schedule a task for all sampled nodes
+        fit_ins: FitIns = FitIns(parameters=parameters, config={})
+        recordset = fitins_to_recordset(fitins=fit_ins, keep_input=True)
+
+        messages = []
+        for node_id in sampled_nodes:
+            message = driver.create_message(
+                content=recordset,
+                message_type="fit",
+                dst_node_id=node_id,
+                group_id=str(server_round),
+                ttl="",
+            )
+            messages.append(message)
+
+        message_ids = driver.push_messages(messages)
+        print(f"Pushed {len(message_ids)} messages: {message_ids}")
+
+        # Wait for results, ignore empty message_ids
+        message_ids = [message_id for message_id in message_ids if message_id != ""]
+
+        all_replies: List[Message] = []
+        while True:
+            replies = driver.pull_messages(message_ids=message_ids)
+            print(f"Got {len(replies)} results")
+            all_replies += replies
+            if len(all_replies) == len(message_ids):
+                break
+            time.sleep(3)
+
+        # Collect correct results
+        all_fitres = [
+            recordset_to_fitres(msg.content, keep_input=True) for msg in all_replies
+        ]
+        print(f"Received {len(all_fitres)} results")
+
+        weights_results: List[Tuple[NDArrays, int]] = []
+        metrics_results: List[Tuple[int, Dict]] = []
+        for fitres in all_fitres:
+            print(f"num_examples: {fitres.num_examples}, status: {fitres.status.code}")
+
+            # Aggregate only if the status is OK
+            if fitres.status.code != Code.OK:
+                continue
+            weights_results.append(
+                (parameters_to_ndarrays(fitres.parameters), fitres.num_examples)
+            )
+            metrics_results.append((fitres.num_examples, fitres.metrics))
+
+        # Aggregate parameters (FedAvg)
+        parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results))
+        parameters = parameters_aggregated
+
+        # Aggregate metrics
+        metrics_aggregated = weighted_average(metrics_results)
+        history.add_metrics_distributed_fit(
+            server_round=server_round, metrics=metrics_aggregated
+        )
+        print("Round ", server_round, " metrics: ", metrics_aggregated)
+
+        # Slow down the start of the next round
+        time.sleep(sleep_time)
+
+    print("app_fit: losses_distributed %s", str(history.losses_distributed))
+    print("app_fit: metrics_distributed_fit %s", str(history.metrics_distributed_fit))
+    print("app_fit: metrics_distributed %s", str(history.metrics_distributed))
+    print("app_fit: losses_centralized %s", str(history.losses_centralized))
+    print("app_fit: metrics_centralized %s", str(history.metrics_centralized))

From 26da94bbe48ef1ce3c880d47a783b48606381b5a Mon Sep 17 00:00:00 2001
From: "Daniel J. Beutel" <daniel@flower.ai>
Date: Thu, 22 Feb 2024 17:15:41 +0100
Subject: [PATCH 045/102] Update black and isort (#2995)

* Update black and isort

* ran format.sh in baslines/flwr_baselines

* niid_bench format

* formatting new baselines

* format fedbn

---------

Co-authored-by: jafermarq <javier@flower.ai>
---
 baselines/baseline_template/pyproject.toml    |  4 +-
 baselines/dasha/pyproject.toml                |  4 +-
 baselines/depthfl/depthfl/models.py           |  1 -
 baselines/depthfl/pyproject.toml              |  4 +-
 baselines/fedavgm/pyproject.toml              |  4 +-
 baselines/fedbn/fedbn/dataset.py              |  1 +
 baselines/fedbn/fedbn/strategy.py             |  1 +
 baselines/fedbn/pyproject.toml                |  4 +-
 .../fedmeta/fedmeta/dataset_preparation.py    |  1 +
 baselines/fedmeta/fedmeta/main.py             |  1 -
 baselines/fedmeta/fedmeta/strategy.py         |  1 +
 baselines/fedmeta/pyproject.toml              |  4 +-
 baselines/fedmlb/fedmlb/dataset.py            |  1 +
 baselines/fedmlb/pyproject.toml               |  4 +-
 .../fednova/fednova/dataset_preparation.py    |  1 -
 baselines/fednova/fednova/utils.py            |  1 -
 baselines/fednova/pyproject.toml              |  4 +-
 baselines/fedpara/fedpara/models.py           |  4 +-
 baselines/fedpara/pyproject.toml              |  4 +-
 baselines/fedper/fedper/client.py             |  7 ++--
 baselines/fedper/fedper/constants.py          |  1 +
 baselines/fedper/fedper/dataset.py            |  1 +
 .../fedper/fedper/dataset_preparation.py      |  1 +
 .../fedper/implemented_models/mobile_model.py |  1 +
 .../fedper/implemented_models/resnet_model.py |  1 +
 baselines/fedper/fedper/models.py             |  1 +
 baselines/fedper/fedper/server.py             |  1 +
 baselines/fedper/fedper/strategy.py           |  1 +
 baselines/fedper/fedper/utils.py              |  7 ++--
 baselines/fedper/pyproject.toml               |  4 +-
 baselines/fedprox/fedprox/client.py           |  1 -
 baselines/fedprox/fedprox/dataset.py          |  1 -
 .../fedprox/fedprox/dataset_preparation.py    |  1 +
 baselines/fedprox/fedprox/models.py           |  1 -
 baselines/fedprox/fedprox/server.py           |  1 -
 baselines/fedprox/fedprox/strategy.py         |  1 -
 baselines/fedprox/pyproject.toml              |  4 +-
 baselines/fedstar/fedstar/client.py           |  1 +
 baselines/fedstar/fedstar/clients.py          |  1 +
 baselines/fedstar/fedstar/dataset.py          |  1 +
 .../fedstar/fedstar/dataset_preparation.py    |  1 +
 baselines/fedstar/fedstar/models.py           |  1 +
 baselines/fedstar/fedstar/server.py           |  1 +
 baselines/fedstar/fedstar/utils.py            | 35 ++++++++++------
 baselines/fedstar/pyproject.toml              |  4 +-
 baselines/fedvssl/fedvssl/client.py           |  1 +
 .../fedvssl/dataset_convert_to_json.py        |  1 +
 baselines/fedvssl/fedvssl/main.py             |  1 +
 baselines/fedvssl/fedvssl/strategy.py         |  1 +
 baselines/fedvssl/pyproject.toml              |  4 +-
 baselines/fedwav2vec2/fedwav2vec2/client.py   |  1 -
 baselines/fedwav2vec2/fedwav2vec2/dataset.py  |  1 -
 .../fedwav2vec2/dataset_preparation.py        |  1 -
 baselines/fedwav2vec2/fedwav2vec2/models.py   |  1 -
 .../fedwav2vec2/fedwav2vec2/sb_recipe.py      |  1 -
 baselines/fedwav2vec2/fedwav2vec2/strategy.py |  1 -
 baselines/fedwav2vec2/pyproject.toml          |  4 +-
 baselines/fjord/fjord/client.py               |  1 +
 baselines/fjord/fjord/dataset.py              |  1 +
 baselines/fjord/fjord/main.py                 |  1 +
 baselines/fjord/fjord/models.py               |  1 +
 baselines/fjord/fjord/od/layers/__init__.py   |  1 +
 baselines/fjord/fjord/od/layers/batch_norm.py |  1 +
 baselines/fjord/fjord/od/layers/conv.py       |  1 +
 baselines/fjord/fjord/od/layers/linear.py     |  1 +
 baselines/fjord/fjord/od/layers/utils.py      |  1 +
 baselines/fjord/fjord/od/models/utils.py      |  1 +
 baselines/fjord/fjord/od/samplers/__init__.py |  1 +
 .../fjord/fjord/od/samplers/base_sampler.py   |  1 +
 baselines/fjord/fjord/od/samplers/fixed_od.py |  1 +
 baselines/fjord/fjord/server.py               |  1 +
 baselines/fjord/fjord/strategy.py             |  1 +
 baselines/fjord/fjord/utils/logger.py         |  1 +
 baselines/fjord/fjord/utils/utils.py          |  1 +
 baselines/fjord/pyproject.toml                |  4 +-
 baselines/fjord/setup.py                      |  1 +
 .../cifar/client.py                           |  1 -
 .../cifar/utils.py                            |  1 -
 .../adaptive_federated_optimization/main.py   |  1 -
 .../publications/example_test.py              |  1 -
 .../publications/fedavg_mnist/dataset.py      |  1 -
 .../publications/fedavg_mnist/main.py         |  1 -
 .../publications/fedavg_mnist/model.py        |  1 -
 .../fedavg_mnist/tests/data_test.py           |  1 -
 .../fedavg_mnist/tests/model_test.py          |  1 -
 .../publications/fedavg_mnist/utils.py        |  1 -
 .../publications/leaf/femnist/client.py       |  1 -
 .../leaf/femnist/dataset/dataset.py           |  1 -
 .../leaf/femnist/dataset/dataset_test.py      |  1 +
 .../leaf/femnist/dataset/nist_preprocessor.py |  1 -
 .../femnist/dataset/nist_preprocessor_test.py |  1 +
 .../leaf/femnist/dataset/nist_sampler.py      |  1 -
 .../leaf/femnist/dataset/nist_sampler_test.py |  1 +
 .../leaf/femnist/dataset/utils.py             |  1 -
 .../leaf/femnist/dataset/zip_downloader.py    |  1 -
 .../publications/leaf/femnist/main.py         |  1 -
 .../publications/leaf/femnist/model.py        |  1 -
 .../publications/leaf/femnist/model_test.py   |  1 +
 .../publications/leaf/femnist/strategy.py     |  1 -
 .../publications/leaf/femnist/utils.py        |  1 -
 baselines/flwr_baselines/pyproject.toml       |  4 +-
 baselines/flwr_baselines/requirements.txt     |  4 +-
 .../heterofl/heterofl/datasets/__init__.py    |  1 +
 baselines/heterofl/heterofl/datasets/cifar.py |  1 +
 baselines/heterofl/heterofl/datasets/mnist.py |  1 +
 baselines/heterofl/heterofl/datasets/utils.py |  1 +
 baselines/heterofl/heterofl/main.py           | 41 ++++++++++++-------
 .../heterofl/heterofl/model_properties.py     |  1 +
 baselines/heterofl/heterofl/models.py         |  1 +
 baselines/heterofl/heterofl/server.py         |  1 +
 baselines/heterofl/heterofl/strategy.py       |  1 +
 baselines/heterofl/heterofl/utils.py          |  1 +
 baselines/heterofl/pyproject.toml             |  4 +-
 baselines/hfedxgboost/hfedxgboost/client.py   |  1 +
 baselines/hfedxgboost/hfedxgboost/dataset.py  |  1 +
 .../hfedxgboost/dataset_preparation.py        |  1 +
 baselines/hfedxgboost/hfedxgboost/main.py     |  1 +
 baselines/hfedxgboost/hfedxgboost/models.py   |  1 +
 baselines/hfedxgboost/hfedxgboost/strategy.py |  1 +
 baselines/hfedxgboost/hfedxgboost/utils.py    |  7 ++--
 baselines/hfedxgboost/pyproject.toml          |  4 +-
 baselines/moon/moon/main.py                   |  1 +
 baselines/moon/moon/models.py                 |  1 -
 baselines/moon/moon/utils.py                  |  1 +
 baselines/moon/pyproject.toml                 |  4 +-
 baselines/niid_bench/niid_bench/main.py       |  1 +
 .../niid_bench/niid_bench/server_scaffold.py  |  6 +--
 baselines/niid_bench/pyproject.toml           |  4 +-
 baselines/tamuna/pyproject.toml               |  4 +-
 baselines/tamuna/tamuna/client.py             |  1 +
 baselines/tamuna/tamuna/dataset.py            |  1 +
 baselines/tamuna/tamuna/main.py               |  1 +
 baselines/tamuna/tamuna/models.py             |  1 +
 datasets/pyproject.toml                       |  4 +-
 .../client.py                                 |  1 -
 .../mxnet_mnist.py                            |  1 -
 .../server.py                                 |  1 -
 .../client.py                                 |  1 +
 .../server.py                                 |  1 -
 examples/quickstart-jax/client.py             |  1 -
 examples/quickstart-jax/jax_training.py       |  1 -
 examples/quickstart-mxnet/client.py           |  1 -
 examples/quickstart-mxnet/server.py           |  1 -
 examples/xgboost-comprehensive/server.py      |  6 +--
 examples/xgboost-comprehensive/sim.py         | 18 ++++----
 pyproject.toml                                |  2 +-
 src/py/flwr/client/app.py                     |  4 +-
 src/py/flwr/common/version.py                 |  1 -
 src/py/flwr/server/strategy/dpfedavg_fixed.py |  6 +--
 .../baseline/tf_cifar/settings.py             |  6 +--
 .../baseline/tf_fashion_mnist/settings.py     |  6 +--
 .../baseline/tf_hotkey/settings.py            |  6 +--
 src/py/flwr_experimental/logserver/server.py  |  6 +--
 src/py/flwr_experimental/ops/cluster.py       |  8 ++--
 154 files changed, 213 insertions(+), 168 deletions(-)

diff --git a/baselines/baseline_template/pyproject.toml b/baselines/baseline_template/pyproject.toml
index ba079ddf6573..bb1b48357944 100644
--- a/baselines/baseline_template/pyproject.toml
+++ b/baselines/baseline_template/pyproject.toml
@@ -42,8 +42,8 @@ flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/dasha/pyproject.toml b/baselines/dasha/pyproject.toml
index f227293acc9d..135a797c0d3d 100644
--- a/baselines/dasha/pyproject.toml
+++ b/baselines/dasha/pyproject.toml
@@ -49,8 +49,8 @@ torchvision = [{ url = "https://download.pytorch.org/whl/cu118/torchvision-0.15.
                { url = "https://download.pytorch.org/whl/cpu/torchvision-0.15.0-cp39-cp39-macosx_11_0_arm64.whl", markers="sys_platform == 'darwin'"}]
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/depthfl/depthfl/models.py b/baselines/depthfl/depthfl/models.py
index df3eebf9f9ce..5ce5eedb360e 100644
--- a/baselines/depthfl/depthfl/models.py
+++ b/baselines/depthfl/depthfl/models.py
@@ -1,6 +1,5 @@
 """ResNet18 model architecutre, training, and testing functions for CIFAR100."""
 
-
 from typing import List, Tuple
 
 import torch
diff --git a/baselines/depthfl/pyproject.toml b/baselines/depthfl/pyproject.toml
index a1394c89434c..14e392478330 100644
--- a/baselines/depthfl/pyproject.toml
+++ b/baselines/depthfl/pyproject.toml
@@ -45,8 +45,8 @@ torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.13.1%2Bcu116-cp3
 torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.14.1%2Bcu116-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/fedavgm/pyproject.toml b/baselines/fedavgm/pyproject.toml
index b1f50cdc4a3a..c0409d48e5a3 100644
--- a/baselines/fedavgm/pyproject.toml
+++ b/baselines/fedavgm/pyproject.toml
@@ -46,8 +46,8 @@ numpy = "1.25.2"
 matplotlib = "^3.7.2"
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/fedbn/fedbn/dataset.py b/baselines/fedbn/fedbn/dataset.py
index 8ce3eb520f89..58f3462fe9f7 100644
--- a/baselines/fedbn/fedbn/dataset.py
+++ b/baselines/fedbn/fedbn/dataset.py
@@ -1,4 +1,5 @@
 """Digits dataset."""
+
 import os
 from random import shuffle
 from typing import List, Optional, Tuple
diff --git a/baselines/fedbn/fedbn/strategy.py b/baselines/fedbn/fedbn/strategy.py
index 0f457daedb33..f69be79a7182 100644
--- a/baselines/fedbn/fedbn/strategy.py
+++ b/baselines/fedbn/fedbn/strategy.py
@@ -3,6 +3,7 @@
 Needed only when the strategy is not yet implemented in Flower or because you want to
 extend or modify the functionality of an existing strategy.
 """
+
 from typing import Dict, List, Tuple
 
 from flwr.common.typing import Metrics
diff --git a/baselines/fedbn/pyproject.toml b/baselines/fedbn/pyproject.toml
index ff142c6b2435..f8c795921ed1 100644
--- a/baselines/fedbn/pyproject.toml
+++ b/baselines/fedbn/pyproject.toml
@@ -46,8 +46,8 @@ torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2
 tqdm = "^4.66.1"
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/fedmeta/fedmeta/dataset_preparation.py b/baselines/fedmeta/fedmeta/dataset_preparation.py
index c139cdf86d69..6e952306f811 100644
--- a/baselines/fedmeta/fedmeta/dataset_preparation.py
+++ b/baselines/fedmeta/fedmeta/dataset_preparation.py
@@ -6,6 +6,7 @@
 uncomment the lines below and tell us in the README.md (see the "Running the Experiment"
 block) that this file should be executed first.
 """
+
 import json
 import os
 from collections import defaultdict
diff --git a/baselines/fedmeta/fedmeta/main.py b/baselines/fedmeta/fedmeta/main.py
index e43ad94a3089..788286c4fddf 100644
--- a/baselines/fedmeta/fedmeta/main.py
+++ b/baselines/fedmeta/fedmeta/main.py
@@ -4,7 +4,6 @@
 model is going to be evaluated, etc. At the end, this script saves the results.
 """
 
-
 import flwr as fl
 import hydra
 from hydra.core.hydra_config import HydraConfig
diff --git a/baselines/fedmeta/fedmeta/strategy.py b/baselines/fedmeta/fedmeta/strategy.py
index 7947938116e9..a12f556dc8b8 100644
--- a/baselines/fedmeta/fedmeta/strategy.py
+++ b/baselines/fedmeta/fedmeta/strategy.py
@@ -3,6 +3,7 @@
 Needed only when the strategy is not yet implemented in Flower or because you want to
 extend or modify the functionality of an existing strategy.
 """
+
 from collections import OrderedDict
 from logging import WARNING
 from typing import Dict, List, Optional, Tuple, Union
diff --git a/baselines/fedmeta/pyproject.toml b/baselines/fedmeta/pyproject.toml
index aa5d2f3db618..515953ff38f7 100644
--- a/baselines/fedmeta/pyproject.toml
+++ b/baselines/fedmeta/pyproject.toml
@@ -47,8 +47,8 @@ torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2
 pillow = "9.5.0" # needed <10.0.0 for LEAF repo scripts
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/fedmlb/fedmlb/dataset.py b/baselines/fedmlb/fedmlb/dataset.py
index 290f7600332f..6633849a73f7 100644
--- a/baselines/fedmlb/fedmlb/dataset.py
+++ b/baselines/fedmlb/fedmlb/dataset.py
@@ -1,4 +1,5 @@
 """Handle dataset loading and preprocessing utility."""
+
 import os
 from typing import Union
 
diff --git a/baselines/fedmlb/pyproject.toml b/baselines/fedmlb/pyproject.toml
index 2b21172f4679..21ada9a4585e 100644
--- a/baselines/fedmlb/pyproject.toml
+++ b/baselines/fedmlb/pyproject.toml
@@ -45,8 +45,8 @@ matplotlib = "3.7.1"
 psutil = "5.9.5" # this is just to log some info about memory usage
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/fednova/fednova/dataset_preparation.py b/baselines/fednova/fednova/dataset_preparation.py
index f5cc63c19df9..d118d79c4d40 100644
--- a/baselines/fednova/fednova/dataset_preparation.py
+++ b/baselines/fednova/fednova/dataset_preparation.py
@@ -3,7 +3,6 @@
 obtained from source: https://github.com/JYWa/FedNova/blob/master/util_v4.py
 """
 
-
 from random import Random
 
 import numpy as np
diff --git a/baselines/fednova/fednova/utils.py b/baselines/fednova/fednova/utils.py
index f3f7de1e8ac0..7d16c76fcf79 100644
--- a/baselines/fednova/fednova/utils.py
+++ b/baselines/fednova/fednova/utils.py
@@ -1,6 +1,5 @@
 """Utility functions for FedNova such as computing accuracy, plotting results, etc."""
 
-
 import glob
 import os
 from typing import List, Tuple
diff --git a/baselines/fednova/pyproject.toml b/baselines/fednova/pyproject.toml
index 5856fbe262ba..9b3a0d99d581 100644
--- a/baselines/fednova/pyproject.toml
+++ b/baselines/fednova/pyproject.toml
@@ -48,8 +48,8 @@ matplotlib = "3.5.3"
 pandas = "1.3.5"
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/fedpara/fedpara/models.py b/baselines/fedpara/fedpara/models.py
index 370720ff58ce..2546b950283f 100644
--- a/baselines/fedpara/fedpara/models.py
+++ b/baselines/fedpara/fedpara/models.py
@@ -196,9 +196,7 @@ def _calc_from_ratio(self):
         # r3 is floored because we cannot take the ceil as it results a bigger number
         # of parameters than the original problem
 
-        num_target_params = (
-            self.out_channels * self.in_channels * (self.kernel_size**2)
-        )
+        num_target_params = self.out_channels * self.in_channels * (self.kernel_size**2)
         a, b, c = (
             self.kernel_size**2,
             self.out_channels + self.in_channels,
diff --git a/baselines/fedpara/pyproject.toml b/baselines/fedpara/pyproject.toml
index f802b27a7af1..3e5b6495b88a 100644
--- a/baselines/fedpara/pyproject.toml
+++ b/baselines/fedpara/pyproject.toml
@@ -46,8 +46,8 @@ torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp31
 torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/fedper/fedper/client.py b/baselines/fedper/fedper/client.py
index 83babbd9613f..2f9446a99de9 100644
--- a/baselines/fedper/fedper/client.py
+++ b/baselines/fedper/fedper/client.py
@@ -1,4 +1,5 @@
 """Client implementation - can call FedPer and FedAvg clients."""
+
 import pickle
 from collections import OrderedDict, defaultdict
 from pathlib import Path
@@ -322,9 +323,9 @@ def client_fn(cid: str) -> BaseClient:
         # Create the test loader
         testloader = DataLoader(testset, config.batch_size)
 
-        manager: Union[
-            Type[MobileNetModelManager], Type[ResNetModelManager]
-        ] = MobileNetModelManager
+        manager: Union[Type[MobileNetModelManager], Type[ResNetModelManager]] = (
+            MobileNetModelManager
+        )
         if config.model_name.lower() == "resnet":
             manager = ResNetModelManager
         elif config.model_name.lower() == "mobile":
diff --git a/baselines/fedper/fedper/constants.py b/baselines/fedper/fedper/constants.py
index 3eda77c5134e..b11945296692 100644
--- a/baselines/fedper/fedper/constants.py
+++ b/baselines/fedper/fedper/constants.py
@@ -1,4 +1,5 @@
 """Constants used in machine learning pipeline."""
+
 from enum import Enum
 
 
diff --git a/baselines/fedper/fedper/dataset.py b/baselines/fedper/fedper/dataset.py
index 81a95286b1b8..63724bb90eda 100644
--- a/baselines/fedper/fedper/dataset.py
+++ b/baselines/fedper/fedper/dataset.py
@@ -8,6 +8,7 @@
 `dataset_preparation.py` module. You can use all those functions from functions/methods
 defined here of course.
 """
+
 import os
 import pickle
 import sys
diff --git a/baselines/fedper/fedper/dataset_preparation.py b/baselines/fedper/fedper/dataset_preparation.py
index 0b8b53782aac..fa1492c38eb9 100644
--- a/baselines/fedper/fedper/dataset_preparation.py
+++ b/baselines/fedper/fedper/dataset_preparation.py
@@ -1,4 +1,5 @@
 """Dataset preparation."""
+
 import os
 import random
 from collections import Counter
diff --git a/baselines/fedper/fedper/implemented_models/mobile_model.py b/baselines/fedper/fedper/implemented_models/mobile_model.py
index 57d3210c9511..ea9b79726c09 100644
--- a/baselines/fedper/fedper/implemented_models/mobile_model.py
+++ b/baselines/fedper/fedper/implemented_models/mobile_model.py
@@ -1,4 +1,5 @@
 """MobileNet-v1 model, model manager and model split."""
+
 from typing import Dict, List, Optional, Tuple, Union
 
 import torch
diff --git a/baselines/fedper/fedper/implemented_models/resnet_model.py b/baselines/fedper/fedper/implemented_models/resnet_model.py
index 0d9837b118a3..fc742885fab6 100644
--- a/baselines/fedper/fedper/implemented_models/resnet_model.py
+++ b/baselines/fedper/fedper/implemented_models/resnet_model.py
@@ -1,4 +1,5 @@
 """ResNet model, model manager and split."""
+
 from typing import Dict, List, Optional, Tuple, Union
 
 import torch
diff --git a/baselines/fedper/fedper/models.py b/baselines/fedper/fedper/models.py
index 2a2ebde158f8..be2bda9ae88c 100644
--- a/baselines/fedper/fedper/models.py
+++ b/baselines/fedper/fedper/models.py
@@ -1,4 +1,5 @@
 """Abstract class for splitting a model into body and head."""
+
 from abc import ABC, abstractmethod
 from collections import OrderedDict
 from typing import Any, Dict, List, Tuple, Type, Union
diff --git a/baselines/fedper/fedper/server.py b/baselines/fedper/fedper/server.py
index 93616f50f45a..50a4f8c5d8a8 100644
--- a/baselines/fedper/fedper/server.py
+++ b/baselines/fedper/fedper/server.py
@@ -1,4 +1,5 @@
 """Server strategies pipelines for FedPer."""
+
 from flwr.server.strategy.fedavg import FedAvg
 
 from fedper.strategy import (
diff --git a/baselines/fedper/fedper/strategy.py b/baselines/fedper/fedper/strategy.py
index 5ae55086db2f..527397cf1455 100644
--- a/baselines/fedper/fedper/strategy.py
+++ b/baselines/fedper/fedper/strategy.py
@@ -1,4 +1,5 @@
 """FL server strategies."""
+
 from collections import OrderedDict
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
diff --git a/baselines/fedper/fedper/utils.py b/baselines/fedper/fedper/utils.py
index 00b4c5318729..524b68c2e225 100644
--- a/baselines/fedper/fedper/utils.py
+++ b/baselines/fedper/fedper/utils.py
@@ -1,4 +1,5 @@
 """Utility functions for FedPer."""
+
 import os
 import pickle
 import time
@@ -97,9 +98,9 @@ def get_create_model_fn(
 ]:
     """Get create model function."""
     device = config.server_device
-    split: Union[
-        Type[MobileNetModelSplit], Type[ResNetModelSplit]
-    ] = MobileNetModelSplit
+    split: Union[Type[MobileNetModelSplit], Type[ResNetModelSplit]] = (
+        MobileNetModelSplit
+    )
     if config.model_name.lower() == "mobile":
 
         def create_model() -> Union[Type[MobileNet], Type[ResNet]]:
diff --git a/baselines/fedper/pyproject.toml b/baselines/fedper/pyproject.toml
index 84086d1f3660..4ee7ad0f02d6 100644
--- a/baselines/fedper/pyproject.toml
+++ b/baselines/fedper/pyproject.toml
@@ -47,8 +47,8 @@ torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp31
 torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/fedprox/fedprox/client.py b/baselines/fedprox/fedprox/client.py
index e168a9ee24ab..d10a368edae4 100644
--- a/baselines/fedprox/fedprox/client.py
+++ b/baselines/fedprox/fedprox/client.py
@@ -1,6 +1,5 @@
 """Defines the MNIST Flower Client and a function to instantiate it."""
 
-
 from collections import OrderedDict
 from typing import Callable, Dict, List, Tuple
 
diff --git a/baselines/fedprox/fedprox/dataset.py b/baselines/fedprox/fedprox/dataset.py
index e740ef0f67a9..d66aed583623 100644
--- a/baselines/fedprox/fedprox/dataset.py
+++ b/baselines/fedprox/fedprox/dataset.py
@@ -1,6 +1,5 @@
 """MNIST dataset utilities for federated learning."""
 
-
 from typing import Optional, Tuple
 
 import torch
diff --git a/baselines/fedprox/fedprox/dataset_preparation.py b/baselines/fedprox/fedprox/dataset_preparation.py
index 6c44c7411c30..3b9181eaf715 100644
--- a/baselines/fedprox/fedprox/dataset_preparation.py
+++ b/baselines/fedprox/fedprox/dataset_preparation.py
@@ -1,4 +1,5 @@
 """Functions for dataset download and processing."""
+
 from typing import List, Optional, Tuple
 
 import numpy as np
diff --git a/baselines/fedprox/fedprox/models.py b/baselines/fedprox/fedprox/models.py
index 1086f36404aa..b46ce1fdce5a 100644
--- a/baselines/fedprox/fedprox/models.py
+++ b/baselines/fedprox/fedprox/models.py
@@ -1,6 +1,5 @@
 """CNN model architecture, training, and testing functions for MNIST."""
 
-
 from typing import List, Tuple
 
 import torch
diff --git a/baselines/fedprox/fedprox/server.py b/baselines/fedprox/fedprox/server.py
index 3f0f00bea63b..b90b1708d01e 100644
--- a/baselines/fedprox/fedprox/server.py
+++ b/baselines/fedprox/fedprox/server.py
@@ -1,6 +1,5 @@
 """Flower Server."""
 
-
 from collections import OrderedDict
 from typing import Callable, Dict, Optional, Tuple
 
diff --git a/baselines/fedprox/fedprox/strategy.py b/baselines/fedprox/fedprox/strategy.py
index 5086d229da24..bdee4c5e3c3b 100644
--- a/baselines/fedprox/fedprox/strategy.py
+++ b/baselines/fedprox/fedprox/strategy.py
@@ -1,6 +1,5 @@
 """Flower strategy."""
 
-
 from typing import List, Tuple, Union
 
 from flwr.common import Metrics
diff --git a/baselines/fedprox/pyproject.toml b/baselines/fedprox/pyproject.toml
index 941a85443c84..ad31dccc11d1 100644
--- a/baselines/fedprox/pyproject.toml
+++ b/baselines/fedprox/pyproject.toml
@@ -47,8 +47,8 @@ torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp31
 torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/fedstar/fedstar/client.py b/baselines/fedstar/fedstar/client.py
index e126d94933fe..cc6a24c6ff9f 100644
--- a/baselines/fedstar/fedstar/client.py
+++ b/baselines/fedstar/fedstar/client.py
@@ -1,4 +1,5 @@
 """Required imports for client.py script."""
+
 import gc
 import logging
 import os
diff --git a/baselines/fedstar/fedstar/clients.py b/baselines/fedstar/fedstar/clients.py
index 55ca213e86f1..ffcd68a6d5c4 100644
--- a/baselines/fedstar/fedstar/clients.py
+++ b/baselines/fedstar/fedstar/clients.py
@@ -1,4 +1,5 @@
 """Required imports for clients.py script."""
+
 import multiprocessing
 import os
 import sys
diff --git a/baselines/fedstar/fedstar/dataset.py b/baselines/fedstar/fedstar/dataset.py
index e376a6b318f0..929507a19b0c 100644
--- a/baselines/fedstar/fedstar/dataset.py
+++ b/baselines/fedstar/fedstar/dataset.py
@@ -1,4 +1,5 @@
 """Required imports for data.py script."""
+
 import os
 
 import numpy as np
diff --git a/baselines/fedstar/fedstar/dataset_preparation.py b/baselines/fedstar/fedstar/dataset_preparation.py
index 64857318b747..c41abbadbc16 100644
--- a/baselines/fedstar/fedstar/dataset_preparation.py
+++ b/baselines/fedstar/fedstar/dataset_preparation.py
@@ -1,4 +1,5 @@
 """Generate 1-second audio clip from background sounds in speechcommands."""
+
 import os
 import random
 
diff --git a/baselines/fedstar/fedstar/models.py b/baselines/fedstar/fedstar/models.py
index 2e2682096a0e..a8f6d0333fea 100644
--- a/baselines/fedstar/fedstar/models.py
+++ b/baselines/fedstar/fedstar/models.py
@@ -1,4 +1,5 @@
 """Required imports for data.py script."""
+
 import itertools
 import time
 
diff --git a/baselines/fedstar/fedstar/server.py b/baselines/fedstar/fedstar/server.py
index 318613b4db14..7247973d65fa 100644
--- a/baselines/fedstar/fedstar/server.py
+++ b/baselines/fedstar/fedstar/server.py
@@ -1,4 +1,5 @@
 """Required imports for server.py script."""
+
 import gc
 import logging
 import os
diff --git a/baselines/fedstar/fedstar/utils.py b/baselines/fedstar/fedstar/utils.py
index 18bd049614e7..89d2ec3303b5 100644
--- a/baselines/fedstar/fedstar/utils.py
+++ b/baselines/fedstar/fedstar/utils.py
@@ -1,4 +1,5 @@
 """Required imports for utils.py script."""
+
 import collections
 import itertools
 import random
@@ -428,9 +429,13 @@ def distribute_fun():
         if overlap:
             dataset_use = dict(sorted(collections.Counter(distribution).items()))
             class_datasets = [
-                class_datasets[c]
-                if dataset_use[c] == 1
-                else DataTools.split_class_samples(class_datasets[c], dataset_use[c])
+                (
+                    class_datasets[c]
+                    if dataset_use[c] == 1
+                    else DataTools.split_class_samples(
+                        class_datasets[c], dataset_use[c]
+                    )
+                )
                 for c in range(num_classes)
             ]
             parts = list(
@@ -440,9 +445,11 @@ def distribute_fun():
             )
             _iter_ = iter(parts)
             datasets = [
-                class_datasets[i]
-                if dataset_use[i] == 1
-                else class_datasets[i][next(_iter_)]
+                (
+                    class_datasets[i]
+                    if dataset_use[i] == 1
+                    else class_datasets[i][next(_iter_)]
+                )
                 for i in distribution
             ]
             datasets = [DataTools.shuffle_dataset(dataset) for dataset in datasets]
@@ -542,9 +549,11 @@ def distribute_per_class_with_class_limit(
             for class_number in range(num_classes)
         ]
         class_datasets = [
-            class_datasets[c]
-            if dataset_use[c] == 1
-            else DataTools.split_class_samples(class_datasets[c], dataset_use[c])
+            (
+                class_datasets[c]
+                if dataset_use[c] == 1
+                else DataTools.split_class_samples(class_datasets[c], dataset_use[c])
+            )
             for c in range(num_classes)
         ]
         # Keep track of splitted partitions
@@ -552,9 +561,11 @@ def distribute_per_class_with_class_limit(
         # Fill datasets with examples from class distribution.
         datasets = [
             [
-                class_datasets[j]
-                if (len(parts[j]) == 1 and isinstance(class_datasets[j], tuple))
-                else class_datasets[j][parts[j].pop()]
+                (
+                    class_datasets[j]
+                    if (len(parts[j]) == 1 and isinstance(class_datasets[j], tuple))
+                    else class_datasets[j][parts[j].pop()]
+                )
                 for j in dis
             ]
             for dis in distributions
diff --git a/baselines/fedstar/pyproject.toml b/baselines/fedstar/pyproject.toml
index 93d1eb2e042d..fbcaa666ba30 100644
--- a/baselines/fedstar/pyproject.toml
+++ b/baselines/fedstar/pyproject.toml
@@ -46,8 +46,8 @@ scipy = "^1.11.4"
 tqdm = "^4.66.1"
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==3.0.3"
diff --git a/baselines/fedvssl/fedvssl/client.py b/baselines/fedvssl/fedvssl/client.py
index 6d2ac25dc1ea..6f0d3b372097 100644
--- a/baselines/fedvssl/fedvssl/client.py
+++ b/baselines/fedvssl/fedvssl/client.py
@@ -3,6 +3,7 @@
 Please overwrite `flwr.client.NumPyClient` or `flwr.client.Client` and create a function
 to instantiate your client.
 """
+
 import os
 import re
 from collections import OrderedDict
diff --git a/baselines/fedvssl/fedvssl/dataset_convert_to_json.py b/baselines/fedvssl/fedvssl/dataset_convert_to_json.py
index b4e619a5ff0a..b82f11f165c4 100644
--- a/baselines/fedvssl/fedvssl/dataset_convert_to_json.py
+++ b/baselines/fedvssl/fedvssl/dataset_convert_to_json.py
@@ -1,4 +1,5 @@
 """Dataset pre-processing: convert .txt files to .json files."""
+
 import json
 
 # We use the .json files for the annotations.
diff --git a/baselines/fedvssl/fedvssl/main.py b/baselines/fedvssl/fedvssl/main.py
index 8d302b80a5af..6087a82e9706 100644
--- a/baselines/fedvssl/fedvssl/main.py
+++ b/baselines/fedvssl/fedvssl/main.py
@@ -3,6 +3,7 @@
 It includes processioning the dataset, instantiate strategy, specify how the global
 model is going to be evaluated, etc. At the end, this script saves the results.
 """
+
 # These are the basic packages you'll need here
 
 from argparse import Namespace
diff --git a/baselines/fedvssl/fedvssl/strategy.py b/baselines/fedvssl/fedvssl/strategy.py
index 8fcf3cce766f..69e786d71e41 100644
--- a/baselines/fedvssl/fedvssl/strategy.py
+++ b/baselines/fedvssl/fedvssl/strategy.py
@@ -3,6 +3,7 @@
 Needed only when the strategy is not yet implemented in Flower or because you want to
 extend or modify the functionality of an existing strategy.
 """
+
 # import all the necessary libraries
 import os
 import re
diff --git a/baselines/fedvssl/pyproject.toml b/baselines/fedvssl/pyproject.toml
index fc359409dbe8..43128ad59f1d 100644
--- a/baselines/fedvssl/pyproject.toml
+++ b/baselines/fedvssl/pyproject.toml
@@ -49,8 +49,8 @@ numpy = "1.23.0"
 mmcv-full = { url = "https://download.openmmlab.com/mmcv/dist/cu117/torch1.13.0/mmcv_full-1.7.1-cp310-cp310-manylinux1_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/fedwav2vec2/fedwav2vec2/client.py b/baselines/fedwav2vec2/fedwav2vec2/client.py
index 319580a83845..eb276ab07ce8 100644
--- a/baselines/fedwav2vec2/fedwav2vec2/client.py
+++ b/baselines/fedwav2vec2/fedwav2vec2/client.py
@@ -4,7 +4,6 @@
 to instantiate your client.
 """
 
-
 import gc
 import logging
 from math import exp
diff --git a/baselines/fedwav2vec2/fedwav2vec2/dataset.py b/baselines/fedwav2vec2/fedwav2vec2/dataset.py
index 65fee90faf38..b09fd9b24472 100644
--- a/baselines/fedwav2vec2/fedwav2vec2/dataset.py
+++ b/baselines/fedwav2vec2/fedwav2vec2/dataset.py
@@ -9,7 +9,6 @@
 defined here of course.
 """
 
-
 import speechbrain as sb
 import torchaudio
 
diff --git a/baselines/fedwav2vec2/fedwav2vec2/dataset_preparation.py b/baselines/fedwav2vec2/fedwav2vec2/dataset_preparation.py
index 255664d3d2c3..7fca743ac7dd 100644
--- a/baselines/fedwav2vec2/fedwav2vec2/dataset_preparation.py
+++ b/baselines/fedwav2vec2/fedwav2vec2/dataset_preparation.py
@@ -7,7 +7,6 @@
 block) that this file should be executed first.
 """
 
-
 import os
 import ssl
 import tarfile
diff --git a/baselines/fedwav2vec2/fedwav2vec2/models.py b/baselines/fedwav2vec2/fedwav2vec2/models.py
index 38916aaac555..02877ed1594a 100644
--- a/baselines/fedwav2vec2/fedwav2vec2/models.py
+++ b/baselines/fedwav2vec2/fedwav2vec2/models.py
@@ -6,7 +6,6 @@
 the python code at all
 """
 
-
 import gc
 import os
 
diff --git a/baselines/fedwav2vec2/fedwav2vec2/sb_recipe.py b/baselines/fedwav2vec2/fedwav2vec2/sb_recipe.py
index 390edfe246d3..40bd5a1742eb 100644
--- a/baselines/fedwav2vec2/fedwav2vec2/sb_recipe.py
+++ b/baselines/fedwav2vec2/fedwav2vec2/sb_recipe.py
@@ -1,6 +1,5 @@
 """Main SpeechBrain training and testing logic."""
 
-
 import gc
 import time
 from collections import OrderedDict
diff --git a/baselines/fedwav2vec2/fedwav2vec2/strategy.py b/baselines/fedwav2vec2/fedwav2vec2/strategy.py
index 6aa6007e4ccd..cee3ca0f8d8d 100644
--- a/baselines/fedwav2vec2/fedwav2vec2/strategy.py
+++ b/baselines/fedwav2vec2/fedwav2vec2/strategy.py
@@ -4,7 +4,6 @@
 extend or modify the functionality of an existing strategy.
 """
 
-
 import gc
 from typing import Dict, List, Optional, Tuple, Union
 
diff --git a/baselines/fedwav2vec2/pyproject.toml b/baselines/fedwav2vec2/pyproject.toml
index 551c1c697650..406fc6083533 100644
--- a/baselines/fedwav2vec2/pyproject.toml
+++ b/baselines/fedwav2vec2/pyproject.toml
@@ -47,8 +47,8 @@ torchaudio = { url = "https://download.pytorch.org/whl/cu116/torchaudio-0.13.1%2
 transformers = "4.33.2"
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/fjord/fjord/client.py b/baselines/fjord/fjord/client.py
index 2b18d9547086..845a1cec9815 100644
--- a/baselines/fjord/fjord/client.py
+++ b/baselines/fjord/fjord/client.py
@@ -1,4 +1,5 @@
 """Flower client implementing FjORD."""
+
 from collections import OrderedDict
 from copy import deepcopy
 from types import SimpleNamespace
diff --git a/baselines/fjord/fjord/dataset.py b/baselines/fjord/fjord/dataset.py
index 478826c2cf64..7809ec27f2e9 100644
--- a/baselines/fjord/fjord/dataset.py
+++ b/baselines/fjord/fjord/dataset.py
@@ -1,4 +1,5 @@
 """Dataset for CIFAR10."""
+
 import random
 from typing import Optional, Tuple
 
diff --git a/baselines/fjord/fjord/main.py b/baselines/fjord/fjord/main.py
index f85fb9ccf158..0863c12f0212 100644
--- a/baselines/fjord/fjord/main.py
+++ b/baselines/fjord/fjord/main.py
@@ -1,4 +1,5 @@
 """Main script for FjORD."""
+
 import math
 import os
 import random
diff --git a/baselines/fjord/fjord/models.py b/baselines/fjord/fjord/models.py
index 0f3fc276decf..a7821b09f7cd 100644
--- a/baselines/fjord/fjord/models.py
+++ b/baselines/fjord/fjord/models.py
@@ -1,4 +1,5 @@
 """ResNet model for Fjord."""
+
 from types import SimpleNamespace
 from typing import List, Optional, Tuple
 
diff --git a/baselines/fjord/fjord/od/layers/__init__.py b/baselines/fjord/fjord/od/layers/__init__.py
index a87c70401d4c..3ff9d4543f06 100644
--- a/baselines/fjord/fjord/od/layers/__init__.py
+++ b/baselines/fjord/fjord/od/layers/__init__.py
@@ -1,4 +1,5 @@
 """Ordered Dropout layers."""
+
 from .batch_norm import ODBatchNorm2d
 from .conv import ODConv2d
 from .linear import ODLinear
diff --git a/baselines/fjord/fjord/od/layers/batch_norm.py b/baselines/fjord/fjord/od/layers/batch_norm.py
index 5fce4dff0910..b02e2d9f638a 100644
--- a/baselines/fjord/fjord/od/layers/batch_norm.py
+++ b/baselines/fjord/fjord/od/layers/batch_norm.py
@@ -1,4 +1,5 @@
 """BatchNorm using Ordered Dropout."""
+
 from typing import List, Optional
 
 import numpy as np
diff --git a/baselines/fjord/fjord/od/layers/conv.py b/baselines/fjord/fjord/od/layers/conv.py
index 544f3a578418..d68e263c2abb 100644
--- a/baselines/fjord/fjord/od/layers/conv.py
+++ b/baselines/fjord/fjord/od/layers/conv.py
@@ -1,4 +1,5 @@
 """Convolutional layer using Ordered Dropout."""
+
 from typing import Optional, Tuple, Union
 
 import numpy as np
diff --git a/baselines/fjord/fjord/od/layers/linear.py b/baselines/fjord/fjord/od/layers/linear.py
index 927ae4c8d516..3ec4dce3651e 100644
--- a/baselines/fjord/fjord/od/layers/linear.py
+++ b/baselines/fjord/fjord/od/layers/linear.py
@@ -1,4 +1,5 @@
 """Liner layer using Ordered Dropout."""
+
 from typing import Optional, Tuple, Union
 
 import numpy as np
diff --git a/baselines/fjord/fjord/od/layers/utils.py b/baselines/fjord/fjord/od/layers/utils.py
index 46649a51de96..7fc39abad712 100644
--- a/baselines/fjord/fjord/od/layers/utils.py
+++ b/baselines/fjord/fjord/od/layers/utils.py
@@ -1,4 +1,5 @@
 """Utils function for Ordered Dropout layers."""
+
 from typing import Optional, Tuple, Union
 
 from torch.nn import Module
diff --git a/baselines/fjord/fjord/od/models/utils.py b/baselines/fjord/fjord/od/models/utils.py
index 4a1707587ef4..7e121c32964b 100644
--- a/baselines/fjord/fjord/od/models/utils.py
+++ b/baselines/fjord/fjord/od/models/utils.py
@@ -1,4 +1,5 @@
 """Utility functions for models."""
+
 from torch import nn
 
 from ..layers import ODBatchNorm2d, ODConv2d, ODLinear
diff --git a/baselines/fjord/fjord/od/samplers/__init__.py b/baselines/fjord/fjord/od/samplers/__init__.py
index dad08b4236c4..d94b2d49ac61 100644
--- a/baselines/fjord/fjord/od/samplers/__init__.py
+++ b/baselines/fjord/fjord/od/samplers/__init__.py
@@ -1,4 +1,5 @@
 """OD samplers."""
+
 from .base_sampler import BaseSampler
 from .fixed_od import ODSampler
 
diff --git a/baselines/fjord/fjord/od/samplers/base_sampler.py b/baselines/fjord/fjord/od/samplers/base_sampler.py
index 28eac929df81..fd67a7d986ba 100644
--- a/baselines/fjord/fjord/od/samplers/base_sampler.py
+++ b/baselines/fjord/fjord/od/samplers/base_sampler.py
@@ -1,4 +1,5 @@
 """Base sampler class."""
+
 from collections.abc import Generator
 
 from torch.nn import Module
diff --git a/baselines/fjord/fjord/od/samplers/fixed_od.py b/baselines/fjord/fjord/od/samplers/fixed_od.py
index b90912a7b5c2..51aa5a343cd6 100644
--- a/baselines/fjord/fjord/od/samplers/fixed_od.py
+++ b/baselines/fjord/fjord/od/samplers/fixed_od.py
@@ -1,4 +1,5 @@
 """Ordered Dropout stochastic sampler."""
+
 from collections.abc import Generator
 from typing import List
 
diff --git a/baselines/fjord/fjord/server.py b/baselines/fjord/fjord/server.py
index d25e8f17156a..7c1d10f5752d 100644
--- a/baselines/fjord/fjord/server.py
+++ b/baselines/fjord/fjord/server.py
@@ -1,4 +1,5 @@
 """Global evaluation function."""
+
 from typing import Any, Dict, Optional, Tuple
 
 import flwr as fl
diff --git a/baselines/fjord/fjord/strategy.py b/baselines/fjord/fjord/strategy.py
index d3ec99a419bd..fa2fd6bfa7c0 100644
--- a/baselines/fjord/fjord/strategy.py
+++ b/baselines/fjord/fjord/strategy.py
@@ -1,4 +1,5 @@
 """FjORD strategy."""
+
 from copy import deepcopy
 from functools import reduce
 from typing import Dict, List, Optional, Tuple, Union
diff --git a/baselines/fjord/fjord/utils/logger.py b/baselines/fjord/fjord/utils/logger.py
index b0eb2194bfef..183eff4c0daa 100644
--- a/baselines/fjord/fjord/utils/logger.py
+++ b/baselines/fjord/fjord/utils/logger.py
@@ -1,4 +1,5 @@
 """Logger functionality."""
+
 import logging
 
 import coloredlogs
diff --git a/baselines/fjord/fjord/utils/utils.py b/baselines/fjord/fjord/utils/utils.py
index 3a1a327dd555..16f58e8e8df2 100644
--- a/baselines/fjord/fjord/utils/utils.py
+++ b/baselines/fjord/fjord/utils/utils.py
@@ -1,4 +1,5 @@
 """Utility functions for fjord."""
+
 import os
 from typing import List, Optional, OrderedDict
 
diff --git a/baselines/fjord/pyproject.toml b/baselines/fjord/pyproject.toml
index 197bb53caf89..7d7d04d12928 100644
--- a/baselines/fjord/pyproject.toml
+++ b/baselines/fjord/pyproject.toml
@@ -48,8 +48,8 @@ torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp31
 torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/fjord/setup.py b/baselines/fjord/setup.py
index aa09948c34fc..aa338c0aaea7 100644
--- a/baselines/fjord/setup.py
+++ b/baselines/fjord/setup.py
@@ -1,4 +1,5 @@
 """Setup fjord package."""
+
 from setuptools import find_packages, setup
 
 VERSION = "0.1.0"
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/client.py b/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/client.py
index 509587f9762e..62e44cd92192 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/client.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/client.py
@@ -1,6 +1,5 @@
 """Flower Client for CIFAR10/100."""
 
-
 from collections import OrderedDict
 from pathlib import Path
 from typing import Callable, Dict, Tuple
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/utils.py b/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/utils.py
index 5a53314e5cf0..bd65005628ff 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/utils.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/utils.py
@@ -1,6 +1,5 @@
 """Util functions for CIFAR10/100."""
 
-
 from collections import OrderedDict
 from pathlib import Path
 from typing import Callable, Dict, List, Optional, Tuple
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/main.py b/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/main.py
index a4e29e6ac8a7..cab398a4c76d 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/main.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/main.py
@@ -1,6 +1,5 @@
 """Runs AdaptiveFederated Optimization for CIFAR10/100."""
 
-
 from os import chdir
 from pathlib import Path
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/example_test.py b/baselines/flwr_baselines/flwr_baselines/publications/example_test.py
index f40423c9fae0..8108a1ae0538 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/example_test.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/example_test.py
@@ -3,7 +3,6 @@
 Should be removed when first real code has tests.
 """
 
-
 import unittest
 
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/dataset.py b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/dataset.py
index 4010c3ba679d..e63ad55382e3 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/dataset.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/dataset.py
@@ -1,6 +1,5 @@
 """MNIST dataset utilities for federated learning."""
 
-
 from typing import List, Optional, Tuple
 
 import numpy as np
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/main.py b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/main.py
index 378b3f42816b..f90f608b65f3 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/main.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/main.py
@@ -1,6 +1,5 @@
 """Runs CNN federated learning for MNIST dataset."""
 
-
 from pathlib import Path
 
 import flwr as fl
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/model.py b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/model.py
index ea7864c38057..46e0738cd464 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/model.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/model.py
@@ -1,6 +1,5 @@
 """CNN model architecutre, training and testing functions for MNIST."""
 
-
 from typing import Tuple
 
 import torch
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/data_test.py b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/data_test.py
index 06b2782530f8..4f0e3a8e92eb 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/data_test.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/data_test.py
@@ -1,6 +1,5 @@
 """Used to test the model and the data partitionning."""
 
-
 from flwr_baselines.publications.fedavg_mnist import dataset
 
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/model_test.py b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/model_test.py
index f934a74cda9e..9f33c5db43e3 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/model_test.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/model_test.py
@@ -1,6 +1,5 @@
 """Used to test the model and the data partitionning."""
 
-
 from flwr_baselines.publications.fedavg_mnist import model
 
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/utils.py b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/utils.py
index 3f992f50f202..63d37849bee9 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/utils.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/utils.py
@@ -1,6 +1,5 @@
 """Contains utility functions for CNN FL on MNIST."""
 
-
 from collections import OrderedDict
 from pathlib import Path
 from typing import Callable, Dict, List, Optional, Tuple
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/client.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/client.py
index b67169c1c9bb..3a5450f33e80 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/client.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/client.py
@@ -1,6 +1,5 @@
 """Client implementation for federated learning."""
 
-
 from typing import Dict, List, Tuple
 
 import flwr as fl
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset.py
index 9e22d7326ce7..9427292494bc 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset.py
@@ -1,6 +1,5 @@
 """FEMNIST dataset creation module."""
 
-
 import pathlib
 from logging import INFO
 from typing import List, Tuple
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset_test.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset_test.py
index 4a6d52e92787..6043d77b5fb2 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset_test.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset_test.py
@@ -1,4 +1,5 @@
 """Dataset tests."""
+
 # pylint: disable=no-self-use, protected-access
 import tempfile
 import unittest
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor.py
index e1e71788eff4..39e0d152f6c6 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor.py
@@ -1,6 +1,5 @@
 """Module to preprocess the NIST Special Database 19 into FeMNIST/FEMNIST."""
 
-
 import pathlib
 from logging import DEBUG, WARN
 from typing import Dict, Union
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor_test.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor_test.py
index d13f3d45b8b9..9e5266347f6b 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor_test.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor_test.py
@@ -1,4 +1,5 @@
 """NIST preprocessor test."""
+
 # pylint: disable=no-self-use
 import unittest
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler.py
index 9521353eea82..db3e028ee5bb 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler.py
@@ -1,6 +1,5 @@
 """Module for sampling the reference data of FEMNIST."""
 
-
 import math
 from logging import WARN
 from typing import Optional
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler_test.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler_test.py
index 55970fb85fac..290914b58167 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler_test.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler_test.py
@@ -1,4 +1,5 @@
 """NIST sampler test."""
+
 # pylint: disable=no-self-use, protected-access
 import unittest
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/utils.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/utils.py
index 20ea32cc648e..e55e08c37801 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/utils.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/utils.py
@@ -1,6 +1,5 @@
 """Utilities used for the FEMNIST dataset creation."""
 
-
 import hashlib
 import pathlib
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/zip_downloader.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/zip_downloader.py
index 8aa223fc1d49..687fd7be825b 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/zip_downloader.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/zip_downloader.py
@@ -1,7 +1,6 @@
 """Module for downloading the ZIP files, extracting them and removing the
 downloaded zip file."""
 
-
 import os
 import pathlib
 import shutil
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/main.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/main.py
index 1aa5e18bbb6c..701db72748a9 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/main.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/main.py
@@ -1,6 +1,5 @@
 """Main module for running FEMNIST experiments."""
 
-
 import pathlib
 from functools import partial
 from typing import Type, Union
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model.py
index 318661784b61..4f97450a1975 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model.py
@@ -1,6 +1,5 @@
 """Implementation of the model used for the FEMNIST experiments."""
 
-
 from logging import INFO
 from typing import Optional, Tuple
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model_test.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model_test.py
index fd71ddbc52b7..6d289b36bc98 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model_test.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model_test.py
@@ -1,4 +1,5 @@
 """FEMNIST model test."""
+
 # pylint: disable=no-self-use
 import unittest
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/strategy.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/strategy.py
index c8cb8a4f419d..afa6a5cdd6ad 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/strategy.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/strategy.py
@@ -1,6 +1,5 @@
 """FedAvg with the same clients used for both training and evaluation."""
 
-
 from typing import Callable, Dict, List, Optional, Tuple
 
 from flwr.common import (
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/utils.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/utils.py
index 22d010c9601c..8b09a5eda6c5 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/utils.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/utils.py
@@ -1,6 +1,5 @@
 """Utils used for the FEMNIST project."""
 
-
 from typing import Dict, List, Tuple
 
 import numpy as np
diff --git a/baselines/flwr_baselines/pyproject.toml b/baselines/flwr_baselines/pyproject.toml
index 2d64b55981d7..ad2307568c43 100644
--- a/baselines/flwr_baselines/pyproject.toml
+++ b/baselines/flwr_baselines/pyproject.toml
@@ -53,8 +53,8 @@ pandas = "^1.5.3"
 pyhamcrest = "^2.0.4"
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==0.961"
 pylint = "==2.8.2"
diff --git a/baselines/flwr_baselines/requirements.txt b/baselines/flwr_baselines/requirements.txt
index 8d151e697532..39dceb0641c2 100644
--- a/baselines/flwr_baselines/requirements.txt
+++ b/baselines/flwr_baselines/requirements.txt
@@ -18,8 +18,8 @@ wget >= 3.2
 virtualenv >= 20.24.6
 
 ##### dev-dependencies
-isort == 5.11.5
-black == 23.1.0
+isort == 5.13.2
+black == 24.2.0
 docformatter == 1.5.1
 mypy == 0.961
 pylint == 2.8.2
diff --git a/baselines/heterofl/heterofl/datasets/__init__.py b/baselines/heterofl/heterofl/datasets/__init__.py
index 91251db77302..7a6f06836b69 100644
--- a/baselines/heterofl/heterofl/datasets/__init__.py
+++ b/baselines/heterofl/heterofl/datasets/__init__.py
@@ -2,6 +2,7 @@
 
 The entire datasets module is adopted from authors implementation.
 """
+
 from .cifar import CIFAR10
 from .mnist import MNIST
 from .utils import Compose
diff --git a/baselines/heterofl/heterofl/datasets/cifar.py b/baselines/heterofl/heterofl/datasets/cifar.py
index c75194bc8ee7..fd07135875b2 100644
--- a/baselines/heterofl/heterofl/datasets/cifar.py
+++ b/baselines/heterofl/heterofl/datasets/cifar.py
@@ -1,4 +1,5 @@
 """CIFAR10 dataset class, adopted from authors implementation."""
+
 import os
 import pickle
 
diff --git a/baselines/heterofl/heterofl/datasets/mnist.py b/baselines/heterofl/heterofl/datasets/mnist.py
index feae2ea987b4..1fc744f60bb5 100644
--- a/baselines/heterofl/heterofl/datasets/mnist.py
+++ b/baselines/heterofl/heterofl/datasets/mnist.py
@@ -1,4 +1,5 @@
 """MNIST dataset class, adopted from authors implementation."""
+
 import codecs
 import os
 
diff --git a/baselines/heterofl/heterofl/datasets/utils.py b/baselines/heterofl/heterofl/datasets/utils.py
index 6b71811ed50d..e6cc9f128827 100644
--- a/baselines/heterofl/heterofl/datasets/utils.py
+++ b/baselines/heterofl/heterofl/datasets/utils.py
@@ -2,6 +2,7 @@
 
 Adopted from authors implementation.
 """
+
 import glob
 import gzip
 import hashlib
diff --git a/baselines/heterofl/heterofl/main.py b/baselines/heterofl/heterofl/main.py
index 3973841cb60e..6315c0615873 100644
--- a/baselines/heterofl/heterofl/main.py
+++ b/baselines/heterofl/heterofl/main.py
@@ -1,4 +1,5 @@
 """Runs federated learning for given configuration in base.yaml."""
+
 import pickle
 from pathlib import Path
 
@@ -71,9 +72,11 @@ def main(cfg: DictConfig) -> None:
 
     test_model = models.create_model(
         model_config,
-        model_rate=model_split_rate[get_global_model_rate(model_mode)]
-        if model_split_rate is not None
-        else None,
+        model_rate=(
+            model_split_rate[get_global_model_rate(model_mode)]
+            if model_split_rate is not None
+            else None
+        ),
         track=True,
         device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"),
     )
@@ -122,17 +125,21 @@ def main(cfg: DictConfig) -> None:
         test_model,
         models.create_model(
             model_config,
-            model_rate=model_split_rate[get_global_model_rate(model_mode)]
-            if model_split_rate is not None
-            else None,
+            model_rate=(
+                model_split_rate[get_global_model_rate(model_mode)]
+                if model_split_rate is not None
+                else None
+            ),
             track=False,
             device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"),
         )
         .state_dict()
         .keys(),
-        enable_train_on_train_data=cfg.enable_train_on_train_data_while_testing
-        if "enable_train_on_train_data_while_testing" in cfg
-        else True,
+        enable_train_on_train_data=(
+            cfg.enable_train_on_train_data_while_testing
+            if "enable_train_on_train_data_while_testing" in cfg
+            else True
+        ),
     )
     client_resources = {
         "num_cpus": cfg.client_resources.num_cpus,
@@ -145,15 +152,19 @@ def main(cfg: DictConfig) -> None:
             model_name=cfg.model.model_name,
             net=models.create_model(
                 model_config,
-                model_rate=model_split_rate[get_global_model_rate(model_mode)]
-                if model_split_rate is not None
-                else None,
+                model_rate=(
+                    model_split_rate[get_global_model_rate(model_mode)]
+                    if model_split_rate is not None
+                    else None
+                ),
                 device="cpu",
             ),
             optim_scheduler_settings=optim_scheduler_settings,
-            global_model_rate=model_split_rate[get_global_model_rate(model_mode)]
-            if model_split_rate is not None
-            else 1.0,
+            global_model_rate=(
+                model_split_rate[get_global_model_rate(model_mode)]
+                if model_split_rate is not None
+                else 1.0
+            ),
             evaluate_fn=evaluate_fn,
             min_available_clients=cfg.num_clients,
         )
diff --git a/baselines/heterofl/heterofl/model_properties.py b/baselines/heterofl/heterofl/model_properties.py
index 0739fe4fde22..53b09bc75ff3 100644
--- a/baselines/heterofl/heterofl/model_properties.py
+++ b/baselines/heterofl/heterofl/model_properties.py
@@ -1,4 +1,5 @@
 """Determine number of model parameters, space it requires."""
+
 import numpy as np
 import torch
 import torch.nn as nn
diff --git a/baselines/heterofl/heterofl/models.py b/baselines/heterofl/heterofl/models.py
index 1ba040bbb0b9..02173069bead 100644
--- a/baselines/heterofl/heterofl/models.py
+++ b/baselines/heterofl/heterofl/models.py
@@ -2,6 +2,7 @@
 
 Classes Conv, Block, Resnet18 are adopted from authors implementation.
 """
+
 import copy
 from typing import List, OrderedDict
 
diff --git a/baselines/heterofl/heterofl/server.py b/baselines/heterofl/heterofl/server.py
index f82db0a59fff..a1badd87cc11 100644
--- a/baselines/heterofl/heterofl/server.py
+++ b/baselines/heterofl/heterofl/server.py
@@ -1,4 +1,5 @@
 """Flower Server."""
+
 import time
 from collections import OrderedDict
 from typing import Callable, Dict, Optional, Tuple
diff --git a/baselines/heterofl/heterofl/strategy.py b/baselines/heterofl/heterofl/strategy.py
index 70dbd19594df..500d7880c189 100644
--- a/baselines/heterofl/heterofl/strategy.py
+++ b/baselines/heterofl/heterofl/strategy.py
@@ -1,4 +1,5 @@
 """Flower strategy for HeteroFL."""
+
 import copy
 from collections import OrderedDict
 from typing import Dict, List, Optional, Tuple, Union
diff --git a/baselines/heterofl/heterofl/utils.py b/baselines/heterofl/heterofl/utils.py
index 3bcb7f3d8ea7..2ffe4a021cd9 100644
--- a/baselines/heterofl/heterofl/utils.py
+++ b/baselines/heterofl/heterofl/utils.py
@@ -1,4 +1,5 @@
 """Contains utility functions."""
+
 import errno
 import os
 from pathlib import Path
diff --git a/baselines/heterofl/pyproject.toml b/baselines/heterofl/pyproject.toml
index dc03d2380fcb..b4f7c395415a 100644
--- a/baselines/heterofl/pyproject.toml
+++ b/baselines/heterofl/pyproject.toml
@@ -47,8 +47,8 @@ types-six = "^1.16.21.9"
 tqdm = "4.66.1"
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/hfedxgboost/hfedxgboost/client.py b/baselines/hfedxgboost/hfedxgboost/client.py
index 22435e20415b..990a1cf75a81 100644
--- a/baselines/hfedxgboost/hfedxgboost/client.py
+++ b/baselines/hfedxgboost/hfedxgboost/client.py
@@ -3,6 +3,7 @@
 Please overwrite `flwr.client.NumPyClient` or `flwr.client.Client` and create a function
 to instantiate your client.
 """
+
 from typing import Any, Tuple
 
 import flwr as fl
diff --git a/baselines/hfedxgboost/hfedxgboost/dataset.py b/baselines/hfedxgboost/hfedxgboost/dataset.py
index a03ce2cd59fa..9233df1a3211 100644
--- a/baselines/hfedxgboost/hfedxgboost/dataset.py
+++ b/baselines/hfedxgboost/hfedxgboost/dataset.py
@@ -8,6 +8,7 @@
 `dataset_preparation.py` module. You can use all those functions from functions/methods
 defined here of course.
 """
+
 from typing import List, Optional, Tuple, Union
 
 import torch
diff --git a/baselines/hfedxgboost/hfedxgboost/dataset_preparation.py b/baselines/hfedxgboost/hfedxgboost/dataset_preparation.py
index 3fd3cbfb68fd..31c366e9933a 100644
--- a/baselines/hfedxgboost/hfedxgboost/dataset_preparation.py
+++ b/baselines/hfedxgboost/hfedxgboost/dataset_preparation.py
@@ -6,6 +6,7 @@
 uncomment the lines below and tell us in the README.md (see the "Running the Experiment"
 block) that this file should be executed first.
 """
+
 import bz2
 import os
 import shutil
diff --git a/baselines/hfedxgboost/hfedxgboost/main.py b/baselines/hfedxgboost/hfedxgboost/main.py
index 061e635f024c..4267c0bdd885 100644
--- a/baselines/hfedxgboost/hfedxgboost/main.py
+++ b/baselines/hfedxgboost/hfedxgboost/main.py
@@ -3,6 +3,7 @@
 It includes processioning the dataset, instantiate strategy, specify how the global
 model is going to be evaluated, etc. At the end, this script saves the results.
 """
+
 import functools
 from typing import Dict, Union
 
diff --git a/baselines/hfedxgboost/hfedxgboost/models.py b/baselines/hfedxgboost/hfedxgboost/models.py
index fbfc2d966f69..c4aec1f6acb8 100644
--- a/baselines/hfedxgboost/hfedxgboost/models.py
+++ b/baselines/hfedxgboost/hfedxgboost/models.py
@@ -5,6 +5,7 @@
 config. In this way, swapping your model for  another one can be done without changing
 the python code at all
 """
+
 from collections import OrderedDict
 from typing import Union
 
diff --git a/baselines/hfedxgboost/hfedxgboost/strategy.py b/baselines/hfedxgboost/hfedxgboost/strategy.py
index b8e9c6704603..38247746b695 100644
--- a/baselines/hfedxgboost/hfedxgboost/strategy.py
+++ b/baselines/hfedxgboost/hfedxgboost/strategy.py
@@ -3,6 +3,7 @@
 Needed only when the strategy is not yet implemented in Flower or because you want to
 extend or modify the functionality of an existing strategy.
 """
+
 from logging import WARNING
 from typing import Any, Dict, List, Optional, Tuple, Union
 
diff --git a/baselines/hfedxgboost/hfedxgboost/utils.py b/baselines/hfedxgboost/hfedxgboost/utils.py
index 67450f7b8af7..1772a37eb39b 100644
--- a/baselines/hfedxgboost/hfedxgboost/utils.py
+++ b/baselines/hfedxgboost/hfedxgboost/utils.py
@@ -4,6 +4,7 @@
 example, you may define here things like: loading a model from a checkpoint, saving
 results, plotting.
 """
+
 import csv
 import math
 import os
@@ -315,9 +316,9 @@ def single_tree_preds_from_each_client(
 
     for i, _ in enumerate(temp_trees):
         for j in range(n_estimators_client):
-            preds_from_all_trees_from_all_clients[
-                :, i * n_estimators_client + j
-            ] = single_tree_prediction(temp_trees[i], j, x_train)
+            preds_from_all_trees_from_all_clients[:, i * n_estimators_client + j] = (
+                single_tree_prediction(temp_trees[i], j, x_train)
+            )
 
     preds_from_all_trees_from_all_clients = torch.from_numpy(
         np.expand_dims(preds_from_all_trees_from_all_clients, axis=1)
diff --git a/baselines/hfedxgboost/pyproject.toml b/baselines/hfedxgboost/pyproject.toml
index 11064c90b704..237e920064fb 100644
--- a/baselines/hfedxgboost/pyproject.toml
+++ b/baselines/hfedxgboost/pyproject.toml
@@ -49,8 +49,8 @@ torchvision = "0.14.1"
 wandb = "0.15.12"
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
index 902ccfa8395c..4b33644ca71d 100644
--- a/baselines/moon/moon/main.py
+++ b/baselines/moon/moon/main.py
@@ -3,6 +3,7 @@
 It includes processioning the dataset, instantiate strategy, specify how the global
 model is going to be evaluated, etc. At the end, this script saves the results.
 """
+
 import os
 import random
 import shutil
diff --git a/baselines/moon/moon/models.py b/baselines/moon/moon/models.py
index a323a8e74727..2dea0ae8ea3a 100644
--- a/baselines/moon/moon/models.py
+++ b/baselines/moon/moon/models.py
@@ -6,7 +6,6 @@
 the python code at all
 """
 
-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
diff --git a/baselines/moon/moon/utils.py b/baselines/moon/moon/utils.py
index 4b99a480f77b..377e3575439b 100644
--- a/baselines/moon/moon/utils.py
+++ b/baselines/moon/moon/utils.py
@@ -4,6 +4,7 @@
 example, you may define here things like: loading a model from a checkpoint, saving
 results, plotting.
 """
+
 from pathlib import Path
 from typing import Optional
 
diff --git a/baselines/moon/pyproject.toml b/baselines/moon/pyproject.toml
index f25ca6213dbe..459573dd6923 100644
--- a/baselines/moon/pyproject.toml
+++ b/baselines/moon/pyproject.toml
@@ -46,8 +46,8 @@ torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.12.0%2Bcu116-cp3
 torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.13.0%2Bcu116-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/niid_bench/niid_bench/main.py b/baselines/niid_bench/niid_bench/main.py
index c845925c66ed..ca629a289ed4 100644
--- a/baselines/niid_bench/niid_bench/main.py
+++ b/baselines/niid_bench/niid_bench/main.py
@@ -3,6 +3,7 @@
 It includes processioning the dataset, instantiate strategy, specify how the global
 model is going to be evaluated, etc. At the end, this script saves the results.
 """
+
 import os
 import pickle
 
diff --git a/baselines/niid_bench/niid_bench/server_scaffold.py b/baselines/niid_bench/niid_bench/server_scaffold.py
index b55bc538d081..22ef2e6bc1da 100644
--- a/baselines/niid_bench/niid_bench/server_scaffold.py
+++ b/baselines/niid_bench/niid_bench/server_scaffold.py
@@ -120,9 +120,9 @@ def fit_round(
         )
 
         # Aggregate training results
-        aggregated_result: Tuple[
-            Optional[Parameters], Dict[str, Scalar]
-        ] = self.strategy.aggregate_fit(server_round, results, failures)
+        aggregated_result: Tuple[Optional[Parameters], Dict[str, Scalar]] = (
+            self.strategy.aggregate_fit(server_round, results, failures)
+        )
 
         aggregated_result_arrays_combined = []
         if aggregated_result[0] is not None:
diff --git a/baselines/niid_bench/pyproject.toml b/baselines/niid_bench/pyproject.toml
index 8260ced95788..c2c9baf34296 100644
--- a/baselines/niid_bench/pyproject.toml
+++ b/baselines/niid_bench/pyproject.toml
@@ -45,8 +45,8 @@ torchvision = { url = "https://download.pytorch.org/whl/cu113/torchvision-0.13.1
 tqdm = "4.66.1"
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/tamuna/pyproject.toml b/baselines/tamuna/pyproject.toml
index 28fdaf0f2ca1..6eee0449db4e 100644
--- a/baselines/tamuna/pyproject.toml
+++ b/baselines/tamuna/pyproject.toml
@@ -46,8 +46,8 @@ torchvision = { url = "https://download.pytorch.org/whl/cu118/torchvision-0.15.2
 torchaudio = { url = "https://download.pytorch.org/whl/cu118/torchaudio-2.0.2%2Bcu118-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
+isort = "==5.13.2"
+black = "==24.2.0"
 docformatter = "==1.5.1"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
diff --git a/baselines/tamuna/tamuna/client.py b/baselines/tamuna/tamuna/client.py
index 8b85cf0c4026..a42faaa4610f 100644
--- a/baselines/tamuna/tamuna/client.py
+++ b/baselines/tamuna/tamuna/client.py
@@ -1,4 +1,5 @@
 """Defines the MNIST Flower Client and a function to instantiate it."""
+
 # pylint: disable=too-many-instance-attributes,too-many-arguments
 import copy
 import os
diff --git a/baselines/tamuna/tamuna/dataset.py b/baselines/tamuna/tamuna/dataset.py
index 7208a788e069..920a7b5a5b13 100644
--- a/baselines/tamuna/tamuna/dataset.py
+++ b/baselines/tamuna/tamuna/dataset.py
@@ -1,4 +1,5 @@
 """Load the MNIST dataset into train and test loaders."""
+
 from typing import List, Optional, Tuple
 
 from torch.utils.data import DataLoader
diff --git a/baselines/tamuna/tamuna/main.py b/baselines/tamuna/tamuna/main.py
index f9c4eff4aa72..c47798ec94bb 100644
--- a/baselines/tamuna/tamuna/main.py
+++ b/baselines/tamuna/tamuna/main.py
@@ -1,4 +1,5 @@
 """Run CNN federated learning for MNIST dataset."""
+
 import os
 from typing import Dict, List
 
diff --git a/baselines/tamuna/tamuna/models.py b/baselines/tamuna/tamuna/models.py
index 58d506d86df2..cc151b56daca 100644
--- a/baselines/tamuna/tamuna/models.py
+++ b/baselines/tamuna/tamuna/models.py
@@ -1,4 +1,5 @@
 """CNN model architecture, training, and testing functions for MNIST."""
+
 # pylint: disable=too-many-arguments
 
 import copy
diff --git a/datasets/pyproject.toml b/datasets/pyproject.toml
index 90a98c7c7a29..cadf4d4338af 100644
--- a/datasets/pyproject.toml
+++ b/datasets/pyproject.toml
@@ -60,8 +60,8 @@ soundfile = { version = ">=0.12.1", optional = true }
 librosa = { version = ">=0.10.0.post2", optional = true }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = { version = "==23.3.0", extras = ["jupyter"] }
+isort = "==5.13.2"
+black = { version = "==24.2.0", extras = ["jupyter"] }
 docformatter = "==1.7.1"
 mypy = "==1.4.0"
 pylint = "==2.13.9"
diff --git a/examples/mxnet-from-centralized-to-federated/client.py b/examples/mxnet-from-centralized-to-federated/client.py
index 3a3a9de146ef..bb666a26508e 100644
--- a/examples/mxnet-from-centralized-to-federated/client.py
+++ b/examples/mxnet-from-centralized-to-federated/client.py
@@ -1,6 +1,5 @@
 """Flower client example using MXNet for MNIST classification."""
 
-
 from typing import Dict, List, Tuple
 
 import flwr as fl
diff --git a/examples/mxnet-from-centralized-to-federated/mxnet_mnist.py b/examples/mxnet-from-centralized-to-federated/mxnet_mnist.py
index a53ed018eb48..5cf39da7c9ca 100644
--- a/examples/mxnet-from-centralized-to-federated/mxnet_mnist.py
+++ b/examples/mxnet-from-centralized-to-federated/mxnet_mnist.py
@@ -5,7 +5,6 @@
 https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html
 """
 
-
 from typing import List, Tuple
 import mxnet as mx
 from mxnet import gluon
diff --git a/examples/mxnet-from-centralized-to-federated/server.py b/examples/mxnet-from-centralized-to-federated/server.py
index 29cbce1884d1..871aa4e8ec99 100644
--- a/examples/mxnet-from-centralized-to-federated/server.py
+++ b/examples/mxnet-from-centralized-to-federated/server.py
@@ -1,6 +1,5 @@
 """Flower server example."""
 
-
 import flwr as fl
 
 if __name__ == "__main__":
diff --git a/examples/pytorch-from-centralized-to-federated/client.py b/examples/pytorch-from-centralized-to-federated/client.py
index f89e03bc2053..2a3bbccae2bb 100644
--- a/examples/pytorch-from-centralized-to-federated/client.py
+++ b/examples/pytorch-from-centralized-to-federated/client.py
@@ -1,4 +1,5 @@
 """Flower client example using PyTorch for CIFAR-10 image classification."""
+
 import argparse
 from collections import OrderedDict
 from typing import Dict, List, Tuple
diff --git a/examples/pytorch-from-centralized-to-federated/server.py b/examples/pytorch-from-centralized-to-federated/server.py
index 42f34b3a78e9..5190d690dc20 100644
--- a/examples/pytorch-from-centralized-to-federated/server.py
+++ b/examples/pytorch-from-centralized-to-federated/server.py
@@ -1,6 +1,5 @@
 """Flower server example."""
 
-
 from typing import List, Tuple
 
 import flwr as fl
diff --git a/examples/quickstart-jax/client.py b/examples/quickstart-jax/client.py
index afd6f197bcde..2257a3d6daa3 100644
--- a/examples/quickstart-jax/client.py
+++ b/examples/quickstart-jax/client.py
@@ -1,6 +1,5 @@
 """Flower client example using JAX for linear regression."""
 
-
 from typing import Dict, List, Tuple, Callable
 
 import flwr as fl
diff --git a/examples/quickstart-jax/jax_training.py b/examples/quickstart-jax/jax_training.py
index 2b523a08516e..a2e23a0927bc 100644
--- a/examples/quickstart-jax/jax_training.py
+++ b/examples/quickstart-jax/jax_training.py
@@ -7,7 +7,6 @@
 please read the JAX documentation or the mentioned tutorial.
 """
 
-
 from typing import Dict, List, Tuple, Callable
 import jax
 import jax.numpy as jnp
diff --git a/examples/quickstart-mxnet/client.py b/examples/quickstart-mxnet/client.py
index b0c937f7350f..6c2b2e99775d 100644
--- a/examples/quickstart-mxnet/client.py
+++ b/examples/quickstart-mxnet/client.py
@@ -5,7 +5,6 @@
 https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html
 """
 
-
 import flwr as fl
 import numpy as np
 import mxnet as mx
diff --git a/examples/quickstart-mxnet/server.py b/examples/quickstart-mxnet/server.py
index 29cbce1884d1..871aa4e8ec99 100644
--- a/examples/quickstart-mxnet/server.py
+++ b/examples/quickstart-mxnet/server.py
@@ -1,6 +1,5 @@
 """Flower server example."""
 
-
 import flwr as fl
 
 if __name__ == "__main__":
diff --git a/examples/xgboost-comprehensive/server.py b/examples/xgboost-comprehensive/server.py
index c6986dc63ce4..2fecbcc65853 100644
--- a/examples/xgboost-comprehensive/server.py
+++ b/examples/xgboost-comprehensive/server.py
@@ -52,9 +52,9 @@
         fraction_evaluate=1.0 if not centralised_eval else 0.0,
         on_evaluate_config_fn=eval_config,
         on_fit_config_fn=fit_config,
-        evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation
-        if not centralised_eval
-        else None,
+        evaluate_metrics_aggregation_fn=(
+            evaluate_metrics_aggregation if not centralised_eval else None
+        ),
     )
 else:
     # Cyclic training
diff --git a/examples/xgboost-comprehensive/sim.py b/examples/xgboost-comprehensive/sim.py
index ec05b566dd95..ae2a9239a493 100644
--- a/examples/xgboost-comprehensive/sim.py
+++ b/examples/xgboost-comprehensive/sim.py
@@ -124,21 +124,21 @@ def main():
     if args.train_method == "bagging":
         # Bagging training
         strategy = FedXgbBagging(
-            evaluate_function=get_evaluate_fn(test_dmatrix)
-            if args.centralised_eval
-            else None,
+            evaluate_function=(
+                get_evaluate_fn(test_dmatrix) if args.centralised_eval else None
+            ),
             fraction_fit=(float(args.num_clients_per_round) / args.pool_size),
             min_fit_clients=args.num_clients_per_round,
             min_available_clients=args.pool_size,
-            min_evaluate_clients=args.num_evaluate_clients
-            if not args.centralised_eval
-            else 0,
+            min_evaluate_clients=(
+                args.num_evaluate_clients if not args.centralised_eval else 0
+            ),
             fraction_evaluate=1.0 if not args.centralised_eval else 0.0,
             on_evaluate_config_fn=eval_config,
             on_fit_config_fn=fit_config,
-            evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation
-            if not args.centralised_eval
-            else None,
+            evaluate_metrics_aggregation_fn=(
+                evaluate_metrics_aggregation if not args.centralised_eval else None
+            ),
         )
     else:
         # Cyclic training
diff --git a/pyproject.toml b/pyproject.toml
index 3912a8410a74..6bd5c74f29a9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -88,7 +88,7 @@ types-requests = "==2.31.0.20240125"
 types-setuptools = "==69.0.0.20240125"
 clang-format = "==17.0.6"
 isort = "==5.13.2"
-black = { version = "==23.10.1", extras = ["jupyter"] }
+black = { version = "==24.2.0", extras = ["jupyter"] }
 docformatter = "==1.7.5"
 mypy = "==1.8.0"
 pylint = "==3.0.3"
diff --git a/src/py/flwr/client/app.py b/src/py/flwr/client/app.py
index 86e792d784e7..6c3c3a99680d 100644
--- a/src/py/flwr/client/app.py
+++ b/src/py/flwr/client/app.py
@@ -506,9 +506,7 @@ def start_numpy_client(
     )
 
 
-def _init_connection(
-    transport: Optional[str], server_address: str
-) -> Tuple[
+def _init_connection(transport: Optional[str], server_address: str) -> Tuple[
     Callable[
         [str, bool, int, Union[bytes, str, None]],
         ContextManager[
diff --git a/src/py/flwr/common/version.py b/src/py/flwr/common/version.py
index 9545b4b68073..6808c66606b1 100644
--- a/src/py/flwr/common/version.py
+++ b/src/py/flwr/common/version.py
@@ -1,6 +1,5 @@
 """Flower package version helper."""
 
-
 import importlib.metadata as importlib_metadata
 from typing import Tuple
 
diff --git a/src/py/flwr/server/strategy/dpfedavg_fixed.py b/src/py/flwr/server/strategy/dpfedavg_fixed.py
index b182ac26cef8..c54379fc7087 100644
--- a/src/py/flwr/server/strategy/dpfedavg_fixed.py
+++ b/src/py/flwr/server/strategy/dpfedavg_fixed.py
@@ -104,9 +104,9 @@ def configure_fit(
         """
         additional_config = {"dpfedavg_clip_norm": self.clip_norm}
         if not self.server_side_noising:
-            additional_config[
-                "dpfedavg_noise_stddev"
-            ] = self._calc_client_noise_stddev()
+            additional_config["dpfedavg_noise_stddev"] = (
+                self._calc_client_noise_stddev()
+            )
 
         client_instructions = self.strategy.configure_fit(
             server_round, parameters, client_manager
diff --git a/src/py/flwr_experimental/baseline/tf_cifar/settings.py b/src/py/flwr_experimental/baseline/tf_cifar/settings.py
index 829856af0ace..ed1a72cafac9 100644
--- a/src/py/flwr_experimental/baseline/tf_cifar/settings.py
+++ b/src/py/flwr_experimental/baseline/tf_cifar/settings.py
@@ -120,9 +120,9 @@ def configure_clients(
             cid=str(i),
             partition=i,
             # Indices 0 to 49 fast, 50 to 99 slow
-            delay_factor=delay_factor_fast
-            if i < int(num_clients / 2)
-            else delay_factor_slow,
+            delay_factor=(
+                delay_factor_fast if i < int(num_clients / 2) else delay_factor_slow
+            ),
             # Shared
             iid_fraction=iid_fraction,
             num_clients=num_clients,
diff --git a/src/py/flwr_experimental/baseline/tf_fashion_mnist/settings.py b/src/py/flwr_experimental/baseline/tf_fashion_mnist/settings.py
index b0de9841de30..72adc1f0be04 100644
--- a/src/py/flwr_experimental/baseline/tf_fashion_mnist/settings.py
+++ b/src/py/flwr_experimental/baseline/tf_fashion_mnist/settings.py
@@ -141,9 +141,9 @@ def configure_clients(
             cid=str(i),
             partition=i,
             # Indices 0 to 49 fast, 50 to 99 slow
-            delay_factor=delay_factor_fast
-            if i < int(num_clients / 2)
-            else delay_factor_slow,
+            delay_factor=(
+                delay_factor_fast if i < int(num_clients / 2) else delay_factor_slow
+            ),
             # Shared
             iid_fraction=iid_fraction,
             num_clients=num_clients,
diff --git a/src/py/flwr_experimental/baseline/tf_hotkey/settings.py b/src/py/flwr_experimental/baseline/tf_hotkey/settings.py
index 18c58d1333a5..5bfb7b1e42ad 100644
--- a/src/py/flwr_experimental/baseline/tf_hotkey/settings.py
+++ b/src/py/flwr_experimental/baseline/tf_hotkey/settings.py
@@ -144,9 +144,9 @@ def configure_clients(
             cid=str(i),
             partition=i,
             # Indices 0 to 49 fast, 50 to 99 slow
-            delay_factor=delay_factor_fast
-            if i < int(num_clients / 2)
-            else delay_factor_slow,
+            delay_factor=(
+                delay_factor_fast if i < int(num_clients / 2) else delay_factor_slow
+            ),
             # Shared
             iid_fraction=iid_fraction,
             num_clients=num_clients,
diff --git a/src/py/flwr_experimental/logserver/server.py b/src/py/flwr_experimental/logserver/server.py
index dc729cebbf33..683b12b6db6c 100644
--- a/src/py/flwr_experimental/logserver/server.py
+++ b/src/py/flwr_experimental/logserver/server.py
@@ -69,9 +69,9 @@ def upload_file(local_filepath: str, s3_key: Optional[str]) -> None:
                 Bucket=CONFIG["s3_bucket"],
                 Key=s3_key,
                 ExtraArgs={
-                    "ContentType": "application/pdf"
-                    if s3_key.endswith(".pdf")
-                    else "text/plain"
+                    "ContentType": (
+                        "application/pdf" if s3_key.endswith(".pdf") else "text/plain"
+                    )
                 },
             )
         # pylint: disable=broad-except
diff --git a/src/py/flwr_experimental/ops/cluster.py b/src/py/flwr_experimental/ops/cluster.py
index dfd0dd13727e..692704676f4a 100644
--- a/src/py/flwr_experimental/ops/cluster.py
+++ b/src/py/flwr_experimental/ops/cluster.py
@@ -73,10 +73,10 @@ def ssh_connection(
     username, key_filename = ssh_credentials
 
     instance_ssh_port: int = cast(int, instance.ssh_port)
-    ignore_host_key_policy: Union[
-        Type[MissingHostKeyPolicy], MissingHostKeyPolicy
-    ] = cast(
-        Union[Type[MissingHostKeyPolicy], MissingHostKeyPolicy], IgnoreHostKeyPolicy
+    ignore_host_key_policy: Union[Type[MissingHostKeyPolicy], MissingHostKeyPolicy] = (
+        cast(
+            Union[Type[MissingHostKeyPolicy], MissingHostKeyPolicy], IgnoreHostKeyPolicy
+        )
     )
 
     client = SSHClient()

From 9260ed82b9219b82c1307034f17a7262c69ad867 Mon Sep 17 00:00:00 2001
From: Taner Topal <taner@flower.dev>
Date: Thu, 22 Feb 2024 19:38:01 +0100
Subject: [PATCH 046/102] Update docformatter to latest version (#2955)

---
 baselines/baseline_template/pyproject.toml                | 2 +-
 baselines/dasha/pyproject.toml                            | 2 +-
 baselines/depthfl/pyproject.toml                          | 2 +-
 baselines/fedavgm/pyproject.toml                          | 2 +-
 baselines/fedbn/pyproject.toml                            | 2 +-
 baselines/fedmeta/pyproject.toml                          | 2 +-
 baselines/fedmlb/pyproject.toml                           | 2 +-
 baselines/fednova/pyproject.toml                          | 2 +-
 baselines/fedpara/pyproject.toml                          | 2 +-
 baselines/fedper/pyproject.toml                           | 2 +-
 baselines/fedprox/pyproject.toml                          | 2 +-
 baselines/fedvssl/pyproject.toml                          | 2 +-
 baselines/fedwav2vec2/pyproject.toml                      | 2 +-
 baselines/fjord/pyproject.toml                            | 2 +-
 .../publications/leaf/femnist/dataset/dataset.py          | 8 ++++----
 .../leaf/femnist/dataset/nist_preprocessor.py             | 8 +++++---
 .../flwr_baselines/publications/leaf/femnist/model.py     | 5 +++--
 .../flwr_baselines/publications/leaf/femnist/utils.py     | 4 ++--
 baselines/flwr_baselines/pyproject.toml                   | 2 +-
 baselines/flwr_baselines/requirements.txt                 | 2 +-
 baselines/heterofl/pyproject.toml                         | 2 +-
 baselines/hfedxgboost/pyproject.toml                      | 2 +-
 baselines/moon/pyproject.toml                             | 2 +-
 baselines/niid_bench/pyproject.toml                       | 2 +-
 baselines/tamuna/pyproject.toml                           | 2 +-
 datasets/pyproject.toml                                   | 2 +-
 26 files changed, 36 insertions(+), 33 deletions(-)

diff --git a/baselines/baseline_template/pyproject.toml b/baselines/baseline_template/pyproject.toml
index bb1b48357944..31f1ee7bfe6d 100644
--- a/baselines/baseline_template/pyproject.toml
+++ b/baselines/baseline_template/pyproject.toml
@@ -44,7 +44,7 @@ hydra-core = "1.3.2" # don't change this
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/dasha/pyproject.toml b/baselines/dasha/pyproject.toml
index 135a797c0d3d..f03ad06e26e4 100644
--- a/baselines/dasha/pyproject.toml
+++ b/baselines/dasha/pyproject.toml
@@ -51,7 +51,7 @@ torchvision = [{ url = "https://download.pytorch.org/whl/cu118/torchvision-0.15.
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/depthfl/pyproject.toml b/baselines/depthfl/pyproject.toml
index 14e392478330..e59d16fef88e 100644
--- a/baselines/depthfl/pyproject.toml
+++ b/baselines/depthfl/pyproject.toml
@@ -47,7 +47,7 @@ torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.14.1
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/fedavgm/pyproject.toml b/baselines/fedavgm/pyproject.toml
index c0409d48e5a3..d222baa65b0e 100644
--- a/baselines/fedavgm/pyproject.toml
+++ b/baselines/fedavgm/pyproject.toml
@@ -48,7 +48,7 @@ matplotlib = "^3.7.2"
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/fedbn/pyproject.toml b/baselines/fedbn/pyproject.toml
index f8c795921ed1..97087da700a7 100644
--- a/baselines/fedbn/pyproject.toml
+++ b/baselines/fedbn/pyproject.toml
@@ -48,7 +48,7 @@ tqdm = "^4.66.1"
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/fedmeta/pyproject.toml b/baselines/fedmeta/pyproject.toml
index 515953ff38f7..77279e5e8a5b 100644
--- a/baselines/fedmeta/pyproject.toml
+++ b/baselines/fedmeta/pyproject.toml
@@ -49,7 +49,7 @@ pillow = "9.5.0" # needed <10.0.0 for LEAF repo scripts
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/fedmlb/pyproject.toml b/baselines/fedmlb/pyproject.toml
index 21ada9a4585e..47d115d48834 100644
--- a/baselines/fedmlb/pyproject.toml
+++ b/baselines/fedmlb/pyproject.toml
@@ -47,7 +47,7 @@ psutil = "5.9.5" # this is just to log some info about memory usage
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/fednova/pyproject.toml b/baselines/fednova/pyproject.toml
index 9b3a0d99d581..69c530e83926 100644
--- a/baselines/fednova/pyproject.toml
+++ b/baselines/fednova/pyproject.toml
@@ -50,7 +50,7 @@ pandas = "1.3.5"
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/fedpara/pyproject.toml b/baselines/fedpara/pyproject.toml
index 3e5b6495b88a..be6b3301872a 100644
--- a/baselines/fedpara/pyproject.toml
+++ b/baselines/fedpara/pyproject.toml
@@ -48,7 +48,7 @@ torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/fedper/pyproject.toml b/baselines/fedper/pyproject.toml
index 4ee7ad0f02d6..634ea2898f91 100644
--- a/baselines/fedper/pyproject.toml
+++ b/baselines/fedper/pyproject.toml
@@ -49,7 +49,7 @@ torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/fedprox/pyproject.toml b/baselines/fedprox/pyproject.toml
index ad31dccc11d1..1fe9fe4b6cad 100644
--- a/baselines/fedprox/pyproject.toml
+++ b/baselines/fedprox/pyproject.toml
@@ -49,7 +49,7 @@ torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/fedvssl/pyproject.toml b/baselines/fedvssl/pyproject.toml
index 43128ad59f1d..293388079802 100644
--- a/baselines/fedvssl/pyproject.toml
+++ b/baselines/fedvssl/pyproject.toml
@@ -51,7 +51,7 @@ mmcv-full = { url = "https://download.openmmlab.com/mmcv/dist/cu117/torch1.13.0/
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/fedwav2vec2/pyproject.toml b/baselines/fedwav2vec2/pyproject.toml
index 406fc6083533..d9b0c8090cd8 100644
--- a/baselines/fedwav2vec2/pyproject.toml
+++ b/baselines/fedwav2vec2/pyproject.toml
@@ -49,7 +49,7 @@ transformers = "4.33.2"
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/fjord/pyproject.toml b/baselines/fjord/pyproject.toml
index 7d7d04d12928..86eab6b12dd9 100644
--- a/baselines/fjord/pyproject.toml
+++ b/baselines/fjord/pyproject.toml
@@ -50,7 +50,7 @@ torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset.py
index 9427292494bc..ca2c5868efca 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset.py
@@ -68,8 +68,8 @@ def create_dataset(df_info: pd.DataFrame, labels: np.ndarray) -> NISTLikeDataset
 
 
 def create_partition_list(df_info: pd.DataFrame) -> List[List[int]]:
-    """
-    Create list of list with int masks identifying writers.
+    """Create list of list with int masks identifying writers.
+
     Parameters
     ----------
     df_info: pd.DataFrame
@@ -173,8 +173,8 @@ def train_valid_test_partition(
 def transform_datasets_into_dataloaders(
     datasets: List[Dataset], **dataloader_kwargs
 ) -> List[DataLoader]:
-    """
-    Transform datasets into dataloaders.
+    """Transform datasets into dataloaders.
+
     Parameters
     ----------
     datasets: List[Dataset]
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor.py
index 39e0d152f6c6..9143f6720758 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor.py
@@ -20,10 +20,12 @@ class NISTPreprocessor:
     """Preprocess NIST images from two directories divided by class and by
     writer.
 
-    The preprocessing procedure include different step for FEMNIST and FeMNIST.
+    The preprocessing procedure include different step for FEMNIST and
+    FeMNIST.
 
-    Using two datasets (by_class and by_write, actually one dataset but divided differently) is
-    required to merge information about the writer_id and label.
+    Using two datasets (by_class and by_write, actually one dataset but
+    divided differently) is required to merge information about the
+    writer_id and label.
     """
 
     def __init__(
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model.py
index 4f97450a1975..678dce843f62 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model.py
@@ -53,8 +53,9 @@ def train(
     n_batches: Optional[int] = None,
     verbose: bool = False,
 ) -> Tuple[float, float, Optional[float], Optional[float]]:
-    """Train a given model with CrossEntropy and SGD (or some version of it
-    like batch-SGD).
+    """Train a given model with CrossEntropy and SGD.
+
+    Alternatively, some version of it, like batch-SGD
 
     n_batches is an alternative way of specifying the training length
     (instead of epochs)
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/utils.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/utils.py
index 8b09a5eda6c5..4ce95743599e 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/utils.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/utils.py
@@ -39,8 +39,8 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Dict[str, Scalar]:
 
 
 def setup_seed(seed: int):
-    """
-    Set up seed for numpy and torch.
+    """Set up seed for numpy and torch.
+
     Parameters
     ----------
     seed: int
diff --git a/baselines/flwr_baselines/pyproject.toml b/baselines/flwr_baselines/pyproject.toml
index ad2307568c43..f0b2ac84e66e 100644
--- a/baselines/flwr_baselines/pyproject.toml
+++ b/baselines/flwr_baselines/pyproject.toml
@@ -55,7 +55,7 @@ pyhamcrest = "^2.0.4"
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==0.961"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/flwr_baselines/requirements.txt b/baselines/flwr_baselines/requirements.txt
index 39dceb0641c2..50655dd5a4fe 100644
--- a/baselines/flwr_baselines/requirements.txt
+++ b/baselines/flwr_baselines/requirements.txt
@@ -20,7 +20,7 @@ virtualenv >= 20.24.6
 ##### dev-dependencies
 isort == 5.13.2
 black == 24.2.0
-docformatter == 1.5.1
+docformatter == 1.7.5
 mypy == 0.961
 pylint == 2.8.2
 flake8 == 3.9.2
diff --git a/baselines/heterofl/pyproject.toml b/baselines/heterofl/pyproject.toml
index b4f7c395415a..4a5233785f78 100644
--- a/baselines/heterofl/pyproject.toml
+++ b/baselines/heterofl/pyproject.toml
@@ -49,7 +49,7 @@ tqdm = "4.66.1"
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/hfedxgboost/pyproject.toml b/baselines/hfedxgboost/pyproject.toml
index 237e920064fb..9e4c5e95a527 100644
--- a/baselines/hfedxgboost/pyproject.toml
+++ b/baselines/hfedxgboost/pyproject.toml
@@ -51,7 +51,7 @@ wandb = "0.15.12"
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/moon/pyproject.toml b/baselines/moon/pyproject.toml
index 459573dd6923..6dba034e5c82 100644
--- a/baselines/moon/pyproject.toml
+++ b/baselines/moon/pyproject.toml
@@ -48,7 +48,7 @@ torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.13.0
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/niid_bench/pyproject.toml b/baselines/niid_bench/pyproject.toml
index c2c9baf34296..a5849d955452 100644
--- a/baselines/niid_bench/pyproject.toml
+++ b/baselines/niid_bench/pyproject.toml
@@ -47,7 +47,7 @@ tqdm = "4.66.1"
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/baselines/tamuna/pyproject.toml b/baselines/tamuna/pyproject.toml
index 6eee0449db4e..22a6e499aa1e 100644
--- a/baselines/tamuna/pyproject.toml
+++ b/baselines/tamuna/pyproject.toml
@@ -48,7 +48,7 @@ torchaudio = { url = "https://download.pytorch.org/whl/cu118/torchaudio-2.0.2%2B
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = "==24.2.0"
-docformatter = "==1.5.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
diff --git a/datasets/pyproject.toml b/datasets/pyproject.toml
index cadf4d4338af..0943814ed611 100644
--- a/datasets/pyproject.toml
+++ b/datasets/pyproject.toml
@@ -62,7 +62,7 @@ librosa = { version = ">=0.10.0.post2", optional = true }
 [tool.poetry.dev-dependencies]
 isort = "==5.13.2"
 black = { version = "==24.2.0", extras = ["jupyter"] }
-docformatter = "==1.7.1"
+docformatter = "==1.7.5"
 mypy = "==1.4.0"
 pylint = "==2.13.9"
 flake8 = "==3.9.2"

From ad985f6f2358be1e867967e58557a5846326848a Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Thu, 22 Feb 2024 20:54:14 +0100
Subject: [PATCH 047/102] Create `VCE` `Fleet API` entry point (#2993)

---
 src/py/flwr/common/constant.py                |  2 +
 src/py/flwr/server/app.py                     | 73 +++++++++++++++++++
 .../server/superlink/fleet/vce/__init__.py    | 21 ++++++
 .../server/superlink/fleet/vce/vce_api.py     | 71 ++++++++++++++++++
 4 files changed, 167 insertions(+)
 create mode 100644 src/py/flwr/server/superlink/fleet/vce/__init__.py
 create mode 100644 src/py/flwr/server/superlink/fleet/vce/vce_api.py

diff --git a/src/py/flwr/common/constant.py b/src/py/flwr/common/constant.py
index 811fff73f06d..2946a594e68c 100644
--- a/src/py/flwr/common/constant.py
+++ b/src/py/flwr/common/constant.py
@@ -28,10 +28,12 @@
 TRANSPORT_TYPE_GRPC_BIDI = "grpc-bidi"
 TRANSPORT_TYPE_GRPC_RERE = "grpc-rere"
 TRANSPORT_TYPE_REST = "rest"
+TRANSPORT_TYPE_VCE = "vce"
 TRANSPORT_TYPES = [
     TRANSPORT_TYPE_GRPC_BIDI,
     TRANSPORT_TYPE_GRPC_RERE,
     TRANSPORT_TYPE_REST,
+    TRANSPORT_TYPE_VCE,
 ]
 
 MESSAGE_TYPE_GET_PROPERTIES = "get_properties"
diff --git a/src/py/flwr/server/app.py b/src/py/flwr/server/app.py
index dbbf63b0fe5e..c8cdef9ff323 100644
--- a/src/py/flwr/server/app.py
+++ b/src/py/flwr/server/app.py
@@ -34,6 +34,7 @@
     MISSING_EXTRA_REST,
     TRANSPORT_TYPE_GRPC_RERE,
     TRANSPORT_TYPE_REST,
+    TRANSPORT_TYPE_VCE,
 )
 from flwr.common.logger import log
 from flwr.proto.driver_pb2_grpc import (  # pylint: disable=E0611
@@ -54,6 +55,7 @@
     start_grpc_server,
 )
 from .superlink.fleet.grpc_rere.fleet_servicer import FleetServicer
+from .superlink.fleet.vce import start_vce
 from .superlink.state import StateFactory
 
 ADDRESS_DRIVER_API = "0.0.0.0:9091"
@@ -401,6 +403,15 @@ def run_superlink() -> None:
             certificates=certificates,
         )
         grpc_servers.append(fleet_server)
+    elif args.fleet_api_type == TRANSPORT_TYPE_VCE:
+        _run_fleet_api_vce(
+            num_supernodes=args.num_supernodes,
+            client_app_module_name=args.client_app,
+            backend_name=args.backend,
+            backend_config_json_stream=args.backend_config,
+            working_dir=args.dir,
+            state_factory=state_factory,
+        )
     else:
         raise ValueError(f"Unknown fleet_api_type: {args.fleet_api_type}")
 
@@ -537,6 +548,27 @@ def _run_fleet_api_grpc_rere(
     return fleet_grpc_server
 
 
+# pylint: disable=too-many-arguments
+def _run_fleet_api_vce(
+    num_supernodes: int,
+    client_app_module_name: str,
+    backend_name: str,
+    backend_config_json_stream: str,
+    working_dir: str,
+    state_factory: StateFactory,
+) -> None:
+    log(INFO, "Flower VCE: Starting Fleet API (VirtualClientEngine)")
+
+    start_vce(
+        num_supernodes=num_supernodes,
+        client_app_module_name=client_app_module_name,
+        backend_name=backend_name,
+        backend_config_json_stream=backend_config_json_stream,
+        state_factory=state_factory,
+        working_dir=working_dir,
+    )
+
+
 # pylint: disable=import-outside-toplevel,too-many-arguments
 def _run_fleet_api_rest(
     host: str,
@@ -714,6 +746,14 @@ def _add_args_fleet_api(parser: argparse.ArgumentParser) -> None:
         help="Start a Fleet API server (REST, experimental)",
     )
 
+    ex_group.add_argument(
+        "--vce",
+        action="store_const",
+        dest="fleet_api_type",
+        const=TRANSPORT_TYPE_VCE,
+        help="Start a Fleet API server (VirtualClientEngine)",
+    )
+
     # Fleet API gRPC-rere options
     grpc_rere_group = parser.add_argument_group(
         "Fleet API (gRPC-rere) server options", ""
@@ -749,3 +789,36 @@ def _add_args_fleet_api(parser: argparse.ArgumentParser) -> None:
         type=int,
         default=1,
     )
+
+    # Fleet API VCE options
+    vce_group = parser.add_argument_group("Fleet API (VCE) server options", "")
+    vce_group.add_argument(
+        "--client-app",
+        help="For example: `client:app` or `project.package.module:wrapper.app`.",
+    )
+    vce_group.add_argument(
+        "--num-supernodes",
+        type=int,
+        help="Number of simulated SuperNodes.",
+    )
+    vce_group.add_argument(
+        "--backend",
+        default="ray",
+        type=str,
+        help="Simulation backend that executes the ClientApp.",
+    )
+    vce_group.add_argument(
+        "--backend-config",
+        type=str,
+        default='{"client_resources": {"num_cpus":1, "num_gpus":0.0}, "tensorflow": 0}',
+        help='A JSON formatted stream, e.g \'{"<keyA>":<value>, "<keyB>":<value>}\' to '
+        "configure a backend. Values supported in <value> are those included by "
+        "`flwr.common.typing.ConfigsRecordValues`. ",
+    )
+    parser.add_argument(
+        "--dir",
+        default="",
+        help="Add specified directory to the PYTHONPATH and load"
+        "ClientApp from there."
+        " Default: current working directory.",
+    )
diff --git a/src/py/flwr/server/superlink/fleet/vce/__init__.py b/src/py/flwr/server/superlink/fleet/vce/__init__.py
new file mode 100644
index 000000000000..72cd76f73761
--- /dev/null
+++ b/src/py/flwr/server/superlink/fleet/vce/__init__.py
@@ -0,0 +1,21 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Fleet VirtualClientEngine side."""
+
+from .vce_api import start_vce
+
+__all__ = [
+    "start_vce",
+]
diff --git a/src/py/flwr/server/superlink/fleet/vce/vce_api.py b/src/py/flwr/server/superlink/fleet/vce/vce_api.py
new file mode 100644
index 000000000000..8c76b401b915
--- /dev/null
+++ b/src/py/flwr/server/superlink/fleet/vce/vce_api.py
@@ -0,0 +1,71 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Fleet VirtualClientEngine API."""
+
+import json
+from logging import INFO
+from typing import Dict
+
+from flwr.client.clientapp import ClientApp, load_client_app
+from flwr.client.node_state import NodeState
+from flwr.common.logger import log
+from flwr.server.superlink.state import StateFactory
+
+NodeToPartitionMapping = Dict[int, int]
+
+
+def _register_nodes(
+    num_nodes: int, state_factory: StateFactory
+) -> NodeToPartitionMapping:
+    """Register nodes with the StateFactory and create node-id:partition-id mapping."""
+    nodes_mapping: NodeToPartitionMapping = {}
+    state = state_factory.state()
+    for i in range(num_nodes):
+        node_id = state.create_node()
+        nodes_mapping[node_id] = i
+    log(INFO, "Registered %i nodes", len(nodes_mapping))
+    return nodes_mapping
+
+
+# pylint: disable=too-many-arguments,unused-argument
+def start_vce(
+    num_supernodes: int,
+    client_app_module_name: str,
+    backend_name: str,
+    backend_config_json_stream: str,
+    state_factory: StateFactory,
+    working_dir: str,
+) -> None:
+    """Start Fleet API with the VirtualClientEngine (VCE)."""
+    # Register SuperNodes
+    nodes_mapping = _register_nodes(
+        num_nodes=num_supernodes, state_factory=state_factory
+    )
+
+    # Construct mapping of NodeStates
+    node_states: Dict[int, NodeState] = {}
+    for node_id in nodes_mapping:
+        node_states[node_id] = NodeState()
+
+    # Load backend config
+    _ = json.loads(backend_config_json_stream)
+
+    log(INFO, "client_app_str = %s", client_app_module_name)
+
+    def _load() -> ClientApp:
+        app: ClientApp = load_client_app(client_app_module_name)
+        return app
+
+    # start backend

From 88f830739cb46e15ebafd689045fa5da7da17fbd Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Thu, 22 Feb 2024 22:11:56 +0100
Subject: [PATCH 048/102] Move server functions to `server.py` (#3000)

---
 src/py/flwr/server/app.py        | 47 ++------------------------------
 src/py/flwr/server/compat/app.py |  3 +-
 src/py/flwr/server/server.py     | 47 ++++++++++++++++++++++++++++++--
 src/py/flwr/simulation/app.py    |  3 +-
 4 files changed, 50 insertions(+), 50 deletions(-)

diff --git a/src/py/flwr/server/app.py b/src/py/flwr/server/app.py
index c8cdef9ff323..ac7a8339b31d 100644
--- a/src/py/flwr/server/app.py
+++ b/src/py/flwr/server/app.py
@@ -44,11 +44,11 @@
     add_FleetServicer_to_server,
 )
 
-from .client_manager import ClientManager, SimpleClientManager
+from .client_manager import ClientManager
 from .history import History
-from .server import Server
+from .server import Server, init_defaults, run_fl
 from .server_config import ServerConfig
-from .strategy import FedAvg, Strategy
+from .strategy import Strategy
 from .superlink.driver.driver_servicer import DriverServicer
 from .superlink.fleet.grpc_bidi.grpc_server import (
     generic_create_grpc_server,
@@ -185,47 +185,6 @@ def start_server(  # pylint: disable=too-many-arguments,too-many-locals
     return hist
 
 
-def init_defaults(
-    server: Optional[Server],
-    config: Optional[ServerConfig],
-    strategy: Optional[Strategy],
-    client_manager: Optional[ClientManager],
-) -> Tuple[Server, ServerConfig]:
-    """Create server instance if none was given."""
-    if server is None:
-        if client_manager is None:
-            client_manager = SimpleClientManager()
-        if strategy is None:
-            strategy = FedAvg()
-        server = Server(client_manager=client_manager, strategy=strategy)
-    elif strategy is not None:
-        log(WARN, "Both server and strategy were provided, ignoring strategy")
-
-    # Set default config values
-    if config is None:
-        config = ServerConfig()
-
-    return server, config
-
-
-def run_fl(
-    server: Server,
-    config: ServerConfig,
-) -> History:
-    """Train a model on the given server and return the History object."""
-    hist = server.fit(num_rounds=config.num_rounds, timeout=config.round_timeout)
-    log(INFO, "app_fit: losses_distributed %s", str(hist.losses_distributed))
-    log(INFO, "app_fit: metrics_distributed_fit %s", str(hist.metrics_distributed_fit))
-    log(INFO, "app_fit: metrics_distributed %s", str(hist.metrics_distributed))
-    log(INFO, "app_fit: losses_centralized %s", str(hist.losses_centralized))
-    log(INFO, "app_fit: metrics_centralized %s", str(hist.metrics_centralized))
-
-    # Graceful shutdown
-    server.disconnect_all_clients(timeout=config.round_timeout)
-
-    return hist
-
-
 def run_driver_api() -> None:
     """Run Flower server (Driver API)."""
     log(INFO, "Starting Flower server (Driver API)")
diff --git a/src/py/flwr/server/compat/app.py b/src/py/flwr/server/compat/app.py
index c0255391b885..3df779ebf990 100644
--- a/src/py/flwr/server/compat/app.py
+++ b/src/py/flwr/server/compat/app.py
@@ -26,10 +26,9 @@
 from flwr.common.address import parse_address
 from flwr.common.logger import log, warn_deprecated_feature
 from flwr.proto import driver_pb2  # pylint: disable=E0611
-from flwr.server.app import init_defaults, run_fl
 from flwr.server.client_manager import ClientManager
 from flwr.server.history import History
-from flwr.server.server import Server
+from flwr.server.server import Server, init_defaults, run_fl
 from flwr.server.server_config import ServerConfig
 from flwr.server.strategy import Strategy
 
diff --git a/src/py/flwr/server/server.py b/src/py/flwr/server/server.py
index cf3a4d9aa07c..ea62587b7deb 100644
--- a/src/py/flwr/server/server.py
+++ b/src/py/flwr/server/server.py
@@ -17,7 +17,7 @@
 
 import concurrent.futures
 import timeit
-from logging import DEBUG, INFO
+from logging import DEBUG, INFO, WARN
 from typing import Dict, List, Optional, Tuple, Union
 
 from flwr.common import (
@@ -33,11 +33,13 @@
 )
 from flwr.common.logger import log
 from flwr.common.typing import GetParametersIns
-from flwr.server.client_manager import ClientManager
+from flwr.server.client_manager import ClientManager, SimpleClientManager
 from flwr.server.client_proxy import ClientProxy
 from flwr.server.history import History
 from flwr.server.strategy import FedAvg, Strategy
 
+from .server_config import ServerConfig
+
 FitResultsAndFailures = Tuple[
     List[Tuple[ClientProxy, FitRes]],
     List[Union[Tuple[ClientProxy, FitRes], BaseException]],
@@ -441,3 +443,44 @@ def _handle_finished_future_after_evaluate(
 
     # Not successful, client returned a result where the status code is not OK
     failures.append(result)
+
+
+def init_defaults(
+    server: Optional[Server],
+    config: Optional[ServerConfig],
+    strategy: Optional[Strategy],
+    client_manager: Optional[ClientManager],
+) -> Tuple[Server, ServerConfig]:
+    """Create server instance if none was given."""
+    if server is None:
+        if client_manager is None:
+            client_manager = SimpleClientManager()
+        if strategy is None:
+            strategy = FedAvg()
+        server = Server(client_manager=client_manager, strategy=strategy)
+    elif strategy is not None:
+        log(WARN, "Both server and strategy were provided, ignoring strategy")
+
+    # Set default config values
+    if config is None:
+        config = ServerConfig()
+
+    return server, config
+
+
+def run_fl(
+    server: Server,
+    config: ServerConfig,
+) -> History:
+    """Train a model on the given server and return the History object."""
+    hist = server.fit(num_rounds=config.num_rounds, timeout=config.round_timeout)
+    log(INFO, "app_fit: losses_distributed %s", str(hist.losses_distributed))
+    log(INFO, "app_fit: metrics_distributed_fit %s", str(hist.metrics_distributed_fit))
+    log(INFO, "app_fit: metrics_distributed %s", str(hist.metrics_distributed))
+    log(INFO, "app_fit: losses_centralized %s", str(hist.losses_centralized))
+    log(INFO, "app_fit: metrics_centralized %s", str(hist.metrics_centralized))
+
+    # Graceful shutdown
+    server.disconnect_all_clients(timeout=config.round_timeout)
+
+    return hist
diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py
index f3ffe632bbe8..ff18f37664be 100644
--- a/src/py/flwr/simulation/app.py
+++ b/src/py/flwr/simulation/app.py
@@ -28,10 +28,9 @@
 from flwr.client import ClientFn
 from flwr.common import EventType, event
 from flwr.common.logger import log
-from flwr.server import Server
-from flwr.server.app import init_defaults, run_fl
 from flwr.server.client_manager import ClientManager
 from flwr.server.history import History
+from flwr.server.server import Server, init_defaults, run_fl
 from flwr.server.server_config import ServerConfig
 from flwr.server.strategy import Strategy
 from flwr.simulation.ray_transport.ray_actor import (

From e1010b39bfbb767e4311c0f410f6eda2c3dbb062 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Fri, 23 Feb 2024 12:08:14 +0100
Subject: [PATCH 049/102] Make `ServerApp` shutdown at the end (#3002)

---
 src/py/flwr/server/compat/app.py      | 7 ++++++-
 src/py/flwr/server/compat/app_test.py | 9 +++------
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/src/py/flwr/server/compat/app.py b/src/py/flwr/server/compat/app.py
index 3df779ebf990..2e441db08668 100644
--- a/src/py/flwr/server/compat/app.py
+++ b/src/py/flwr/server/compat/app.py
@@ -142,6 +142,7 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
         initialized_config,
     )
 
+    f_stop = threading.Event()
     # Start the thread updating nodes
     thread = threading.Thread(
         target=update_client_manager,
@@ -149,6 +150,7 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
             grpc_driver,
             initialized_server.client_manager(),
             lock,
+            f_stop,
         ),
     )
     thread.start()
@@ -159,6 +161,8 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
         config=initialized_config,
     )
 
+    f_stop.set()
+
     # Stop the Driver API server and the thread
     with lock:
         if driver:
@@ -177,6 +181,7 @@ def update_client_manager(
     driver: GrpcDriver,
     client_manager: ClientManager,
     lock: threading.Lock,
+    f_stop: threading.Event,
 ) -> None:
     """Update the nodes list in the client manager.
 
@@ -195,7 +200,7 @@ def update_client_manager(
 
     # Loop until the driver is disconnected
     registered_nodes: Dict[int, DriverClientProxy] = {}
-    while True:
+    while not f_stop.is_set():
         with lock:
             # End the while loop if the driver is disconnected
             if driver.stub is None:
diff --git a/src/py/flwr/server/compat/app_test.py b/src/py/flwr/server/compat/app_test.py
index 5f8f04ff2a06..77eeda2848c1 100644
--- a/src/py/flwr/server/compat/app_test.py
+++ b/src/py/flwr/server/compat/app_test.py
@@ -50,15 +50,11 @@ def test_simple_client_manager_update(self) -> None:
         driver.get_nodes.return_value = GetNodesResponse(nodes=expected_nodes)
         client_manager = SimpleClientManager()
         lock = threading.Lock()
-
+        f_stop = threading.Event()
         # Execute
         thread = threading.Thread(
             target=update_client_manager,
-            args=(
-                driver,
-                client_manager,
-                lock,
-            ),
+            args=(driver, client_manager, lock, f_stop),
             daemon=True,
         )
         thread.start()
@@ -83,5 +79,6 @@ def test_simple_client_manager_update(self) -> None:
         assert node_ids == {node.node_id for node in expected_nodes}
         assert updated_node_ids == {node.node_id for node in expected_updated_nodes}
 
+        f_stop.set()
         # Exit
         thread.join()

From 9158dc4f3233e0d330599c3e430b361d2d34d27a Mon Sep 17 00:00:00 2001
From: Heng Pan <pan@flower.ai>
Date: Fri, 23 Feb 2024 11:40:08 +0000
Subject: [PATCH 050/102] Update `RecordSet` (#2992)

Co-authored-by: Daniel J. Beutel <daniel@flower.ai>
---
 e2e/bare/client.py                            |  8 +-
 e2e/pytorch/client.py                         |  8 +-
 examples/secaggplus-mt/workflows.py           | 10 +--
 src/py/flwr/client/grpc_client/connection.py  |  8 +-
 .../client/grpc_client/connection_test.py     |  2 +-
 .../client/message_handler/message_handler.py |  4 +-
 .../mod/secure_aggregation/secaggplus_mod.py  | 12 +--
 .../secure_aggregation/secaggplus_mod_test.py | 12 +--
 src/py/flwr/client/mod/utils_test.py          | 33 +++----
 src/py/flwr/client/node_state_tests.py        | 14 +--
 src/py/flwr/common/record/recordset.py        | 83 ++++++++++--------
 src/py/flwr/common/recordset_compat.py        | 87 +++++++++----------
 src/py/flwr/common/serde.py                   | 17 ++--
 src/py/flwr/common/serde_test.py              |  6 +-
 .../ray_transport/ray_client_proxy_test.py    |  8 +-
 15 files changed, 165 insertions(+), 147 deletions(-)

diff --git a/e2e/bare/client.py b/e2e/bare/client.py
index f044f33b0a75..c291fb0963e4 100644
--- a/e2e/bare/client.py
+++ b/e2e/bare/client.py
@@ -21,14 +21,14 @@ def _record_timestamp_to_state(self):
         """Record timestamp to client's state."""
         t_stamp = datetime.now().timestamp()
         value = str(t_stamp)
-        if STATE_VAR in self.context.state.configs.keys():
-            value = self.context.state.get_configs(STATE_VAR)[STATE_VAR]  # type: ignore
+        if STATE_VAR in self.context.state.configs_records.keys():
+            value = self.context.state.configs_records[STATE_VAR][STATE_VAR]  # type: ignore
             value += f",{t_stamp}"
 
-        self.context.state.set_configs(name=STATE_VAR, record=ConfigsRecord({STATE_VAR: value}))
+        self.context.state.configs_records[STATE_VAR] = ConfigsRecord({STATE_VAR: value})
     
     def _retrieve_timestamp_from_state(self):
-        return self.context.state.get_configs(STATE_VAR)[STATE_VAR]
+        return self.context.state.configs_records[STATE_VAR][STATE_VAR]
     
     def fit(self, parameters, config):
         model_params = parameters
diff --git a/e2e/pytorch/client.py b/e2e/pytorch/client.py
index 6ac9acf92505..1fd07763148e 100644
--- a/e2e/pytorch/client.py
+++ b/e2e/pytorch/client.py
@@ -97,14 +97,14 @@ def _record_timestamp_to_state(self):
         """Record timestamp to client's state."""
         t_stamp = datetime.now().timestamp()
         value = str(t_stamp)
-        if STATE_VAR in self.context.state.configs.keys():
-            value = self.context.state.get_configs(STATE_VAR)[STATE_VAR]  # type: ignore
+        if STATE_VAR in self.context.state.configs_records.keys():
+            value = self.context.state.configs_records[STATE_VAR][STATE_VAR]  # type: ignore
             value += f",{t_stamp}"
 
-        self.context.state.set_configs(name=STATE_VAR, record=ConfigsRecord({STATE_VAR: value}))
+        self.context.state.configs_records[STATE_VAR] = ConfigsRecord({STATE_VAR: value})
     
     def _retrieve_timestamp_from_state(self):
-        return self.context.state.get_configs(STATE_VAR)[STATE_VAR]
+        return self.context.state.configs_records[STATE_VAR][STATE_VAR]
     def fit(self, parameters, config):
         set_parameters(net, parameters)
         train(net, trainloader, epochs=1)
diff --git a/examples/secaggplus-mt/workflows.py b/examples/secaggplus-mt/workflows.py
index 877a5b136485..4079a2568f2f 100644
--- a/examples/secaggplus-mt/workflows.py
+++ b/examples/secaggplus-mt/workflows.py
@@ -54,10 +54,10 @@
     RECORD_KEY_CONFIGS,
 )
 from flwr.common.secure_aggregation.secaggplus_utils import pseudo_rand_gen
-from flwr.common.typing import ConfigsRecordValues, FitIns, ServerMessage
+from flwr.common.typing import ConfigsRecordValues, FitIns
 from flwr.proto.task_pb2 import Task
 from flwr.common import serde
-from flwr.common.constant import TASK_TYPE_FIT
+from flwr.common.constant import MESSAGE_TYPE_FIT
 from flwr.common import RecordSet
 from flwr.common import recordset_compat as compat
 from flwr.common import ConfigsRecord
@@ -79,16 +79,16 @@ def _wrap_in_task(
         recordset = compat.fitins_to_recordset(fit_ins, keep_input=True)
     else:
         recordset = RecordSet()
-    recordset.set_configs(RECORD_KEY_CONFIGS, ConfigsRecord(named_values))
+    recordset.configs_records[RECORD_KEY_CONFIGS] = ConfigsRecord(named_values)
     return Task(
-        task_type=TASK_TYPE_FIT,
+        task_type=MESSAGE_TYPE_FIT,
         recordset=serde.recordset_to_proto(recordset),
     )
 
 
 def _get_from_task(task: Task) -> Dict[str, ConfigsRecordValues]:
     recordset = serde.recordset_from_proto(task.recordset)
-    return recordset.get_configs(RECORD_KEY_CONFIGS).data
+    return recordset.configs_records[RECORD_KEY_CONFIGS]
 
 
 _secure_aggregation_configuration = {
diff --git a/src/py/flwr/client/grpc_client/connection.py b/src/py/flwr/client/grpc_client/connection.py
index 850090d44daf..3561626dcb39 100644
--- a/src/py/flwr/client/grpc_client/connection.py
+++ b/src/py/flwr/client/grpc_client/connection.py
@@ -159,8 +159,8 @@ def receive() -> Message:
             message_type = MESSAGE_TYPE_EVALUATE
         elif field == "reconnect_ins":
             recordset = RecordSet()
-            recordset.set_configs(
-                "config", ConfigsRecord({"seconds": proto.reconnect_ins.seconds})
+            recordset.configs_records["config"] = ConfigsRecord(
+                {"seconds": proto.reconnect_ins.seconds}
             )
             message_type = "reconnect"
         else:
@@ -207,7 +207,9 @@ def send(message: Message) -> None:
             evalres = compat.recordset_to_evaluateres(recordset)
             msg_proto = ClientMessage(evaluate_res=serde.evaluate_res_to_proto(evalres))
         elif message_type == "reconnect":
-            reason = cast(Reason.ValueType, recordset.get_configs("config")["reason"])
+            reason = cast(
+                Reason.ValueType, recordset.configs_records["config"]["reason"]
+            )
             msg_proto = ClientMessage(
                 disconnect_res=ClientMessage.DisconnectRes(reason=reason)
             )
diff --git a/src/py/flwr/client/grpc_client/connection_test.py b/src/py/flwr/client/grpc_client/connection_test.py
index 82dd0fcdd997..30bff068b60a 100644
--- a/src/py/flwr/client/grpc_client/connection_test.py
+++ b/src/py/flwr/client/grpc_client/connection_test.py
@@ -67,7 +67,7 @@
         ttl="",
         message_type="reconnect",
     ),
-    content=RecordSet(configs={"config": ConfigsRecord({"reason": 0})}),
+    content=RecordSet(configs_records={"config": ConfigsRecord({"reason": 0})}),
 )
 
 
diff --git a/src/py/flwr/client/message_handler/message_handler.py b/src/py/flwr/client/message_handler/message_handler.py
index 0f98ade24225..8e2248b88a01 100644
--- a/src/py/flwr/client/message_handler/message_handler.py
+++ b/src/py/flwr/client/message_handler/message_handler.py
@@ -75,7 +75,7 @@ def handle_control_message(message: Message) -> Tuple[Optional[Message], int]:
     if message.metadata.message_type == "reconnect":
         # Retrieve ReconnectIns from recordset
         recordset = message.content
-        seconds = cast(int, recordset.get_configs("config")["seconds"])
+        seconds = cast(int, recordset.configs_records["config"]["seconds"])
         # Construct ReconnectIns and call _reconnect
         disconnect_msg, sleep_duration = _reconnect(
             ServerMessage.ReconnectIns(seconds=seconds)
@@ -83,7 +83,7 @@ def handle_control_message(message: Message) -> Tuple[Optional[Message], int]:
         # Store DisconnectRes in recordset
         reason = cast(int, disconnect_msg.disconnect_res.reason)
         recordset = RecordSet()
-        recordset.set_configs("config", ConfigsRecord({"reason": reason}))
+        recordset.configs_records["config"] = ConfigsRecord({"reason": reason})
         out_message = message.create_reply(recordset, ttl="")
         # Return TaskRes and sleep duration
         return out_message, sleep_duration
diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
index 7ac311108383..fcb30c1eb0da 100644
--- a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
@@ -175,13 +175,13 @@ def secaggplus_mod(
         return call_next(msg, ctxt)
 
     # Retrieve local state
-    if RECORD_KEY_STATE not in ctxt.state.configs:
-        ctxt.state.set_configs(RECORD_KEY_STATE, ConfigsRecord({}))
-    state_dict = ctxt.state.get_configs(RECORD_KEY_STATE)
+    if RECORD_KEY_STATE not in ctxt.state.configs_records:
+        ctxt.state.configs_records[RECORD_KEY_STATE] = ConfigsRecord({})
+    state_dict = ctxt.state.configs_records[RECORD_KEY_STATE]
     state = SecAggPlusState(**state_dict)
 
     # Retrieve incoming configs
-    configs = msg.content.get_configs(RECORD_KEY_CONFIGS)
+    configs = msg.content.configs_records[RECORD_KEY_CONFIGS]
 
     # Check the validity of the next stage
     check_stage(state.current_stage, configs)
@@ -206,10 +206,10 @@ def secaggplus_mod(
         raise ValueError(f"Unknown secagg stage: {state.current_stage}")
 
     # Save state
-    ctxt.state.set_configs(RECORD_KEY_STATE, ConfigsRecord(state.to_dict()))
+    ctxt.state.configs_records[RECORD_KEY_STATE] = ConfigsRecord(state.to_dict())
 
     # Return message
-    content = RecordSet(configs={RECORD_KEY_CONFIGS: ConfigsRecord(res, False)})
+    content = RecordSet(configs_records={RECORD_KEY_CONFIGS: ConfigsRecord(res, False)})
     return msg.create_reply(content, ttl="")
 
 
diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
index 3a888e6cd87d..0a181f2ea8f5 100644
--- a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
@@ -69,27 +69,29 @@ def func(configs: Dict[str, ConfigsRecordValues]) -> ConfigsRecord:
                 ttl="",
                 message_type=MESSAGE_TYPE_FIT,
             ),
-            content=RecordSet(configs={RECORD_KEY_CONFIGS: ConfigsRecord(configs)}),
+            content=RecordSet(
+                configs_records={RECORD_KEY_CONFIGS: ConfigsRecord(configs)}
+            ),
         )
         out_msg = app(in_msg, ctxt)
-        return out_msg.content.get_configs(RECORD_KEY_CONFIGS)
+        return out_msg.content.configs_records[RECORD_KEY_CONFIGS]
 
     return func
 
 
 def _make_ctxt() -> Context:
     cfg = ConfigsRecord(SecAggPlusState().to_dict())
-    return Context(RecordSet(configs={RECORD_KEY_STATE: cfg}))
+    return Context(RecordSet(configs_records={RECORD_KEY_STATE: cfg}))
 
 
 def _make_set_state_fn(
     ctxt: Context,
 ) -> Callable[[str], None]:
     def set_stage(stage: str) -> None:
-        state_dict = ctxt.state.get_configs(RECORD_KEY_STATE)
+        state_dict = ctxt.state.configs_records[RECORD_KEY_STATE]
         state = SecAggPlusState(**state_dict)
         state.current_stage = stage
-        ctxt.state.set_configs(RECORD_KEY_STATE, ConfigsRecord(state.to_dict()))
+        ctxt.state.configs_records[RECORD_KEY_STATE] = ConfigsRecord(state.to_dict())
 
     return set_stage
 
diff --git a/src/py/flwr/client/mod/utils_test.py b/src/py/flwr/client/mod/utils_test.py
index 4915f0710a0f..e588b8b53b3b 100644
--- a/src/py/flwr/client/mod/utils_test.py
+++ b/src/py/flwr/client/mod/utils_test.py
@@ -16,7 +16,7 @@
 
 
 import unittest
-from typing import List
+from typing import List, cast
 
 from flwr.client.typing import ClientAppCallable, Mod
 from flwr.common import (
@@ -36,10 +36,10 @@
 
 def _increment_context_counter(context: Context) -> None:
     # Read from context
-    current_counter: int = context.state.get_metrics(METRIC)[COUNTER]  # type: ignore
+    current_counter = cast(int, context.state.metrics_records[METRIC][COUNTER])
     # update and override context
     current_counter += 1
-    context.state.set_metrics(METRIC, record=MetricsRecord({COUNTER: current_counter}))
+    context.state.metrics_records[METRIC] = MetricsRecord({COUNTER: current_counter})
 
 
 def make_mock_mod(name: str, footprint: List[str]) -> Mod:
@@ -48,13 +48,13 @@ def make_mock_mod(name: str, footprint: List[str]) -> Mod:
     def mod(message: Message, context: Context, app: ClientAppCallable) -> Message:
         footprint.append(name)
         # add empty ConfigRecord to in_message for this mod
-        message.content.set_configs(name=name, record=ConfigsRecord())
+        message.content.configs_records[name] = ConfigsRecord()
         _increment_context_counter(context)
         out_message: Message = app(message, context)
         footprint.append(name)
         _increment_context_counter(context)
         # add empty ConfigRegcord to out_message for this mod
-        out_message.content.set_configs(name=name, record=ConfigsRecord())
+        out_message.content.configs_records[name] = ConfigsRecord()
         return out_message
 
     return mod
@@ -65,9 +65,9 @@ def make_mock_app(name: str, footprint: List[str]) -> ClientAppCallable:
 
     def app(message: Message, context: Context) -> Message:
         footprint.append(name)
-        message.content.set_configs(name=name, record=ConfigsRecord())
+        message.content.configs_records[name] = ConfigsRecord()
         out_message = Message(metadata=message.metadata, content=RecordSet())
-        out_message.content.set_configs(name=name, record=ConfigsRecord())
+        out_message.content.configs_records[name] = ConfigsRecord()
         print(context)
         return out_message
 
@@ -102,7 +102,7 @@ def test_multiple_mods(self) -> None:
         mock_mods = [make_mock_mod(name, footprint) for name in mock_mod_names]
 
         state = RecordSet()
-        state.set_metrics(METRIC, record=MetricsRecord({COUNTER: 0.0}))
+        state.metrics_records[METRIC] = MetricsRecord({COUNTER: 0.0})
         context = Context(state=state)
         message = _get_dummy_flower_message()
 
@@ -114,11 +114,14 @@ def test_multiple_mods(self) -> None:
         trace = mock_mod_names + ["app"]
         self.assertEqual(footprint, trace + list(reversed(mock_mod_names)))
         # pylint: disable-next=no-member
-        self.assertEqual("".join(message.content.configs.keys()), "".join(trace))
         self.assertEqual(
-            "".join(out_message.content.configs.keys()), "".join(reversed(trace))
+            "".join(message.content.configs_records.keys()), "".join(trace)
         )
-        self.assertEqual(state.get_metrics(METRIC)[COUNTER], 2 * len(mock_mods))
+        self.assertEqual(
+            "".join(out_message.content.configs_records.keys()),
+            "".join(reversed(trace)),
+        )
+        self.assertEqual(state.metrics_records[METRIC][COUNTER], 2 * len(mock_mods))
 
     def test_filter(self) -> None:
         """Test if a mod can filter incoming TaskIns."""
@@ -134,9 +137,9 @@ def filter_mod(
             _2: ClientAppCallable,
         ) -> Message:
             footprint.append("filter")
-            message.content.set_configs(name="filter", record=ConfigsRecord())
+            message.content.configs_records["filter"] = ConfigsRecord()
             out_message = Message(metadata=message.metadata, content=RecordSet())
-            out_message.content.set_configs(name="filter", record=ConfigsRecord())
+            out_message.content.configs_records["filter"] = ConfigsRecord()
             # Skip calling app
             return out_message
 
@@ -147,5 +150,5 @@ def filter_mod(
         # Assert
         self.assertEqual(footprint, ["filter"])
         # pylint: disable-next=no-member
-        self.assertEqual(list(message.content.configs.keys())[0], "filter")
-        self.assertEqual(list(out_message.content.configs.keys())[0], "filter")
+        self.assertEqual(list(message.content.configs_records.keys())[0], "filter")
+        self.assertEqual(list(out_message.content.configs_records.keys())[0], "filter")
diff --git a/src/py/flwr/client/node_state_tests.py b/src/py/flwr/client/node_state_tests.py
index fddcd8ed4755..193f52661579 100644
--- a/src/py/flwr/client/node_state_tests.py
+++ b/src/py/flwr/client/node_state_tests.py
@@ -15,6 +15,8 @@
 """Node state tests."""
 
 
+from typing import cast
+
 from flwr.client.node_state import NodeState
 from flwr.common import ConfigsRecord, Context
 from flwr.proto.task_pb2 import TaskIns  # pylint: disable=E0611
@@ -22,13 +24,11 @@
 
 def _run_dummy_task(context: Context) -> Context:
     counter_value: str = "1"
-    if "counter" in context.state.configs.keys():
-        counter_value = context.get_configs("counter")["count"]  # type: ignore
+    if "counter" in context.state.configs_records.keys():
+        counter_value = cast(str, context.state.configs_records["counter"]["count"])
         counter_value += "1"
 
-    context.state.set_configs(
-        name="counter", record=ConfigsRecord({"count": counter_value})
-    )
+    context.state.configs_records["counter"] = ConfigsRecord({"count": counter_value})
 
     return context
 
@@ -60,4 +60,6 @@ def test_multirun_in_node_state() -> None:
 
     # Verify values
     for run_id, context in node_state.run_contexts.items():
-        assert context.state.get_configs("counter")["count"] == expected_values[run_id]
+        assert (
+            context.state.configs_records["counter"]["count"] == expected_values[run_id]
+        )
diff --git a/src/py/flwr/common/record/recordset.py b/src/py/flwr/common/record/recordset.py
index 61c880c970b8..d8ef44ab15c2 100644
--- a/src/py/flwr/common/record/recordset.py
+++ b/src/py/flwr/common/record/recordset.py
@@ -15,54 +15,65 @@
 """RecordSet."""
 
 
-from dataclasses import dataclass, field
-from typing import Dict
+from dataclasses import dataclass
+from typing import Callable, Dict, Optional, Type, TypeVar
 
 from .configsrecord import ConfigsRecord
 from .metricsrecord import MetricsRecord
 from .parametersrecord import ParametersRecord
+from .typeddict import TypedDict
+
+T = TypeVar("T")
 
 
 @dataclass
 class RecordSet:
-    """Definition of RecordSet."""
-
-    parameters: Dict[str, ParametersRecord] = field(default_factory=dict)
-    metrics: Dict[str, MetricsRecord] = field(default_factory=dict)
-    configs: Dict[str, ConfigsRecord] = field(default_factory=dict)
-
-    def set_parameters(self, name: str, record: ParametersRecord) -> None:
-        """Add a ParametersRecord."""
-        self.parameters[name] = record
-
-    def get_parameters(self, name: str) -> ParametersRecord:
-        """Get a ParametesRecord."""
-        return self.parameters[name]
+    """RecordSet stores groups of parameters, metrics and configs."""
 
-    def del_parameters(self, name: str) -> None:
-        """Delete a ParametersRecord."""
-        del self.parameters[name]
+    _parameters_records: TypedDict[str, ParametersRecord]
+    _metrics_records: TypedDict[str, MetricsRecord]
+    _configs_records: TypedDict[str, ConfigsRecord]
 
-    def set_metrics(self, name: str, record: MetricsRecord) -> None:
-        """Add a MetricsRecord."""
-        self.metrics[name] = record
+    def __init__(
+        self,
+        parameters_records: Optional[Dict[str, ParametersRecord]] = None,
+        metrics_records: Optional[Dict[str, MetricsRecord]] = None,
+        configs_records: Optional[Dict[str, ConfigsRecord]] = None,
+    ) -> None:
+        def _get_check_fn(__t: Type[T]) -> Callable[[T], None]:
+            def _check_fn(__v: T) -> None:
+                if not isinstance(__v, __t):
+                    raise TypeError(f"Expected `{__t}`, but `{type(__v)}` was passed.")
 
-    def get_metrics(self, name: str) -> MetricsRecord:
-        """Get a MetricsRecord."""
-        return self.metrics[name]
+            return _check_fn
 
-    def del_metrics(self, name: str) -> None:
-        """Delete a MetricsRecord."""
-        del self.metrics[name]
+        self._parameters_records = TypedDict[str, ParametersRecord](
+            _get_check_fn(str), _get_check_fn(ParametersRecord)
+        )
+        self._metrics_records = TypedDict[str, MetricsRecord](
+            _get_check_fn(str), _get_check_fn(MetricsRecord)
+        )
+        self._configs_records = TypedDict[str, ConfigsRecord](
+            _get_check_fn(str), _get_check_fn(ConfigsRecord)
+        )
+        if parameters_records is not None:
+            self._parameters_records.update(parameters_records)
+        if metrics_records is not None:
+            self._metrics_records.update(metrics_records)
+        if configs_records is not None:
+            self._configs_records.update(configs_records)
 
-    def set_configs(self, name: str, record: ConfigsRecord) -> None:
-        """Add a ConfigsRecord."""
-        self.configs[name] = record
+    @property
+    def parameters_records(self) -> TypedDict[str, ParametersRecord]:
+        """Dictionary holding ParametersRecord instances."""
+        return self._parameters_records
 
-    def get_configs(self, name: str) -> ConfigsRecord:
-        """Get a ConfigsRecord."""
-        return self.configs[name]
+    @property
+    def metrics_records(self) -> TypedDict[str, MetricsRecord]:
+        """Dictionary holding MetricsRecord instances."""
+        return self._metrics_records
 
-    def del_configs(self, name: str) -> None:
-        """Delete a ConfigsRecord."""
-        del self.configs[name]
+    @property
+    def configs_records(self) -> TypedDict[str, ConfigsRecord]:
+        """Dictionary holding ConfigsRecord instances."""
+        return self._configs_records
diff --git a/src/py/flwr/common/recordset_compat.py b/src/py/flwr/common/recordset_compat.py
index 4e90ed79a425..394ea1353bab 100644
--- a/src/py/flwr/common/recordset_compat.py
+++ b/src/py/flwr/common/recordset_compat.py
@@ -129,14 +129,14 @@ def _recordset_to_fit_or_evaluate_ins_components(
 ) -> Tuple[Parameters, Dict[str, Scalar]]:
     """Derive Fit/Evaluate Ins from a RecordSet."""
     # get Array and construct Parameters
-    parameters_record = recordset.get_parameters(f"{ins_str}.parameters")
+    parameters_record = recordset.parameters_records[f"{ins_str}.parameters"]
 
     parameters = parametersrecord_to_parameters(
         parameters_record, keep_input=keep_input
     )
 
     # get config dict
-    config_record = recordset.get_configs(f"{ins_str}.config")
+    config_record = recordset.configs_records[f"{ins_str}.config"]
     # pylint: disable-next=protected-access
     config_dict = _check_mapping_from_recordscalartype_to_scalar(config_record._data)
 
@@ -149,13 +149,11 @@ def _fit_or_evaluate_ins_to_recordset(
     recordset = RecordSet()
 
     ins_str = "fitins" if isinstance(ins, FitIns) else "evaluateins"
-    recordset.set_parameters(
-        name=f"{ins_str}.parameters",
-        record=parameters_to_parametersrecord(ins.parameters, keep_input=keep_input),
-    )
+    parametersrecord = parameters_to_parametersrecord(ins.parameters, keep_input)
+    recordset.parameters_records[f"{ins_str}.parameters"] = parametersrecord
 
-    recordset.set_configs(
-        name=f"{ins_str}.config", record=ConfigsRecord(ins.config)  # type: ignore
+    recordset.configs_records[f"{ins_str}.config"] = ConfigsRecord(
+        ins.config  # type: ignore
     )
 
     return recordset
@@ -170,12 +168,12 @@ def _embed_status_into_recordset(
     }
     # we add it to a `ConfigsRecord`` because the `status.message`` is a string
     # and `str` values aren't supported in `MetricsRecords`
-    recordset.set_configs(f"{res_str}.status", record=ConfigsRecord(status_dict))
+    recordset.configs_records[f"{res_str}.status"] = ConfigsRecord(status_dict)
     return recordset
 
 
 def _extract_status_from_recordset(res_str: str, recordset: RecordSet) -> Status:
-    status = recordset.get_configs(f"{res_str}.status")
+    status = recordset.configs_records[f"{res_str}.status"]
     code = cast(int, status["code"])
     return Status(code=Code(code), message=str(status["message"]))
 
@@ -200,13 +198,13 @@ def recordset_to_fitres(recordset: RecordSet, keep_input: bool) -> FitRes:
     """Derive FitRes from a RecordSet object."""
     ins_str = "fitres"
     parameters = parametersrecord_to_parameters(
-        recordset.get_parameters(f"{ins_str}.parameters"), keep_input=keep_input
+        recordset.parameters_records[f"{ins_str}.parameters"], keep_input=keep_input
     )
 
     num_examples = cast(
-        int, recordset.get_metrics(f"{ins_str}.num_examples")["num_examples"]
+        int, recordset.metrics_records[f"{ins_str}.num_examples"]["num_examples"]
     )
-    configs_record = recordset.get_configs(f"{ins_str}.metrics")
+    configs_record = recordset.configs_records[f"{ins_str}.metrics"]
     # pylint: disable-next=protected-access
     metrics = _check_mapping_from_recordscalartype_to_scalar(configs_record._data)
     status = _extract_status_from_recordset(ins_str, recordset)
@@ -222,16 +220,17 @@ def fitres_to_recordset(fitres: FitRes, keep_input: bool) -> RecordSet:
 
     res_str = "fitres"
 
-    recordset.set_configs(
-        name=f"{res_str}.metrics", record=ConfigsRecord(fitres.metrics)  # type: ignore
+    recordset.configs_records[f"{res_str}.metrics"] = ConfigsRecord(
+        fitres.metrics  # type: ignore
     )
-    recordset.set_metrics(
-        name=f"{res_str}.num_examples",
-        record=MetricsRecord({"num_examples": fitres.num_examples}),
+    recordset.metrics_records[f"{res_str}.num_examples"] = MetricsRecord(
+        {"num_examples": fitres.num_examples},
     )
-    recordset.set_parameters(
-        name=f"{res_str}.parameters",
-        record=parameters_to_parametersrecord(fitres.parameters, keep_input),
+    recordset.parameters_records[f"{res_str}.parameters"] = (
+        parameters_to_parametersrecord(
+            fitres.parameters,
+            keep_input,
+        )
     )
 
     # status
@@ -260,12 +259,12 @@ def recordset_to_evaluateres(recordset: RecordSet) -> EvaluateRes:
     """Derive EvaluateRes from a RecordSet object."""
     ins_str = "evaluateres"
 
-    loss = cast(int, recordset.get_metrics(f"{ins_str}.loss")["loss"])
+    loss = cast(int, recordset.metrics_records[f"{ins_str}.loss"]["loss"])
 
     num_examples = cast(
-        int, recordset.get_metrics(f"{ins_str}.num_examples")["num_examples"]
+        int, recordset.metrics_records[f"{ins_str}.num_examples"]["num_examples"]
     )
-    configs_record = recordset.get_configs(f"{ins_str}.metrics")
+    configs_record = recordset.configs_records[f"{ins_str}.metrics"]
 
     # pylint: disable-next=protected-access
     metrics = _check_mapping_from_recordscalartype_to_scalar(configs_record._data)
@@ -282,21 +281,18 @@ def evaluateres_to_recordset(evaluateres: EvaluateRes) -> RecordSet:
 
     res_str = "evaluateres"
     # loss
-    recordset.set_metrics(
-        name=f"{res_str}.loss",
-        record=MetricsRecord({"loss": evaluateres.loss}),
+    recordset.metrics_records[f"{res_str}.loss"] = MetricsRecord(
+        {"loss": evaluateres.loss},
     )
 
     # num_examples
-    recordset.set_metrics(
-        name=f"{res_str}.num_examples",
-        record=MetricsRecord({"num_examples": evaluateres.num_examples}),
+    recordset.metrics_records[f"{res_str}.num_examples"] = MetricsRecord(
+        {"num_examples": evaluateres.num_examples},
     )
 
     # metrics
-    recordset.set_configs(
-        name=f"{res_str}.metrics",
-        record=ConfigsRecord(evaluateres.metrics),  # type: ignore
+    recordset.configs_records[f"{res_str}.metrics"] = ConfigsRecord(
+        evaluateres.metrics,  # type: ignore
     )
 
     # status
@@ -309,7 +305,7 @@ def evaluateres_to_recordset(evaluateres: EvaluateRes) -> RecordSet:
 
 def recordset_to_getparametersins(recordset: RecordSet) -> GetParametersIns:
     """Derive GetParametersIns from a RecordSet object."""
-    config_record = recordset.get_configs("getparametersins.config")
+    config_record = recordset.configs_records["getparametersins.config"]
     # pylint: disable-next=protected-access
     config_dict = _check_mapping_from_recordscalartype_to_scalar(config_record._data)
 
@@ -320,9 +316,8 @@ def getparametersins_to_recordset(getparameters_ins: GetParametersIns) -> Record
     """Construct a RecordSet from a GetParametersIns object."""
     recordset = RecordSet()
 
-    recordset.set_configs(
-        name="getparametersins.config",
-        record=ConfigsRecord(getparameters_ins.config),  # type: ignore
+    recordset.configs_records["getparametersins.config"] = ConfigsRecord(
+        getparameters_ins.config,  # type: ignore
     )
     return recordset
 
@@ -336,7 +331,7 @@ def getparametersres_to_recordset(
     parameters_record = parameters_to_parametersrecord(
         getparametersres.parameters, keep_input=keep_input
     )
-    recordset.set_parameters(f"{res_str}.parameters", parameters_record)
+    recordset.parameters_records[f"{res_str}.parameters"] = parameters_record
 
     # status
     recordset = _embed_status_into_recordset(
@@ -352,7 +347,7 @@ def recordset_to_getparametersres(
     """Derive GetParametersRes from a RecordSet object."""
     res_str = "getparametersres"
     parameters = parametersrecord_to_parameters(
-        recordset.get_parameters(f"{res_str}.parameters"), keep_input=keep_input
+        recordset.parameters_records[f"{res_str}.parameters"], keep_input=keep_input
     )
 
     status = _extract_status_from_recordset(res_str, recordset)
@@ -361,7 +356,7 @@ def recordset_to_getparametersres(
 
 def recordset_to_getpropertiesins(recordset: RecordSet) -> GetPropertiesIns:
     """Derive GetPropertiesIns from a RecordSet object."""
-    config_record = recordset.get_configs("getpropertiesins.config")
+    config_record = recordset.configs_records["getpropertiesins.config"]
     # pylint: disable-next=protected-access
     config_dict = _check_mapping_from_recordscalartype_to_scalar(config_record._data)
 
@@ -371,9 +366,8 @@ def recordset_to_getpropertiesins(recordset: RecordSet) -> GetPropertiesIns:
 def getpropertiesins_to_recordset(getpropertiesins: GetPropertiesIns) -> RecordSet:
     """Construct a RecordSet from a GetPropertiesRes object."""
     recordset = RecordSet()
-    recordset.set_configs(
-        name="getpropertiesins.config",
-        record=ConfigsRecord(getpropertiesins.config),  # type: ignore
+    recordset.configs_records["getpropertiesins.config"] = ConfigsRecord(
+        getpropertiesins.config,  # type: ignore
     )
     return recordset
 
@@ -381,7 +375,7 @@ def getpropertiesins_to_recordset(getpropertiesins: GetPropertiesIns) -> RecordS
 def recordset_to_getpropertiesres(recordset: RecordSet) -> GetPropertiesRes:
     """Derive GetPropertiesRes from a RecordSet object."""
     res_str = "getpropertiesres"
-    config_record = recordset.get_configs(f"{res_str}.properties")
+    config_record = recordset.configs_records[f"{res_str}.properties"]
     # pylint: disable-next=protected-access
     properties = _check_mapping_from_recordscalartype_to_scalar(config_record._data)
 
@@ -394,9 +388,8 @@ def getpropertiesres_to_recordset(getpropertiesres: GetPropertiesRes) -> RecordS
     """Construct a RecordSet from a GetPropertiesRes object."""
     recordset = RecordSet()
     res_str = "getpropertiesres"
-    recordset.set_configs(
-        name=f"{res_str}.properties",
-        record=ConfigsRecord(getpropertiesres.properties),  # type: ignore
+    recordset.configs_records[f"{res_str}.properties"] = ConfigsRecord(
+        getpropertiesres.properties,  # type: ignore
     )
     # status
     recordset = _embed_status_into_recordset(
diff --git a/src/py/flwr/common/serde.py b/src/py/flwr/common/serde.py
index 8911cbe690de..531a4bde6e9d 100644
--- a/src/py/flwr/common/serde.py
+++ b/src/py/flwr/common/serde.py
@@ -519,24 +519,29 @@ def recordset_to_proto(recordset: RecordSet) -> ProtoRecordSet:
     """Serialize RecordSet to ProtoBuf."""
     return ProtoRecordSet(
         parameters={
-            k: parameters_record_to_proto(v) for k, v in recordset.parameters.items()
+            k: parameters_record_to_proto(v)
+            for k, v in recordset.parameters_records.items()
+        },
+        metrics={
+            k: metrics_record_to_proto(v) for k, v in recordset.metrics_records.items()
+        },
+        configs={
+            k: configs_record_to_proto(v) for k, v in recordset.configs_records.items()
         },
-        metrics={k: metrics_record_to_proto(v) for k, v in recordset.metrics.items()},
-        configs={k: configs_record_to_proto(v) for k, v in recordset.configs.items()},
     )
 
 
 def recordset_from_proto(recordset_proto: ProtoRecordSet) -> RecordSet:
     """Deserialize RecordSet from ProtoBuf."""
     return RecordSet(
-        parameters={
+        parameters_records={
             k: parameters_record_from_proto(v)
             for k, v in recordset_proto.parameters.items()
         },
-        metrics={
+        metrics_records={
             k: metrics_record_from_proto(v) for k, v in recordset_proto.metrics.items()
         },
-        configs={
+        configs_records={
             k: configs_record_from_proto(v) for k, v in recordset_proto.configs.items()
         },
     )
diff --git a/src/py/flwr/common/serde_test.py b/src/py/flwr/common/serde_test.py
index 1c548b4f9e24..1f25fd1852c1 100644
--- a/src/py/flwr/common/serde_test.py
+++ b/src/py/flwr/common/serde_test.py
@@ -195,15 +195,15 @@ def recordset(
     ) -> RecordSet:
         """Create a RecordSet."""
         return RecordSet(
-            parameters={
+            parameters_records={
                 self.get_str(): self.parameters_record()
                 for _ in range(num_params_records)
             },
-            metrics={
+            metrics_records={
                 self.get_str(): self.metrics_record()
                 for _ in range(num_metrics_records)
             },
-            configs={
+            configs_records={
                 self.get_str(): self.configs_record()
                 for _ in range(num_configs_records)
             },
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
index 0db78e0e3bed..3eeabe0292c9 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
@@ -57,8 +57,8 @@ def get_properties(self, config: Config) -> Dict[str, Scalar]:
         result = int(self.cid) * pi
 
         # store something in context
-        self.context.state.set_configs(
-            "result", record=ConfigsRecord({"result": str(result)})
+        self.context.state.configs_records["result"] = ConfigsRecord(
+            {"result": str(result)}
         )
         return {"result": result}
 
@@ -168,9 +168,9 @@ def test_cid_consistency_all_submit_first_run_consistency() -> None:
         assert int(prox.cid) * pi == res.properties["result"]
         assert (
             str(int(prox.cid) * pi)
-            == prox.proxy_state.retrieve_context(run_id).state.get_configs("result")[
+            == prox.proxy_state.retrieve_context(run_id).state.configs_records[
                 "result"
-            ]
+            ]["result"]
         )
 
     ray.shutdown()

From a50dfd06431fa29e832dffca67b318e0503628e1 Mon Sep 17 00:00:00 2001
From: Heng Pan <pan@flower.ai>
Date: Fri, 23 Feb 2024 12:11:41 +0000
Subject: [PATCH 051/102] Add deprecation warning when NumPyClient is returned
 (#3003)

---
 .../flwr/client/message_handler/message_handler.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/src/py/flwr/client/message_handler/message_handler.py b/src/py/flwr/client/message_handler/message_handler.py
index 8e2248b88a01..e7e6c7e05c71 100644
--- a/src/py/flwr/client/message_handler/message_handler.py
+++ b/src/py/flwr/client/message_handler/message_handler.py
@@ -15,6 +15,7 @@
 """Client-side message handler."""
 
 
+from logging import WARN
 from typing import Optional, Tuple, cast
 
 from flwr.client.client import (
@@ -23,8 +24,9 @@
     maybe_call_get_parameters,
     maybe_call_get_properties,
 )
+from flwr.client.numpy_client import NumPyClient
 from flwr.client.typing import ClientFn
-from flwr.common import ConfigsRecord, Context, Message, Metadata, RecordSet
+from flwr.common import ConfigsRecord, Context, Message, Metadata, RecordSet, log
 from flwr.common.constant import (
     MESSAGE_TYPE_EVALUATE,
     MESSAGE_TYPE_FIT,
@@ -98,6 +100,16 @@ def handle_legacy_message_from_msgtype(
     """Handle legacy message in the inner most mod."""
     client = client_fn(str(message.metadata.dst_node_id))
 
+    # Check if NumPyClient is returend
+    if isinstance(client, NumPyClient):
+        client = client.to_client()
+        log(
+            WARN,
+            "Deprecation Warning: The `client_fn` function must return an instance "
+            "of `Client`, but an instance of `NumpyClient` was returned. "
+            "Please use `NumPyClient.to_client()` method to convert it to `Client`.",
+        )
+
     client.set_context(context)
 
     message_type = message.metadata.message_type

From 3c28c26e2e6c4ba5cba56400b169325d83a5f3d9 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Fri, 23 Feb 2024 16:04:38 +0100
Subject: [PATCH 052/102] Introduce base `Backend` (#2994)

---
 .../superlink/fleet/vce/backend/__init__.py   | 22 +++++++
 .../superlink/fleet/vce/backend/backend.py    | 60 +++++++++++++++++++
 2 files changed, 82 insertions(+)
 create mode 100644 src/py/flwr/server/superlink/fleet/vce/backend/__init__.py
 create mode 100644 src/py/flwr/server/superlink/fleet/vce/backend/backend.py

diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/__init__.py b/src/py/flwr/server/superlink/fleet/vce/backend/__init__.py
new file mode 100644
index 000000000000..98e7a2758f2e
--- /dev/null
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/__init__.py
@@ -0,0 +1,22 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Simulation Engine Backends."""
+
+from .backend import Backend, BackendConfig
+
+__all__ = [
+    "Backend",
+    "BackendConfig",
+]
diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/backend.py b/src/py/flwr/server/superlink/fleet/vce/backend/backend.py
new file mode 100644
index 000000000000..b6cddf2eefa5
--- /dev/null
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/backend.py
@@ -0,0 +1,60 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Generic Backend class for Fleet API using the Simulation Engine."""
+
+
+from abc import ABC, abstractmethod
+from typing import Callable, Dict, Tuple
+
+from flwr.client.clientapp import ClientApp
+from flwr.common.context import Context
+from flwr.common.message import Message
+from flwr.common.typing import ConfigsRecordValues
+
+BackendConfig = Dict[str, Dict[str, ConfigsRecordValues]]
+
+
+class Backend(ABC):
+    """Abstract base class for a Simulation Engine Backend."""
+
+    @abstractmethod
+    async def build(self) -> None:
+        """Build backend asynchronously.
+
+        Different components need to be in place before workers in a backend are ready
+        to accept jobs. When this method finishes executing, the backend should be fully
+        ready to run jobs.
+        """
+
+    @property
+    def num_workers(self) -> int:
+        """Return number of workers in the backend.
+
+        This is the number of TaskIns that can be processed concurrently.
+        """
+        return 0
+
+    @abstractmethod
+    def is_worker_idle(self) -> bool:
+        """Report whether a backend worker is idle and can therefore run a ClientApp."""
+
+    @abstractmethod
+    async def process_message(
+        self,
+        app: Callable[[], ClientApp],
+        message: Message,
+        context: Context,
+    ) -> Tuple[Message, Context]:
+        """Submit a job to the backend."""

From 52b1b090189392576b7127edafb0ae6d6e4691bb Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Fri, 23 Feb 2024 16:17:56 +0100
Subject: [PATCH 053/102] Migrate fed kaplan to flwr next (#2996)

Co-authored-by: Daniel J. Beutel <daniel@flower.ai>
---
 .../federated-kaplan-meier-fitter/README.md   |  29 ++++++-----
 .../_static/survival_function_federated.png   | Bin 47262 -> 51741 bytes
 .../centralized.py                            |   2 +-
 .../federated-kaplan-meier-fitter/client.py   |  45 ++++++++----------
 .../pyproject.toml                            |   2 +-
 .../requirements.txt                          |   2 +-
 examples/federated-kaplan-meier-fitter/run.sh |  17 -------
 .../federated-kaplan-meier-fitter/server.py   |  30 ++++++------
 8 files changed, 56 insertions(+), 71 deletions(-)
 delete mode 100755 examples/federated-kaplan-meier-fitter/run.sh

diff --git a/examples/federated-kaplan-meier-fitter/README.md b/examples/federated-kaplan-meier-fitter/README.md
index 29b2d2a0731d..1569467d6f82 100644
--- a/examples/federated-kaplan-meier-fitter/README.md
+++ b/examples/federated-kaplan-meier-fitter/README.md
@@ -35,7 +35,6 @@ This will create a new directory called `federated-kaplan-meier-fitter` containi
 -- client.py
 -- server.py
 -- centralized.py
--- run.sh
 -- README.md
 ```
 
@@ -68,24 +67,32 @@ pip install -r requirements.txt
 
 ## Run Federated Survival Analysis with Flower and lifelines's KaplanMeierFitter
 
-Afterwards you are ready to start the Flower server as well as the clients. You can simply start the server in a terminal as follows:
+### Start the long-running Flower server (SuperLink)
 
-```shell
-$ python3 server.py
+```bash
+flower-superlink --insecure
 ```
 
-Now you are ready to start the Flower clients which will participate in the learning. To do so simply open two more terminal windows and run the following commands.
+### Start the long-running Flower client (SuperNode)
 
-Start client 1 in the first terminal:
+In a new terminal window, start the first long-running Flower client:
 
-```shell
-$ python3 client.py --node-id 0
+```bash
+flower-client-app client:node_1_app --insecure
 ```
 
-Start client 2 in the second terminal:
+In yet another new terminal window, start the second long-running Flower client:
 
-```shell
-$ python3 client.py --node-id 1
+```bash
+flower-client-app client:node_2_app --insecure
+```
+
+### Run the Flower App
+
+With both the long-running server (SuperLink) and two clients (SuperNode) up and running, we can now run the actual Flower App:
+
+```bash
+flower-server-app server:app --insecure
 ```
 
 You will see that the server is printing survival function, median survival time and saves the plot with the survival function.
diff --git a/examples/federated-kaplan-meier-fitter/_static/survival_function_federated.png b/examples/federated-kaplan-meier-fitter/_static/survival_function_federated.png
index e761333fe42c05e6261cc362e8c45f8a4267e413..b7797f0879f24a7e0823ecfb254265222090cb13 100644
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diff --git a/examples/federated-kaplan-meier-fitter/centralized.py b/examples/federated-kaplan-meier-fitter/centralized.py
index 4d0aef4890cf..c94edfef9a18 100644
--- a/examples/federated-kaplan-meier-fitter/centralized.py
+++ b/examples/federated-kaplan-meier-fitter/centralized.py
@@ -12,5 +12,5 @@
     print(fitter.median_survival_time_)
     fitter.plot_survival_function()
     plt.title("Survival function of fruit flies (Walton's data)", fontsize=16)
-    plt.savefig("./survival_function_centralized.png", dpi=200)
+    plt.savefig("./_static/survival_function_centralized.png", dpi=200)
     print("Centralized survival function saved.")
diff --git a/examples/federated-kaplan-meier-fitter/client.py b/examples/federated-kaplan-meier-fitter/client.py
index 6f3040757481..948492efc575 100644
--- a/examples/federated-kaplan-meier-fitter/client.py
+++ b/examples/federated-kaplan-meier-fitter/client.py
@@ -1,4 +1,3 @@
-import argparse
 from typing import Dict, List, Tuple
 
 import flwr as fl
@@ -41,28 +40,26 @@ def fit(
         )
 
 
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Flower")
-    parser.add_argument(
-        "--node-id",
-        type=int,
-        required=True,
-        help="Node id. Each node holds different part of the dataset.",
-    )
-    args = parser.parse_args()
-    partition_id = args.node_id
+# Prepare data
+X = load_waltons()
+partitioner = NaturalIdPartitioner(partition_by="group")
+partitioner.dataset = Dataset.from_pandas(X)
 
-    # Prepare data
-    X = load_waltons()
-    partitioner = NaturalIdPartitioner(partition_by="group")
-    partitioner.dataset = Dataset.from_pandas(X)
-    partition = partitioner.load_partition(partition_id).to_pandas()
-    events = partition["E"].values
-    times = partition["T"].values
 
-    # Start Flower client
-    client = FlowerClient(times=times, events=events).to_client()
-    fl.client.start_client(
-        server_address="127.0.0.1:8080",
-        client=client,
-    )
+def get_client_fn(partition_id: int):
+    def client_fn(cid: str):
+        partition = partitioner.load_partition(partition_id).to_pandas()
+        events = partition["E"].values
+        times = partition["T"].values
+        return FlowerClient(times=times, events=events).to_client()
+
+    return client_fn
+
+
+# Run via `flower-client-app client:app`
+node_1_app = fl.client.ClientApp(
+    client_fn=get_client_fn(0),
+)
+node_2_app = fl.client.ClientApp(
+    client_fn=get_client_fn(1),
+)
diff --git a/examples/federated-kaplan-meier-fitter/pyproject.toml b/examples/federated-kaplan-meier-fitter/pyproject.toml
index 708ac3f482f8..8fe354ffb750 100644
--- a/examples/federated-kaplan-meier-fitter/pyproject.toml
+++ b/examples/federated-kaplan-meier-fitter/pyproject.toml
@@ -11,7 +11,7 @@ maintainers = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.9,<3.11"
-flwr = ">=1.0,<2.0"
+flwr-nightly = "*"
 flwr-datasets = ">=0.0.2,<1.0.0"
 numpy = ">=1.23.2"
 pandas = ">=2.0.0"
diff --git a/examples/federated-kaplan-meier-fitter/requirements.txt b/examples/federated-kaplan-meier-fitter/requirements.txt
index 1cad920ca048..cc8146545c7b 100644
--- a/examples/federated-kaplan-meier-fitter/requirements.txt
+++ b/examples/federated-kaplan-meier-fitter/requirements.txt
@@ -1,4 +1,4 @@
-flwr>=1.0, <2.0
+flwr-nightly
 flwr-datasets>=0.0.2, <1.0.0
 numpy>=1.23.2
 pandas>=2.0.0
diff --git a/examples/federated-kaplan-meier-fitter/run.sh b/examples/federated-kaplan-meier-fitter/run.sh
deleted file mode 100755
index b82ea3c58fbe..000000000000
--- a/examples/federated-kaplan-meier-fitter/run.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/bin/bash
-set -e
-cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
-
-echo "Starting server"
-python server.py &
-sleep 3  # Sleep for 3s to give the server enough time to start
-
-for i in $(seq 0 1); do
-    echo "Starting client $i"
-    python client.py --node-id "${i}" &
-done
-
-# This will allow you to use CTRL+C to stop all background processes
-trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM
-# Wait for all background processes to complete
-wait
diff --git a/examples/federated-kaplan-meier-fitter/server.py b/examples/federated-kaplan-meier-fitter/server.py
index 72f0e8f70429..141504ab59c0 100644
--- a/examples/federated-kaplan-meier-fitter/server.py
+++ b/examples/federated-kaplan-meier-fitter/server.py
@@ -97,6 +97,13 @@ def aggregate_fit(
         sorted_times = combined_times[args_sorted]
         sorted_events = combined_events[args_sorted]
         self.fitter.fit(sorted_times, sorted_events)
+        print("Survival function:")
+        print(self.fitter.survival_function_)
+        self.fitter.plot_survival_function()
+        plt.title("Survival function of fruit flies (Walton's data)", fontsize=16)
+        plt.savefig("./_static/survival_function_federated.png", dpi=200)
+        print("Mean survival time:")
+        print(self.fitter.median_survival_time_)
         return None, {}
 
     # The methods below return None or empty results.
@@ -129,19 +136,10 @@ def configure_evaluate(
         return []
 
 
-if __name__ == "__main__":
-    fitter = KaplanMeierFitter()  # You can choose other method that work on E, T data
-    strategy = EventTimeFitterStrategy(min_num_clients=2, fitter=fitter)
-    # Start Flower server
-    fl.server.start_server(
-        server_address="127.0.0.1:8080",
-        config=fl.server.ServerConfig(num_rounds=1),
-        strategy=strategy,
-    )
-    print("Survival function:")
-    print(strategy.fitter.survival_function_)
-    strategy.fitter.plot_survival_function()
-    plt.title("Survival function of fruit flies (Walton's data)", fontsize=16)
-    plt.savefig("./survival_function_federated.png", dpi=200)
-    print("Mean survival time:")
-    print(strategy.fitter.median_survival_time_)
+fitter = KaplanMeierFitter()  # You can choose other method that work on E, T data
+strategy = EventTimeFitterStrategy(min_num_clients=2, fitter=fitter)
+
+app = fl.server.ServerApp(
+    config=fl.server.ServerConfig(num_rounds=1),
+    strategy=strategy,
+)

From 334a9ff60d36dd07c01a0530a80323c071fb4115 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Fri, 23 Feb 2024 19:20:10 +0100
Subject: [PATCH 054/102] updated images (#2904)

---
 baselines/fedpara/README.md                    |   5 +++--
 baselines/fedpara/_static/Cifar100_iid.jpeg    | Bin 38597 -> 0 bytes
 baselines/fedpara/_static/Cifar100_iid.png     | Bin 0 -> 92309 bytes
 baselines/fedpara/_static/Cifar100_noniid.jpeg | Bin 39653 -> 0 bytes
 baselines/fedpara/_static/Cifar100_noniid.png  | Bin 0 -> 98057 bytes
 baselines/fedpara/_static/Cifar10_iid.jpeg     | Bin 39653 -> 0 bytes
 baselines/fedpara/_static/Cifar10_iid.png      | Bin 0 -> 91016 bytes
 baselines/fedpara/_static/Cifar10_noniid.jpeg  | Bin 40363 -> 0 bytes
 baselines/fedpara/_static/Cifar10_noniid.png   | Bin 0 -> 99211 bytes
 9 files changed, 3 insertions(+), 2 deletions(-)
 delete mode 100644 baselines/fedpara/_static/Cifar100_iid.jpeg
 create mode 100644 baselines/fedpara/_static/Cifar100_iid.png
 delete mode 100644 baselines/fedpara/_static/Cifar100_noniid.jpeg
 create mode 100644 baselines/fedpara/_static/Cifar100_noniid.png
 delete mode 100644 baselines/fedpara/_static/Cifar10_iid.jpeg
 create mode 100644 baselines/fedpara/_static/Cifar10_iid.png
 delete mode 100644 baselines/fedpara/_static/Cifar10_noniid.jpeg
 create mode 100644 baselines/fedpara/_static/Cifar10_noniid.png

diff --git a/baselines/fedpara/README.md b/baselines/fedpara/README.md
index 82efe5fac537..0ff9203a9a58 100644
--- a/baselines/fedpara/README.md
+++ b/baselines/fedpara/README.md
@@ -163,14 +163,15 @@ Communication costs as measured as described in the paper:
 
 |                    IID                     |                      Non-IID                      |
 | :----------------------------------------: | :-----------------------------------------------: |
-| ![Cifar100 iid](_static/Cifar100_iid.jpeg) | ![Cifar100 non-iid](_static/Cifar100_noniid.jpeg) |
+| ![Cifar100 iid](_static/Cifar100_iid.png) | ![Cifar100 non-iid](_static/Cifar100_noniid.png) |
 
 
 ### CIFAR-10 (Accuracy vs Communication Cost)
 
 |                   IID                    |                     Non-IID                     |
 | :--------------------------------------: | :---------------------------------------------: |
-| ![CIFAR10 iid](_static/Cifar10_iid.jpeg) | ![CIFAR10 non-iid](_static/Cifar10_noniid.jpeg) |
+| ![CIFAR10 iid](_static/Cifar10_iid.png) | ![CIFAR10 non-iid](_static/Cifar10_noniid.png) |
+
 
 ### NON-IID MINST (FedAvg vs FedPer vs pFedPara)
 
diff --git a/baselines/fedpara/_static/Cifar100_iid.jpeg b/baselines/fedpara/_static/Cifar100_iid.jpeg
deleted file mode 100644
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GIT binary patch
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zK-M%U4Z^#@2s(T0l(79}_28gTk_!<bdqrwBXgyqim|9$*r=lmI6upaCy$8AK%%}<E
zhvb!qda4oZ{Q%p4{dYZ3L?{;qxJ0{wT7TpTXA1`Nvit^}5$7H0V*NluN@C!CWzhv{
zW^+p$Llu5a;5y36@frO>6t+mBSR(_Zj2Y}SugfZ;cp`<Xh+a(MmHNRBwdI{zt!5g~
z1+iIZz<8)?_m2~Z)<qo>mnZ)q%1kt<V=+>`s1r#=|3iFeg(w3kCRb21fQOK%13MGK
zDb|8XaJQQD&KDqdF2d4PQhcHU!MlN0*CwW0{U)uSi)1;8wSDBCCWB1h^BF?NprRsF
zEr8leg=oOd0DF+W#sO1*4t9&YxavC4hn%!QLqcIcq!NH=15xXF#daAs>97~AE$W`A
zXzCd6@nX6DZ$w?!3g&-2R6HqscqjOF+!N$%8d8XCtM4zvuy#PE7}ZLWSrSKMR~g6%
z|MeV1Z4H`dRX>t8PV_>N@0*Qn*(Y8?-31uJs6hkG{76flhGxWpNN0@U!O;fAjhj)U
zBlYt7g-Es&NfWV=ldylJk$`8kvn*m6ZtYoH7ZPgDKo-lI`>kl|tY8AJFi4V6$>X3k
zEb6p7*Rt)QO)1%GU_D#<mybo*#AXLOFIP02yFzzA$oWHK7Nx;;w14bQq;!AGHruIZ
zMvs0JD*LX?{bz-bu6P~gGf!rKK7YTRPtF!+B$hE$U8qi_nHR(P7E4sDi13fPIrH@Q
z`J*1!kG){u?%HYkX#6QJixwVWi~o#N^_>}8XpsfoTTA^2n|9}`nVl1>Q=>b7WAOSe
zNdT|J_$BkLY)p?t*bUkG>+`R14M$h>C(;$3x2RW{f4g4APg9mqkDb1flP=4yaM6!(
zHSqpL4p(})C|2g+*RNOPOm57lr|*36R_d*z48<iZVns&>vKt~R13!ATPFC;hryBXu
zpm|$R8mT@EL8!|&TsXb2B>EQz(!zcJJ=WxX7Ou$Tl*K8R>mpp~a3Lojsm**GY4FwE
zfpakbC_hieo6f3`tXt3m4o#-NvS69FR-dt7)tmbqFs}S7uRSt2q9}*#7JTS3D@(J0
z1>2kse-viAr=j1X{w;wG@2vJGb`1oj$W)GP^KeVm77Y<SLC{6M9mj6B)N?uCv%gVg
zbSKIyA_lG|hTFud%yA~}Lgh<f%Cfn0U9trK9Cp)%3@f~|v$MZv=5Q39zu%Sf)!&#H
zr5ezS!dg_i9*e*s5ruguaJ{nRmu;_X?J(fJ{P1UO{|ju8n%$gRpUy0uEt`e^G#Qxx
zD;p(0{I4Pu-@%_>inC857Bcp;qk(TWR}*~XzsK18=TbzfT*)ZloBz)TPR#I9*WHmH
THyUu`!52l>cppdSc+md<jbz2%

literal 0
HcmV?d00001


From de5af249a8036ccc931fbe98e9517390dc747d1a Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Fri, 23 Feb 2024 19:35:40 +0100
Subject: [PATCH 055/102] Add `RayBackend` and `SimpleActorPool` (#2997)

---
 .../superlink/fleet/vce/backend/__init__.py   |  26 +++
 .../superlink/fleet/vce/backend/backend.py    |   3 +
 .../superlink/fleet/vce/backend/raybackend.py | 153 ++++++++++++++++++
 .../server/superlink/fleet/vce/vce_api.py     |  25 ++-
 .../simulation/ray_transport/ray_actor.py     |  77 ++++++++-
 5 files changed, 280 insertions(+), 4 deletions(-)
 create mode 100644 src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py

diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/__init__.py b/src/py/flwr/server/superlink/fleet/vce/backend/__init__.py
index 98e7a2758f2e..d751cf4bcae1 100644
--- a/src/py/flwr/server/superlink/fleet/vce/backend/__init__.py
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/__init__.py
@@ -14,8 +14,34 @@
 # ==============================================================================
 """Simulation Engine Backends."""
 
+import importlib
+from typing import Dict, Type
+
 from .backend import Backend, BackendConfig
 
+is_ray_installed = importlib.util.find_spec("ray") is not None
+
+# Mapping of supported backends
+supported_backends: Dict[str, Type[Backend]] = {}
+
+# To log backend-specific error message when chosen backend isn't available
+error_messages_backends: Dict[str, str] = {}
+
+if is_ray_installed:
+    from .raybackend import RayBackend
+
+    supported_backends["ray"] = RayBackend
+else:
+    error_messages_backends[
+        "ray"
+    ] = """Unable to import module `ray`.
+
+    To install the necessary dependencies, install `flwr` with the `simulation` extra:
+
+        pip install -U flwr["simulation"]
+    """
+
+
 __all__ = [
     "Backend",
     "BackendConfig",
diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/backend.py b/src/py/flwr/server/superlink/fleet/vce/backend/backend.py
index b6cddf2eefa5..2df4be76e7a0 100644
--- a/src/py/flwr/server/superlink/fleet/vce/backend/backend.py
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/backend.py
@@ -29,6 +29,9 @@
 class Backend(ABC):
     """Abstract base class for a Simulation Engine Backend."""
 
+    def __init__(self, backend_config: BackendConfig, work_dir: str) -> None:
+        """Construct a backend."""
+
     @abstractmethod
     async def build(self) -> None:
         """Build backend asynchronously.
diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
new file mode 100644
index 000000000000..24620aab083f
--- /dev/null
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
@@ -0,0 +1,153 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Ray backend for the Fleet API using the Simulation Engine."""
+
+import asyncio
+import pathlib
+from logging import INFO
+from typing import Callable, Dict, List, Tuple, Union
+
+from flwr.client.clientapp import ClientApp
+from flwr.common.context import Context
+from flwr.common.logger import log
+from flwr.common.message import Message
+from flwr.simulation.ray_transport.ray_actor import (
+    BasicActorPool,
+    ClientAppActor,
+    init_ray,
+)
+
+from .backend import Backend, BackendConfig
+
+ClientResourcesDict = Dict[str, Union[int, float]]
+
+
+class RayBackend(Backend):
+    """A backend that submits jobs to a `BasicActorPool`."""
+
+    def __init__(
+        self,
+        backend_config: BackendConfig,
+        work_dir: str,
+    ) -> None:
+        """Prepare RayBackend by initialising Ray and creating the ActorPool."""
+        log(INFO, "Initialising: %s", self.__class__.__name__)
+        log(INFO, "Backend config: %s", backend_config)
+
+        # Init ray and append working dir if needed
+        runtime_env = (
+            self._configure_runtime_env(work_dir=work_dir) if work_dir else None
+        )
+        init_ray(runtime_env=runtime_env)
+
+        # Validate client resources
+        self.client_resources_key = "client_resources"
+
+        # Create actor pool
+        actor_kwargs = backend_config.get("actor_kwargs", {})
+        client_resources = self._validate_client_resources(config=backend_config)
+        self.pool = BasicActorPool(
+            actor_type=ClientAppActor,
+            client_resources=client_resources,
+            actor_kwargs=actor_kwargs,
+        )
+
+    def _configure_runtime_env(self, work_dir: str) -> Dict[str, Union[str, List[str]]]:
+        """Return list of files/subdirectories to exclude relative to work_dir.
+
+        Without this, Ray will push everything to the Ray Cluster.
+        """
+        runtime_env: Dict[str, Union[str, List[str]]] = {"working_dir": work_dir}
+
+        excludes = []
+        path = pathlib.Path(work_dir)
+        for p in path.rglob("*"):
+            # Exclude files need to be relative to the working_dir
+            if p.is_file() and not str(p).endswith(".py"):
+                excludes.append(str(p.relative_to(path)))
+        runtime_env["excludes"] = excludes
+
+        return runtime_env
+
+    def _validate_client_resources(self, config: BackendConfig) -> ClientResourcesDict:
+        client_resources_config = config.get(self.client_resources_key)
+        client_resources: ClientResourcesDict = {}
+        valid_types = (int, float)
+        if client_resources_config:
+            for k, v in client_resources_config.items():
+                if not isinstance(k, str):
+                    raise ValueError(
+                        f"client resources keys are expected to be `str` but you used "
+                        f"{type(k)} for `{k}`"
+                    )
+                if not isinstance(v, valid_types):
+                    raise ValueError(
+                        f"client resources are expected to be of type {valid_types} "
+                        f"but found `{type(v)}` for key `{k}`",
+                    )
+                client_resources[k] = v
+
+        else:
+            client_resources = {"num_cpus": 2, "num_gpus": 0.0}
+            log(
+                INFO,
+                "`%s` not specified in backend config. Applying default setting: %s",
+                self.client_resources_key,
+                client_resources,
+            )
+
+        return client_resources
+
+    @property
+    def num_workers(self) -> int:
+        """Return number of actors in pool."""
+        return self.pool.num_actors
+
+    def is_worker_idle(self) -> bool:
+        """Report whether the pool has idle actors."""
+        return self.pool.is_actor_available()
+
+    async def build(self) -> None:
+        """Build pool of Ray actors that this backend will submit jobs to."""
+        await self.pool.add_actors_to_pool(self.pool.actors_capacity)
+        log(INFO, "Constructed ActorPool with: %i actors", self.pool.num_actors)
+
+    async def process_message(
+        self,
+        app: Callable[[], ClientApp],
+        message: Message,
+        context: Context,
+    ) -> Tuple[Message, Context]:
+        """Run ClientApp that process a given message.
+
+        Return output message and updated context.
+        """
+        node_id = message.metadata.dst_node_id
+
+        # Submite a task to the pool
+        future = await self.pool.submit(
+            lambda a, a_fn, mssg, cid, state: a.run.remote(a_fn, mssg, cid, state),
+            (app, message, str(node_id), context),
+        )
+
+        await asyncio.wait([future])
+
+        # Fetch result
+        (
+            out_mssg,
+            updated_context,
+        ) = await self.pool.fetch_result_and_return_actor_to_pool(future)
+
+        return out_mssg, updated_context
diff --git a/src/py/flwr/server/superlink/fleet/vce/vce_api.py b/src/py/flwr/server/superlink/fleet/vce/vce_api.py
index 8c76b401b915..c91bae9ddabb 100644
--- a/src/py/flwr/server/superlink/fleet/vce/vce_api.py
+++ b/src/py/flwr/server/superlink/fleet/vce/vce_api.py
@@ -15,7 +15,7 @@
 """Fleet VirtualClientEngine API."""
 
 import json
-from logging import INFO
+from logging import ERROR, INFO
 from typing import Dict
 
 from flwr.client.clientapp import ClientApp, load_client_app
@@ -23,6 +23,8 @@
 from flwr.common.logger import log
 from flwr.server.superlink.state import StateFactory
 
+from .backend import error_messages_backends, supported_backends
+
 NodeToPartitionMapping = Dict[int, int]
 
 
@@ -60,9 +62,26 @@ def start_vce(
         node_states[node_id] = NodeState()
 
     # Load backend config
-    _ = json.loads(backend_config_json_stream)
+    log(INFO, "Supported backends: %s", list(supported_backends.keys()))
+    backend_config = json.loads(backend_config_json_stream)
+
+    try:
+        backend_type = supported_backends[backend_name]
+        _ = backend_type(backend_config, work_dir=working_dir)
+    except KeyError as ex:
+        log(
+            ERROR,
+            "Backend `%s`, is not supported. Use any of %s or add support "
+            "for a new backend.",
+            backend_name,
+            list(supported_backends.keys()),
+        )
+        if backend_name in error_messages_backends:
+            log(ERROR, error_messages_backends[backend_name])
+
+        raise ex
 
-    log(INFO, "client_app_str = %s", client_app_module_name)
+    log(INFO, "client_app_module_name = %s", client_app_module_name)
 
     def _load() -> ClientApp:
         app: ClientApp = load_client_app(client_app_module_name)
diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py
index 70a220dc2a19..e899ce282618 100644
--- a/src/py/flwr/simulation/ray_transport/ray_actor.py
+++ b/src/py/flwr/simulation/ray_transport/ray_actor.py
@@ -14,7 +14,7 @@
 # ==============================================================================
 """Ray-based Flower Actor and ActorPool implementation."""
 
-
+import asyncio
 import threading
 import traceback
 from abc import ABC
@@ -414,3 +414,78 @@ def get_client_result(
         # Fetch result belonging to the VirtualClient calling this method
         # Return both result from tasks and (potentially) updated run context
         return self._fetch_future_result(cid)
+
+
+def init_ray(*args: Any, **kwargs: Any) -> None:
+    """Intialises Ray if not already initialised."""
+    if not ray.is_initialized():
+        ray.init(*args, **kwargs)
+
+
+class BasicActorPool:
+    """A basic actor pool."""
+
+    def __init__(
+        self,
+        actor_type: Type[VirtualClientEngineActor],
+        client_resources: Dict[str, Union[int, float]],
+        actor_kwargs: Dict[str, Any],
+    ):
+        self.client_resources = client_resources
+
+        # Queue of idle actors
+        self.pool: "asyncio.Queue[Type[VirtualClientEngineActor]]" = asyncio.Queue()
+        self.num_actors = 0
+
+        # Resolve arguments to pass during actor init
+        actor_args = {} if actor_kwargs is None else actor_kwargs
+
+        # A function that creates an actor
+        self.create_actor_fn = lambda: actor_type.options(  # type: ignore
+            **client_resources
+        ).remote(**actor_args)
+
+        # Figure out how many actors can be created given the cluster resources
+        # and the resources the user indicates each VirtualClient will need
+        self.actors_capacity = pool_size_from_resources(client_resources)
+        self._future_to_actor: Dict[Any, Type[VirtualClientEngineActor]] = {}
+
+    def is_actor_available(self) -> bool:
+        """Return true if there is an idle actor."""
+        return self.pool.qsize() > 0
+
+    async def add_actors_to_pool(self, num_actors: int) -> None:
+        """Add actors to the pool.
+
+        This method may be executed also if new resources are added to your Ray cluster
+        (e.g. you add a new node).
+        """
+        for _ in range(num_actors):
+            await self.pool.put(self.create_actor_fn())  # type: ignore
+        self.num_actors += num_actors
+
+    async def submit(
+        self, actor_fn: Any, job: Tuple[ClientAppFn, Message, str, Context]
+    ) -> Any:
+        """On idle actor, submit job and return future."""
+        # Remove idle actor from pool
+        actor = await self.pool.get()
+        # Submit job to actor
+        app_fn, mssg, cid, context = job
+        future = actor_fn(actor, app_fn, mssg, cid, context)
+        # Keep track of future:actor (so we can fetch the actor upon job completion
+        # and add it back to the pool)
+        self._future_to_actor[future] = actor
+        return future
+
+    async def fetch_result_and_return_actor_to_pool(
+        self, future: Any
+    ) -> Tuple[Message, Context]:
+        """Pull result given a future and add actor back to pool."""
+        # Get actor that ran job
+        actor = self._future_to_actor.pop(future)
+        await self.pool.put(actor)
+        # Retrieve result for object store
+        # Instead of doing ray.get(future) we await it
+        _, out_mssg, updated_context = await future
+        return out_mssg, updated_context

From 2b0c33fa6429d876966995db0df9f56939bf3292 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Mon, 26 Feb 2024 11:18:30 +0100
Subject: [PATCH 056/102] Introduce Backend.terminate and asyncio event to stop
 (#3008)

---
 .../superlink/fleet/vce/backend/backend.py     |  4 ++++
 .../superlink/fleet/vce/backend/raybackend.py  |  9 ++++++++-
 .../flwr/server/superlink/fleet/vce/vce_api.py |  4 +++-
 .../flwr/simulation/ray_transport/ray_actor.py | 18 +++++++++++++++---
 4 files changed, 30 insertions(+), 5 deletions(-)

diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/backend.py b/src/py/flwr/server/superlink/fleet/vce/backend/backend.py
index 2df4be76e7a0..f2796a5758a0 100644
--- a/src/py/flwr/server/superlink/fleet/vce/backend/backend.py
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/backend.py
@@ -53,6 +53,10 @@ def num_workers(self) -> int:
     def is_worker_idle(self) -> bool:
         """Report whether a backend worker is idle and can therefore run a ClientApp."""
 
+    @abstractmethod
+    async def terminate(self) -> None:
+        """Terminate backend."""
+
     @abstractmethod
     async def process_message(
         self,
diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
index 24620aab083f..cc3cf4348491 100644
--- a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
@@ -28,6 +28,7 @@
     ClientAppActor,
     init_ray,
 )
+from flwr.simulation.ray_transport.utils import enable_tf_gpu_growth
 
 from .backend import Backend, BackendConfig
 
@@ -56,7 +57,9 @@ def __init__(
         self.client_resources_key = "client_resources"
 
         # Create actor pool
-        actor_kwargs = backend_config.get("actor_kwargs", {})
+        use_tf = backend_config.get("tensorflow", False)
+        actor_kwargs = {"on_actor_init_fn": enable_tf_gpu_growth} if use_tf else {}
+
         client_resources = self._validate_client_resources(config=backend_config)
         self.pool = BasicActorPool(
             actor_type=ClientAppActor,
@@ -151,3 +154,7 @@ async def process_message(
         ) = await self.pool.fetch_result_and_return_actor_to_pool(future)
 
         return out_mssg, updated_context
+
+    async def terminate(self) -> None:
+        """Terminate all actors in actor pool."""
+        await self.pool.terminate_all_actors()
diff --git a/src/py/flwr/server/superlink/fleet/vce/vce_api.py b/src/py/flwr/server/superlink/fleet/vce/vce_api.py
index c91bae9ddabb..666e7e7d9ec3 100644
--- a/src/py/flwr/server/superlink/fleet/vce/vce_api.py
+++ b/src/py/flwr/server/superlink/fleet/vce/vce_api.py
@@ -14,9 +14,10 @@
 # ==============================================================================
 """Fleet VirtualClientEngine API."""
 
+import asyncio
 import json
 from logging import ERROR, INFO
-from typing import Dict
+from typing import Dict, Optional
 
 from flwr.client.clientapp import ClientApp, load_client_app
 from flwr.client.node_state import NodeState
@@ -49,6 +50,7 @@ def start_vce(
     backend_config_json_stream: str,
     state_factory: StateFactory,
     working_dir: str,
+    f_stop: Optional[asyncio.Event] = None,
 ) -> None:
     """Start Fleet API with the VirtualClientEngine (VCE)."""
     # Register SuperNodes
diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py
index e899ce282618..5ac0b2c27484 100644
--- a/src/py/flwr/simulation/ray_transport/ray_actor.py
+++ b/src/py/flwr/simulation/ray_transport/ray_actor.py
@@ -18,7 +18,7 @@
 import threading
 import traceback
 from abc import ABC
-from logging import ERROR, WARNING
+from logging import DEBUG, ERROR, WARNING
 from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
 
 import ray
@@ -46,7 +46,7 @@ class VirtualClientEngineActor(ABC):
 
     def terminate(self) -> None:
         """Manually terminate Actor object."""
-        log(WARNING, "Manually terminating %s}", self.__class__.__name__)
+        log(WARNING, "Manually terminating %s", self.__class__.__name__)
         ray.actor.exit_actor()
 
     def run(
@@ -434,7 +434,9 @@ def __init__(
         self.client_resources = client_resources
 
         # Queue of idle actors
-        self.pool: "asyncio.Queue[Type[VirtualClientEngineActor]]" = asyncio.Queue()
+        self.pool: "asyncio.Queue[Type[VirtualClientEngineActor]]" = asyncio.Queue(
+            maxsize=1024
+        )
         self.num_actors = 0
 
         # Resolve arguments to pass during actor init
@@ -464,6 +466,16 @@ async def add_actors_to_pool(self, num_actors: int) -> None:
             await self.pool.put(self.create_actor_fn())  # type: ignore
         self.num_actors += num_actors
 
+    async def terminate_all_actors(self) -> None:
+        """Terminate actors in pool."""
+        num_terminated = 0
+        while self.pool.qsize():
+            actor = await self.pool.get()
+            actor.terminate.remote()  # type: ignore
+            num_terminated += 1
+
+        log(DEBUG, "Terminated %i actors", num_terminated)
+
     async def submit(
         self, actor_fn: Any, job: Tuple[ClientAppFn, Message, str, Context]
     ) -> Any:

From ad94eaf0621de776a4a0a2645e41f27bc536b536 Mon Sep 17 00:00:00 2001
From: Sebastian van der Voort <Svdvoort@users.noreply.github.com>
Date: Mon, 26 Feb 2024 11:45:06 +0100
Subject: [PATCH 057/102] Replace dead links, typo fixes and general
 improvements in framework docs (#2989)

Co-authored-by: svdvoort <23049683+Svdvoort@users.noreply.github.com>
---
 doc/Makefile                                  |   6 ++
 ...contributor-how-to-build-docker-images.rst |   2 +-
 ...ributor-how-to-contribute-translations.rst |   4 +-
 ...contributor-how-to-create-new-messages.rst |   4 +-
 ...ow-to-develop-in-vscode-dev-containers.rst |   6 +-
 ...ntributor-ref-good-first-contributions.rst |   4 +-
 ...tributor-tutorial-contribute-on-github.rst |  35 +++---
 ...-tutorial-get-started-as-a-contributor.rst |   6 +-
 ...-pytorch-from-centralized-to-federated.rst |  10 +-
 .../example-walkthrough-pytorch-mnist.rst     | 100 +++++++++---------
 doc/source/how-to-configure-clients.rst       |   2 +-
 doc/source/how-to-install-flower.rst          |   2 +-
 doc/source/how-to-monitor-simulation.rst      |   4 +-
 doc/source/ref-example-projects.rst           |  10 +-
 doc/source/ref-faq.rst                        |   4 +-
 .../tutorial-quickstart-huggingface.rst       |  47 ++++----
 doc/source/tutorial-quickstart-ios.rst        |   6 +-
 doc/source/tutorial-quickstart-mxnet.rst      |   4 +-
 doc/source/tutorial-quickstart-pytorch.rst    |   2 +-
 .../tutorial-quickstart-scikitlearn.rst       |   8 +-
 doc/source/tutorial-quickstart-xgboost.rst    |   4 +-
 ...uild-a-strategy-from-scratch-pytorch.ipynb |  10 +-
 ...-series-customize-the-client-pytorch.ipynb |   8 +-
 ...ries-get-started-with-flower-pytorch.ipynb |   4 +-
 ...-federated-learning-strategy-pytorch.ipynb |   4 +-
 ...al-series-what-is-federated-learning.ipynb |   6 +-
 src/py/flwr/server/driver/driver.py           |   1 +
 27 files changed, 154 insertions(+), 149 deletions(-)

diff --git a/doc/Makefile b/doc/Makefile
index 085fe301e162..0e11bb3b4b3c 100644
--- a/doc/Makefile
+++ b/doc/Makefile
@@ -7,6 +7,7 @@ SPHINXOPTS    ?=
 SPHINXBUILD   ?= sphinx-build
 SOURCEDIR     = source
 BUILDDIR      = build
+LINKCHECKDIR  = build/linkcheck
 
 # Put it first so that "make" without argument is like "make help".
 help:
@@ -29,6 +30,11 @@ serve:
 	make docs
 	python -m http.server --directory build/html
 
+checklinks:
+	$(SPHINXBUILD) -b linkcheck "$(SOURCEDIR)" "$(LINKCHECKDIR)"
+	@echo
+	@echo "Check finished. Report is in $(LINKCHECKDIR)."
+
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
diff --git a/doc/source/contributor-how-to-build-docker-images.rst b/doc/source/contributor-how-to-build-docker-images.rst
index 3beae7422bef..5dead265bee2 100644
--- a/doc/source/contributor-how-to-build-docker-images.rst
+++ b/doc/source/contributor-how-to-build-docker-images.rst
@@ -17,7 +17,7 @@ Before we can start, we need to meet a few prerequisites in our local developmen
 #. Verify the Docker daemon is running.
 
     Please follow the first section on
-    `Run Flower using Docker <https://flower.ai/docs/framework/how-to-run-flower-using-docker>`_
+    :doc:`Run Flower using Docker <how-to-run-flower-using-docker>`
     which covers this step in more detail.
 
 Currently, Flower provides two images, a base image and a server image. There will also be a client
diff --git a/doc/source/contributor-how-to-contribute-translations.rst b/doc/source/contributor-how-to-contribute-translations.rst
index 1614b8e5a040..ba59901cf1c4 100644
--- a/doc/source/contributor-how-to-contribute-translations.rst
+++ b/doc/source/contributor-how-to-contribute-translations.rst
@@ -8,7 +8,7 @@ the translations are often imperfect. If you speak languages other than
 English, you might be able to help us in our effort to make Federated Learning
 accessible to as many people as possible by contributing to those translations!
 This might also be a great opportunity for those wanting to become open source
-contributors with little prerequistes.
+contributors with little prerequisites.
 
 Our translation project is publicly available over on `Weblate
 <https://hosted.weblate.org/projects/flower-docs/framework/>`_, this where most
@@ -44,7 +44,7 @@ This is what the interface looks like:
 
   .. image:: _static/weblate_interface.png
 
-You input your translation in the textbox at the top and then, once you are
+You input your translation in the text box at the top and then, once you are
 happy with it, you either press ``Save and continue`` (to save the translation
 and go to the next untranslated string), ``Save and stay`` (to save the
 translation and stay on the same page), ``Suggest`` (to add your translation to
diff --git a/doc/source/contributor-how-to-create-new-messages.rst b/doc/source/contributor-how-to-create-new-messages.rst
index 24fa5f573158..5d9f4600361c 100644
--- a/doc/source/contributor-how-to-create-new-messages.rst
+++ b/doc/source/contributor-how-to-create-new-messages.rst
@@ -29,8 +29,8 @@ Let's now see what we need to implement in order to get this simple function bet
 Message Types for Protocol Buffers
 ----------------------------------
 
-The first thing we need to do is to define a message type for the RPC system in :code:`transport.proto`. 
-Note that we have to do it for both the request and response messages. For more details on the syntax of proto3, please see the  `official documentation <https://developers.google.com/protocol-buffers/docs/proto3>`_.
+The first thing we need to do is to define a message type for the RPC system in :code:`transport.proto`.
+Note that we have to do it for both the request and response messages. For more details on the syntax of proto3, please see the  `official documentation <https://protobuf.dev/programming-guides/proto3/>`_.
 
 Within the :code:`ServerMessage` block:
 
diff --git a/doc/source/contributor-how-to-develop-in-vscode-dev-containers.rst b/doc/source/contributor-how-to-develop-in-vscode-dev-containers.rst
index 19d46c5753c6..c861457b6edc 100644
--- a/doc/source/contributor-how-to-develop-in-vscode-dev-containers.rst
+++ b/doc/source/contributor-how-to-develop-in-vscode-dev-containers.rst
@@ -8,7 +8,7 @@ When working on the Flower framework we want to ensure that all contributors use
 
   Workspace files are mounted from the local file system or copied or cloned into the container. Extensions are installed and run inside the container, where they have full access to the tools, platform, and file system. This means that you can seamlessly switch your entire development environment just by connecting to a different container.
 
-Source: `Official VSCode documentation <https://code.visualstudio.com/docs/remote/containers>`_
+Source: `Official VSCode documentation <https://code.visualstudio.com/docs/devcontainers/containers>`_
 
 
 Getting started
@@ -20,5 +20,5 @@ Now you should be good to go. When starting VSCode, it will ask you to run in th
 
 In some cases your setup might be more involved. For those cases consult the following sources:
 
-* `Developing inside a Container <https://code.visualstudio.com/docs/remote/containers#_system-requirements>`_
-* `Remote development in Containers <https://code.visualstudio.com/docs/remote/containers-tutorial>`_
+* `Developing inside a Container <https://code.visualstudio.com/docs/devcontainers/containers#_system-requirements>`_
+* `Remote development in Containers <https://code.visualstudio.com/docs/devcontainers/tutorial>`_
diff --git a/doc/source/contributor-ref-good-first-contributions.rst b/doc/source/contributor-ref-good-first-contributions.rst
index cbf21e2845bc..2b8ce88413f5 100644
--- a/doc/source/contributor-ref-good-first-contributions.rst
+++ b/doc/source/contributor-ref-good-first-contributions.rst
@@ -22,11 +22,11 @@ are:
 Request for Flower Baselines
 ----------------------------
 
-If you are not familiar with Flower Baselines, you should probably check-out our `contributing guide for baselines <https://flower.ai/docs/contributing-baselines.html>`_.
+If you are not familiar with Flower Baselines, you should probably check-out our `contributing guide for baselines <https://flower.ai/docs/baselines/how-to-contribute-baselines.html>`_.
 
 You should then check out the open
 `issues <https://github.com/adap/flower/issues?q=is%3Aopen+is%3Aissue+label%3A%22new+baseline%22>`_ for baseline requests.
-If you find a baseline that you'd like to work on and that has no assignes, feel free to assign it to yourself and start working on it!
+If you find a baseline that you'd like to work on and that has no assignees, feel free to assign it to yourself and start working on it!
 
 Otherwise, if you don't find a baseline you'd like to work on, be sure to open a new issue with the baseline request template!
 
diff --git a/doc/source/contributor-tutorial-contribute-on-github.rst b/doc/source/contributor-tutorial-contribute-on-github.rst
index 273b47a636cc..6da81ce73662 100644
--- a/doc/source/contributor-tutorial-contribute-on-github.rst
+++ b/doc/source/contributor-tutorial-contribute-on-github.rst
@@ -3,8 +3,7 @@ Contribute on GitHub
 
 This guide is for people who want to get involved with Flower, but who are not used to contributing to GitHub projects.
 
-If you're familiar with how contributing on GitHub works, you can directly checkout our
-`getting started guide for contributors <https://flower.ai/docs/contributor-tutorial-get-started-as-a-contributor.html>`_.
+If you're familiar with how contributing on GitHub works, you can directly checkout our :doc:`getting started guide for contributors <contributor-tutorial-get-started-as-a-contributor>`.
 
 
 Setting up the repository
@@ -12,7 +11,7 @@ Setting up the repository
 
 1. **Create a GitHub account and setup Git**
     Git is a distributed version control tool. This allows for an entire codebase's history to be stored and every developer's machine.
-    It is a software that will need to be installed on your local machine, you can follow this `guide <https://docs.github.com/en/get-started/quickstart/set-up-git>`_ to set it up.
+    It is a software that will need to be installed on your local machine, you can follow this `guide <https://docs.github.com/en/get-started/getting-started-with-git/set-up-git>`_ to set it up.
 
     GitHub, itself, is a code hosting platform for version control and collaboration. It allows for everyone to collaborate and work from anywhere on remote repositories.
 
@@ -22,7 +21,7 @@ Setting up the repository
     you download code from a remote repository on GitHub, make changes locally and keep track of them using Git and then you upload your new history back to GitHub.
 
 2. **Forking the Flower repository**
-    A fork is a personal copy of a GitHub repository. To create one for Flower, you must navigate to https://github.com/adap/flower (while connected to your GitHub account)
+    A fork is a personal copy of a GitHub repository. To create one for Flower, you must navigate to `<https://github.com/adap/flower>`_ (while connected to your GitHub account)
     and click the ``Fork`` button situated on the top right of the page.
 
     .. image:: _static/fork_button.png
@@ -68,7 +67,7 @@ Setting up the repository
 
 5. **Add upstream**
     Now we will add an upstream address to our repository.
-    Still in the same directroy, we must run the following command:
+    Still in the same directory, we must run the following command:
 
     .. code-block:: shell
 
@@ -93,7 +92,7 @@ Setting up the repository
 Setting up the coding environment
 ---------------------------------
 
-This can be achieved by following this `getting started guide for contributors`_ (note that you won't need to clone the repository).
+This can be achieved by following this :doc:`getting started guide for contributors <contributor-tutorial-get-started-as-a-contributor>` (note that you won't need to clone the repository).
 Once you are able to write code and test it, you can finally start making changes!
 
 
@@ -256,28 +255,28 @@ Example of first contribution
 Problem
 *******
 
-For our documentation, we’ve started to use the `Diàtaxis framework <https://diataxis.fr/>`_.
+For our documentation, we've started to use the `Diàtaxis framework <https://diataxis.fr/>`_.
 
-Our “How to” guides should have titles that continue the sencence “How to …”, for example, “How to upgrade to Flower 1.0”.
+Our "How to" guides should have titles that continue the sentence "How to …", for example, "How to upgrade to Flower 1.0".
 
 Most of our guides do not follow this new format yet, and changing their title is (unfortunately) more involved than one might think.
 
-This issue is about changing the title of a doc from present continious to present simple.
+This issue is about changing the title of a doc from present continuous to present simple.
 
-Let's take the example of “Saving Progress” which we changed to “Save Progress”. Does this pass our check?
+Let's take the example of "Saving Progress" which we changed to "Save Progress". Does this pass our check?
 
-Before: ”How to saving progress” ❌
+Before: "How to saving progress" ❌
 
-After: ”How to save progress” ✅
+After: "How to save progress" ✅
 
 Solution
 ********
 
-This is a tiny change, but it’ll allow us to test your end-to-end setup. After cloning and setting up the Flower repo, here’s what you should do:
+This is a tiny change, but it'll allow us to test your end-to-end setup. After cloning and setting up the Flower repo, here's what you should do:
 
 - Find the source file in ``doc/source``
 - Make the change in the ``.rst`` file (beware, the dashes under the title should be the same length as the title itself)
-- Build the docs and check the result: `<https://flower.ai/docs/writing-documentation.html#edit-an-existing-page>`_
+- Build the docs and `check the result <contributor-how-to-write-documentation.html#edit-an-existing-page>`_
 
 Rename file
 :::::::::::
@@ -285,7 +284,7 @@ Rename file
 You might have noticed that the file name still reflects the old wording.
 If we just change the file, then we break all existing links to it - it is **very important** to avoid that, breaking links can harm our search engine ranking.
 
-Here’s how to change the file name:
+Here's how to change the file name:
 
 - Change the file name to ``save-progress.rst``
 - Add a redirect rule to ``doc/source/conf.py``
@@ -303,7 +302,7 @@ This is where we define the whole arborescence of the navbar.
 Open PR
 :::::::
 
-- Commit the changes (commit messages are always imperative: “Do something”, in this case “Change …”)
+- Commit the changes (commit messages are always imperative: "Do something", in this case "Change …")
 - Push the changes to your fork
 - Open a PR (as shown above)
 - Wait for it to be approved!
@@ -343,7 +342,7 @@ Next steps
 
 Once you have made your first PR, and want to contribute more, be sure to check out the following :
 
-- `Good first contributions <https://flower.ai/docs/framework/contributor-ref-good-first-contributions.html>`_, where you should particularly look into the :code:`baselines` contributions.
+- :doc:`Good first contributions <contributor-ref-good-first-contributions>`, where you should particularly look into the :code:`baselines` contributions.
 
 
 Appendix
@@ -361,7 +360,7 @@ Above this header you should see the following comment that explains how to writ
     Inside the following 'Changelog entry' section,
     you should put the description of your changes that will be added to the changelog alongside your PR title.
 
-    If the section is completely empty (without any token) or non-existant,
+    If the section is completely empty (without any token) or non-existent,
     the changelog will just contain the title of the PR for the changelog entry, without any description.
 
     If the section contains some text other than tokens, it will use it to add a description to the change.
diff --git a/doc/source/contributor-tutorial-get-started-as-a-contributor.rst b/doc/source/contributor-tutorial-get-started-as-a-contributor.rst
index 72c6df5fdbc7..01810c7244d3 100644
--- a/doc/source/contributor-tutorial-get-started-as-a-contributor.rst
+++ b/doc/source/contributor-tutorial-get-started-as-a-contributor.rst
@@ -11,7 +11,7 @@ Prerequisites
 
 Flower uses :code:`pyproject.toml` to manage dependencies and configure
 development tools (the ones which support it). Poetry is a build tool which
-supports `PEP 517 <https://www.python.org/dev/peps/pep-0517/>`_.
+supports `PEP 517 <https://peps.python.org/pep-0517/>`_.
 
 
 Developer Machine Setup
@@ -27,7 +27,7 @@ For macOS
 * Install `homebrew <https://brew.sh/>`_. Don't forget the post-installation actions to add `brew` to your PATH.
 * Install `xz` (to install different Python versions) and `pandoc` to build the
   docs::
-    
+
   $ brew install xz pandoc
 
 For Ubuntu
@@ -54,7 +54,7 @@ GitHub::
 * If you don't have :code:`pyenv` installed, the following script that will install it, set it up, and create the virtual environment (with :code:`Python 3.8.17` by default)::
 
   $ ./dev/setup-defaults.sh <version> # once completed, run the bootstrap script
-  
+
 * If you already have :code:`pyenv` installed (along with the :code:`pyenv-virtualenv` plugin), you can use the following convenience script (with :code:`Python 3.8.17` by default)::
 
   $ ./dev/venv-create.sh <version> # once completed, run the `bootstrap.sh` script
diff --git a/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst b/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst
index 5d4dac0c0cda..0139f3b8dc31 100644
--- a/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst
+++ b/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst
@@ -3,11 +3,11 @@ Example: FedBN in PyTorch - From Centralized To Federated
 
 This tutorial will show you how to use Flower to build a federated version of an existing machine learning workload with `FedBN <https://github.com/med-air/FedBN>`_, a federated training strategy designed for non-iid data.
 We are using PyTorch to train a Convolutional Neural Network(with Batch Normalization layers) on the CIFAR-10 dataset.
-When applying FedBN, only few changes needed compared to `Example: PyTorch - From Centralized To Federated <https://flower.ai/docs/examples/pytorch-from-centralized-to-federated.html>`_.
+When applying FedBN, only few changes needed compared to :doc:`Example: PyTorch - From Centralized To Federated <example-pytorch-from-centralized-to-federated>`.
 
 Centralized Training
 --------------------
-All files are revised based on `Example: PyTorch - From Centralized To Federated <https://flower.ai/docs/examples/pytorch-from-centralized-to-federated.html>`_.
+All files are revised based on :doc:`Example: PyTorch - From Centralized To Federated <example-pytorch-from-centralized-to-federated>`.
 The only thing to do is modifying the file called :code:`cifar.py`, revised part is shown below:
 
 The model architecture defined in class Net() is added with Batch Normalization layers accordingly.
@@ -45,13 +45,13 @@ You can now run your machine learning workload:
     python3 cifar.py
 
 So far this should all look fairly familiar if you've used PyTorch before.
-Let's take the next step and use what we've built to create a federated learning system within FedBN, the sytstem consists of one server and two clients.
+Let's take the next step and use what we've built to create a federated learning system within FedBN, the system consists of one server and two clients.
 
 Federated Training
 ------------------
 
-If you have read `Example: PyTorch - From Centralized To Federated <https://flower.ai/docs/examples/pytorch-from-centralized-to-federated.html>`_, the following parts are easy to follow, onyl :code:`get_parameters` and :code:`set_parameters` function in :code:`client.py` needed to revise.
-If not, please read the `Example: PyTorch - From Centralized To Federated <https://flower.ai/docs/examples/pytorch-from-centralized-to-federated.html>`_. first.
+If you have read :doc:`Example: PyTorch - From Centralized To Federated <example-pytorch-from-centralized-to-federated>`, the following parts are easy to follow, only :code:`get_parameters` and :code:`set_parameters` function in :code:`client.py` needed to revise.
+If not, please read the :doc:`Example: PyTorch - From Centralized To Federated <example-pytorch-from-centralized-to-federated>`. first.
 
 Our example consists of one *server* and two *clients*. In FedBN, :code:`server.py` keeps unchanged, we can start the server directly.
 
diff --git a/doc/source/example-walkthrough-pytorch-mnist.rst b/doc/source/example-walkthrough-pytorch-mnist.rst
index 0be0af6e1ca6..8717c196f043 100644
--- a/doc/source/example-walkthrough-pytorch-mnist.rst
+++ b/doc/source/example-walkthrough-pytorch-mnist.rst
@@ -1,11 +1,11 @@
 Example: Walk-Through PyTorch & MNIST
 =====================================
 
-In this tutorial we will learn, how to train a Convolutional Neural Network on MNIST using Flower and PyTorch. 
+In this tutorial we will learn, how to train a Convolutional Neural Network on MNIST using Flower and PyTorch.
 
-Our example consists of one *server* and two *clients* all having the same model. 
+Our example consists of one *server* and two *clients* all having the same model.
 
-*Clients* are responsible for generating individual weight-updates for the model based on their local datasets. 
+*Clients* are responsible for generating individual weight-updates for the model based on their local datasets.
 These updates are then sent to the *server* which will aggregate them to produce a better model. Finally, the *server* sends this improved version of the model back to each *client*.
 A complete cycle of weight updates is called a *round*.
 
@@ -15,7 +15,7 @@ Now that we have a rough idea of what is going on, let's get started. We first n
 
   $ pip install flwr
 
-Since we want to use PyTorch to solve a computer vision task, let's go ahead an install PyTorch and the **torchvision** library: 
+Since we want to use PyTorch to solve a computer vision task, let's go ahead an install PyTorch and the **torchvision** library:
 
 .. code-block:: shell
 
@@ -32,51 +32,51 @@ Go ahead and launch on a terminal the *run-server.sh* script first as follows:
 
 .. code-block:: shell
 
-  $ bash ./run-server.sh 
+  $ bash ./run-server.sh
 
 
-Now that the server is up and running, go ahead and launch the clients.  
+Now that the server is up and running, go ahead and launch the clients.
 
 .. code-block:: shell
 
-  $ bash ./run-clients.sh 
+  $ bash ./run-clients.sh
 
 
 Et voilà! You should be seeing the training procedure and, after a few iterations, the test accuracy for each client.
 
 .. code-block:: shell
 
-    Train Epoch: 10 [30000/30016 (100%)] Loss: 0.007014				
-    
-    Train Epoch: 10 [30000/30016 (100%)] Loss: 0.000403				
-    
-    Train Epoch: 11 [30000/30016 (100%)] Loss: 0.001280				
-    
-    Train Epoch: 11 [30000/30016 (100%)] Loss: 0.000641				
-    
-    Train Epoch: 12 [30000/30016 (100%)] Loss: 0.006784				
-    
-    Train Epoch: 12 [30000/30016 (100%)] Loss: 0.007134				
-    
-    Client 1 - Evaluate on 5000 samples: Average loss: 0.0290, Accuracy: 99.16%	
-    
+    Train Epoch: 10 [30000/30016 (100%)] Loss: 0.007014
+
+    Train Epoch: 10 [30000/30016 (100%)] Loss: 0.000403
+
+    Train Epoch: 11 [30000/30016 (100%)] Loss: 0.001280
+
+    Train Epoch: 11 [30000/30016 (100%)] Loss: 0.000641
+
+    Train Epoch: 12 [30000/30016 (100%)] Loss: 0.006784
+
+    Train Epoch: 12 [30000/30016 (100%)] Loss: 0.007134
+
+    Client 1 - Evaluate on 5000 samples: Average loss: 0.0290, Accuracy: 99.16%
+
     Client 0 - Evaluate on 5000 samples: Average loss: 0.0328, Accuracy: 99.14%
 
 
-Now, let's see what is really happening inside. 
+Now, let's see what is really happening inside.
 
 Flower Server
 -------------
 
 Inside the server helper script *run-server.sh* you will find the following code that basically runs the :code:`server.py`
 
-.. code-block:: bash 
+.. code-block:: bash
 
     python -m flwr_example.quickstart-pytorch.server
 
 
 We can go a bit deeper and see that :code:`server.py` simply launches a server that will coordinate three rounds of training.
-Flower Servers are very customizable, but for simple workloads, we can start a server using the `start_server <https://flower.ai/doc/framework/ref-api/flwr.server.start_server.html>`_ function and leave all the configuration possibilities at their default values, as seen below.
+Flower Servers are very customizable, but for simple workloads, we can start a server using the `start_server <https://flower.ai/docs/framework/ref-api/flwr.server.start_server.html#flwr.server.start_server>`_ function and leave all the configuration possibilities at their default values, as seen below.
 
 .. code-block:: python
 
@@ -90,18 +90,18 @@ Flower Client
 
 Next, let's take a look at the *run-clients.sh* file. You will see that it contains the main loop that starts a set of *clients*.
 
-.. code-block:: bash 
+.. code-block:: bash
 
     python -m flwr_example.quickstart-pytorch.client \
       --cid=$i \
       --server_address=$SERVER_ADDRESS \
-      --nb_clients=$NUM_CLIENTS 
+      --nb_clients=$NUM_CLIENTS
 
 * **cid**: is the client ID. It is an integer that uniquely identifies client identifier.
-* **sever_address**: String that identifies IP and port of the server. 
+* **sever_address**: String that identifies IP and port of the server.
 * **nb_clients**: This defines the number of clients being created. This piece of information is not required by the client, but it helps us partition the original MNIST dataset to make sure that every client is working on unique subsets of both *training* and *test* sets.
 
-Again, we can go deeper and look inside :code:`flwr_example/quickstart-pytorch/client.py`. 
+Again, we can go deeper and look inside :code:`flwr_example/quickstart-pytorch/client.py`.
 After going through the argument parsing code at the beginning of our :code:`main` function, you will find a call to :code:`mnist.load_data`. This function is responsible for partitioning the original MNIST datasets (*training* and *test*) and returning a :code:`torch.utils.data.DataLoader` s for each of them.
 We then instantiate a :code:`PytorchMNISTClient` object with our client ID, our DataLoaders, the number of epochs in each round, and which device we want to use for training (CPU or GPU).
 
@@ -152,7 +152,7 @@ Now, let's look closely into the :code:`PytorchMNISTClient` inside :code:`flwr_e
 
             Parameters
             ----------
-            weights: fl.common.NDArrays 
+            weights: fl.common.NDArrays
                 Weights received by the server and set to local model
 
 
@@ -179,8 +179,8 @@ Now, let's look closely into the :code:`PytorchMNISTClient` inside :code:`flwr_e
 
             Parameters
             ----------
-            ins: fl.common.FitIns 
-            Parameters sent by the server to be used during training. 
+            ins: fl.common.FitIns
+            Parameters sent by the server to be used during training.
 
             Returns
             -------
@@ -214,9 +214,9 @@ Now, let's look closely into the :code:`PytorchMNISTClient` inside :code:`flwr_e
 
             Parameters
             ----------
-            ins: fl.common.EvaluateIns 
-            Parameters sent by the server to be used during testing. 
-                
+            ins: fl.common.EvaluateIns
+            Parameters sent by the server to be used during testing.
+
 
             Returns
             -------
@@ -262,9 +262,9 @@ The code for the CNN is available under :code:`quickstart-pytorch.mnist` and it
 
             Parameters
             ----------
-            x: Tensor 
+            x: Tensor
                 Mini-batch of shape (N,28,28) containing images from MNIST dataset.
-                
+
 
             Returns
             -------
@@ -287,7 +287,7 @@ The code for the CNN is available under :code:`quickstart-pytorch.mnist` and it
             return output
 
 
-The second thing to notice is that :code:`PytorchMNISTClient` class inherits from the :code:`fl.client.Client`, and hence it must implement the following methods:  
+The second thing to notice is that :code:`PytorchMNISTClient` class inherits from the :code:`fl.client.Client`, and hence it must implement the following methods:
 
 .. code-block:: python
 
@@ -312,7 +312,7 @@ The second thing to notice is that :code:`PytorchMNISTClient` class inherits fro
             """Evaluate the provided weights using the locally held dataset."""
 
 
-When comparing the abstract class to its derived class :code:`PytorchMNISTClient` you will notice that :code:`fit` calls a :code:`train` function and that :code:`evaluate` calls a :code:`test`: function. 
+When comparing the abstract class to its derived class :code:`PytorchMNISTClient` you will notice that :code:`fit` calls a :code:`train` function and that :code:`evaluate` calls a :code:`test`: function.
 
 These functions can both be found inside the same :code:`quickstart-pytorch.mnist` module:
 
@@ -330,14 +330,14 @@ These functions can both be found inside the same :code:`quickstart-pytorch.mnis
         ----------
         model: torch.nn.ModuleList
             Neural network model used in this example.
-            
+
         train_loader: torch.utils.data.DataLoader
             DataLoader used in traning.
-            
-        epochs: int 
-            Number of epochs to run in each round. 
-            
-        device: torch.device 
+
+        epochs: int
+            Number of epochs to run in each round.
+
+        device: torch.device
             (Default value = torch.device("cpu"))
             Device where the network will be trained within a client.
 
@@ -399,10 +399,10 @@ These functions can both be found inside the same :code:`quickstart-pytorch.mnis
         ----------
         model: torch.nn.ModuleList :
             Neural network model used in this example.
-            
+
         test_loader: torch.utils.data.DataLoader :
             DataLoader used in test.
-            
+
         device: torch.device :
             (Default value = torch.device("cpu"))
             Device where the network will be tested within a client.
@@ -435,19 +435,19 @@ These functions can both be found inside the same :code:`quickstart-pytorch.mnis
 
 
 Observe that these functions encapsulate regular training and test loops and provide :code:`fit` and :code:`evaluate` with final statistics for each round.
-You could substitute them with your custom train and test loops and change the network architecture, and the entire example would still work flawlessly. 
-As a matter of fact, why not try and modify the code to an example of your liking? 
+You could substitute them with your custom train and test loops and change the network architecture, and the entire example would still work flawlessly.
+As a matter of fact, why not try and modify the code to an example of your liking?
 
 
 
 Give It a Try
 -------------
-Looking through the quickstart code description above will have given a good understanding of how *clients* and *servers* work in Flower, how to run a simple experiment, and the internals of a client wrapper. 
+Looking through the quickstart code description above will have given a good understanding of how *clients* and *servers* work in Flower, how to run a simple experiment, and the internals of a client wrapper.
 Here are a few things you could try on your own and get more experience with Flower:
 
 - Try and change :code:`PytorchMNISTClient` so it can accept different architectures.
 - Modify the :code:`train` function so that it accepts different optimizers
 - Modify the :code:`test` function so that it proves not only the top-1 (regular accuracy) but also the top-5 accuracy?
-- Go larger! Try to adapt the code to larger images and datasets. Why not try training on ImageNet with a ResNet-50? 
+- Go larger! Try to adapt the code to larger images and datasets. Why not try training on ImageNet with a ResNet-50?
 
 You are ready now. Enjoy learning in a federated way!
diff --git a/doc/source/how-to-configure-clients.rst b/doc/source/how-to-configure-clients.rst
index bfb5a8f63761..ff0a2f4033df 100644
--- a/doc/source/how-to-configure-clients.rst
+++ b/doc/source/how-to-configure-clients.rst
@@ -86,7 +86,7 @@ Configuring individual clients
 
 In some cases, it is necessary to send different configuration values to different clients.
 
-This can be achieved by customizing an existing strategy or by `implementing a custom strategy from scratch <https://flower.ai/docs/framework/how-to-implement-strategies.html>`_. Here's a nonsensical example that customizes :code:`FedAvg` by adding a custom ``"hello": "world"`` configuration key/value pair to the config dict of a *single client* (only the first client in the list, the other clients in this round to not receive this "special" config value):
+This can be achieved by customizing an existing strategy or by :doc:`implementing a custom strategy from scratch <how-to-implement-strategies>`. Here's a nonsensical example that customizes :code:`FedAvg` by adding a custom ``"hello": "world"`` configuration key/value pair to the config dict of a *single client* (only the first client in the list, the other clients in this round to not receive this "special" config value):
 
 .. code-block:: python
 
diff --git a/doc/source/how-to-install-flower.rst b/doc/source/how-to-install-flower.rst
index dc88076424f8..aebe5f7316de 100644
--- a/doc/source/how-to-install-flower.rst
+++ b/doc/source/how-to-install-flower.rst
@@ -57,7 +57,7 @@ Advanced installation options
 Install via Docker
 ~~~~~~~~~~~~~~~~~~
 
-`How to run Flower using Docker <https://flower.ai/docs/framework/how-to-run-flower-using-docker.html>`_
+:doc:`How to run Flower using Docker <how-to-run-flower-using-docker>`
 
 Install pre-release
 ~~~~~~~~~~~~~~~~~~~
diff --git a/doc/source/how-to-monitor-simulation.rst b/doc/source/how-to-monitor-simulation.rst
index 740004914eed..61a3be68deec 100644
--- a/doc/source/how-to-monitor-simulation.rst
+++ b/doc/source/how-to-monitor-simulation.rst
@@ -231,6 +231,6 @@ A: Either the simulation has already finished, or you still need to start Promet
 Resources
 ---------
 
-Ray Dashboard: `<https://docs.ray.io/en/latest/ray-core/ray-dashboard.html>`_
+Ray Dashboard: `<https://docs.ray.io/en/latest/ray-observability/getting-started.html>`_
 
-Ray Metrics: `<https://docs.ray.io/en/latest/ray-observability/ray-metrics.html>`_
+Ray Metrics: `<https://docs.ray.io/en/latest/cluster/metrics.html>`_
diff --git a/doc/source/ref-example-projects.rst b/doc/source/ref-example-projects.rst
index 8eb723000cac..bade86dfaa54 100644
--- a/doc/source/ref-example-projects.rst
+++ b/doc/source/ref-example-projects.rst
@@ -23,7 +23,7 @@ The TensorFlow/Keras quickstart example shows CIFAR-10 image classification
 with MobileNetV2:
 
 - `Quickstart TensorFlow (Code) <https://github.com/adap/flower/tree/main/examples/quickstart-tensorflow>`_
-- `Quickstart TensorFlow (Tutorial) <https://flower.ai/docs/framework/tutorial-quickstart-tensorflow.html>`_
+- :doc:`Quickstart TensorFlow (Tutorial) <tutorial-quickstart-tensorflow>`
 - `Quickstart TensorFlow (Blog Post) <https://flower.ai/blog/2020-12-11-federated-learning-in-less-than-20-lines-of-code>`_
 
 
@@ -34,7 +34,7 @@ The PyTorch quickstart example shows CIFAR-10 image classification
 with a simple Convolutional Neural Network:
 
 - `Quickstart PyTorch (Code) <https://github.com/adap/flower/tree/main/examples/quickstart-pytorch>`_
-- `Quickstart PyTorch (Tutorial) <https://flower.ai/docs/framework/tutorial-quickstart-pytorch.html>`_
+- :doc:`Quickstart PyTorch (Tutorial) <tutorial-quickstart-pytorch>`
 
 
 PyTorch: From Centralized To Federated
@@ -43,7 +43,7 @@ PyTorch: From Centralized To Federated
 This example shows how a regular PyTorch project can be federated using Flower:
 
 - `PyTorch: From Centralized To Federated (Code) <https://github.com/adap/flower/tree/main/examples/pytorch-from-centralized-to-federated>`_
-- `PyTorch: From Centralized To Federated (Tutorial) <https://flower.ai/docs/framework/example-pytorch-from-centralized-to-federated.html>`_
+- :doc:`PyTorch: From Centralized To Federated (Tutorial) <example-pytorch-from-centralized-to-federated>`
 
 
 Federated Learning on Raspberry Pi and Nvidia Jetson
@@ -60,7 +60,7 @@ Legacy Examples (`flwr_example`)
 --------------------------------
 
 .. warning::
-   The useage examples in `flwr_example` are deprecated and will be removed in
+   The usage examples in `flwr_example` are deprecated and will be removed in
    the future. New examples are provided as standalone projects in
    `examples <https://github.com/adap/flower/tree/main/examples>`_.
 
@@ -114,7 +114,7 @@ For more details, see :code:`src/py/flwr_example/pytorch_cifar`.
 ImageNet-2012 Image Classification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-`ImageNet-2012 <http://www.image-net.org/>`_ is one of the major computer
+`ImageNet-2012 <https://www.image-net.org/>`_ is one of the major computer
 vision datasets. The Flower ImageNet example uses PyTorch to train a ResNet-18
 classifier in a federated learning setup with ten clients.
 
diff --git a/doc/source/ref-faq.rst b/doc/source/ref-faq.rst
index 932396e3c583..26b7dca4a0a7 100644
--- a/doc/source/ref-faq.rst
+++ b/doc/source/ref-faq.rst
@@ -3,7 +3,7 @@ FAQ
 
 This page collects answers to commonly asked questions about Federated Learning with Flower.
 
-.. dropdown:: :fa:`eye,mr-1` Can Flower run on Juptyter Notebooks / Google Colab?
+.. dropdown:: :fa:`eye,mr-1` Can Flower run on Jupyter Notebooks / Google Colab?
 
     Yes, it can! Flower even comes with a few under-the-hood optimizations to make it work even better on Colab. Here's a quickstart example:
 
@@ -27,6 +27,6 @@ This page collects answers to commonly asked questions about Federated Learning
 
     * `Flower meets Nevermined GitHub Repository <https://github.com/nevermined-io/fl-demo/tree/master/image-classification-flower>`_.
     * `Flower meets Nevermined YouTube video <https://www.youtube.com/watch?v=A0A9hSlPhKI>`_.
-    * `Flower meets KOSMoS <https://www.kosmos-bmbf.de/wp-content/uploads/sites/13/2021/05/Talk-Flower-Summit-2021.pdf>`_.
+    * `Flower meets KOSMoS <https://www.isw-sites.de/kosmos/wp-content/uploads/sites/13/2021/05/Talk-Flower-Summit-2021.pdf>`_.
     * `Flower meets Talan blog post <https://www.linkedin.com/pulse/federated-learning-same-mask-different-faces-imen-ayari/?trackingId=971oIlxLQ9%2BA9RB0IQ73XQ%3D%3D>`_ .
     * `Flower meets Talan GitHub Repository <https://gitlab.com/Talan_Innovation_Factory/food-waste-prevention>`_ .
diff --git a/doc/source/tutorial-quickstart-huggingface.rst b/doc/source/tutorial-quickstart-huggingface.rst
index 1e06120b452f..7d8128230901 100644
--- a/doc/source/tutorial-quickstart-huggingface.rst
+++ b/doc/source/tutorial-quickstart-huggingface.rst
@@ -9,8 +9,8 @@ Quickstart 🤗 Transformers
 
 Let's build a federated learning system using Hugging Face Transformers and Flower!
 
-We will leverage Hugging Face to federate the training of language models over multiple clients using Flower. 
-More specifically, we will fine-tune a pre-trained Transformer model (distilBERT) 
+We will leverage Hugging Face to federate the training of language models over multiple clients using Flower.
+More specifically, we will fine-tune a pre-trained Transformer model (distilBERT)
 for sequence classification over a dataset of IMDB ratings.
 The end goal is to detect if a movie rating is positive or negative.
 
@@ -32,8 +32,8 @@ Standard Hugging Face workflow
 Handling the data
 ^^^^^^^^^^^^^^^^^
 
-To fetch the IMDB dataset, we will use Hugging Face's :code:`datasets` library. 
-We then need to tokenize the data and create :code:`PyTorch` dataloaders, 
+To fetch the IMDB dataset, we will use Hugging Face's :code:`datasets` library.
+We then need to tokenize the data and create :code:`PyTorch` dataloaders,
 this is all done in the :code:`load_data` function:
 
 .. code-block:: python
@@ -80,8 +80,8 @@ this is all done in the :code:`load_data` function:
 Training and testing the model
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Once we have a way of creating our trainloader and testloader, 
-we can take care of the training and testing. 
+Once we have a way of creating our trainloader and testloader,
+we can take care of the training and testing.
 This is very similar to any :code:`PyTorch` training or testing loop:
 
 .. code-block:: python
@@ -120,12 +120,12 @@ This is very similar to any :code:`PyTorch` training or testing loop:
 Creating the model itself
 ^^^^^^^^^^^^^^^^^^^^^^^^^
 
-To create the model itself, 
+To create the model itself,
 we will just load the pre-trained distillBERT model using Hugging Face’s :code:`AutoModelForSequenceClassification` :
 
 .. code-block:: python
 
-    from transformers import AutoModelForSequenceClassification 
+    from transformers import AutoModelForSequenceClassification
 
     net = AutoModelForSequenceClassification.from_pretrained(
             CHECKPOINT, num_labels=2
@@ -138,8 +138,8 @@ Federating the example
 Creating the IMDBClient
 ^^^^^^^^^^^^^^^^^^^^^^^
 
-To federate our example to multiple clients, 
-we first need to write our Flower client class (inheriting from :code:`flwr.client.NumPyClient`). 
+To federate our example to multiple clients,
+we first need to write our Flower client class (inheriting from :code:`flwr.client.NumPyClient`).
 This is very easy, as our model is a standard :code:`PyTorch` model:
 
 .. code-block:: python
@@ -166,17 +166,17 @@ This is very easy, as our model is a standard :code:`PyTorch` model:
                 return float(loss), len(testloader), {"accuracy": float(accuracy)}
 
 
-The :code:`get_parameters` function lets the server get the client's parameters. 
-Inversely, the :code:`set_parameters` function allows the server to send its parameters to the client. 
-Finally, the :code:`fit` function trains the model locally for the client, 
-and the :code:`evaluate` function tests the model locally and returns the relevant metrics. 
+The :code:`get_parameters` function lets the server get the client's parameters.
+Inversely, the :code:`set_parameters` function allows the server to send its parameters to the client.
+Finally, the :code:`fit` function trains the model locally for the client,
+and the :code:`evaluate` function tests the model locally and returns the relevant metrics.
 
 Starting the server
 ^^^^^^^^^^^^^^^^^^^
 
-Now that we have a way to instantiate clients, we need to create our server in order to aggregate the results. 
-Using Flower, this can be done very easily by first choosing a strategy (here, we are using :code:`FedAvg`, 
-which will define the global weights as the average of all the clients' weights at each round) 
+Now that we have a way to instantiate clients, we need to create our server in order to aggregate the results.
+Using Flower, this can be done very easily by first choosing a strategy (here, we are using :code:`FedAvg`,
+which will define the global weights as the average of all the clients' weights at each round)
 and then using the :code:`flwr.server.start_server` function:
 
 .. code-block:: python
@@ -186,7 +186,7 @@ and then using the :code:`flwr.server.start_server` function:
         losses = [num_examples * m["loss"] for num_examples, m in metrics]
         examples = [num_examples for num_examples, _ in metrics]
         return {"accuracy": sum(accuracies) / sum(examples), "loss": sum(losses) / sum(examples)}
-        
+
     # Define strategy
     strategy = fl.server.strategy.FedAvg(
         fraction_fit=1.0,
@@ -202,7 +202,7 @@ and then using the :code:`flwr.server.start_server` function:
     )
 
 
-The :code:`weighted_average` function is there to provide a way to aggregate the metrics distributed amongst 
+The :code:`weighted_average` function is there to provide a way to aggregate the metrics distributed amongst
 the clients (basically this allows us to display a nice average accuracy and loss for every round).
 
 Putting everything together
@@ -213,18 +213,17 @@ We can now start client instances using:
 .. code-block:: python
 
     fl.client.start_client(
-        server_address="127.0.0.1:8080", 
+        server_address="127.0.0.1:8080",
         client=IMDBClient().to_client()
     )
 
 
 And they will be able to connect to the server and start the federated training.
 
-If you want to check out everything put together, 
-you should check out the full code example: 
-[https://github.com/adap/flower/tree/main/examples/quickstart-huggingface](https://github.com/adap/flower/tree/main/examples/quickstart-huggingface). 
+If you want to check out everything put together,
+you should check out the `full code example <https://github.com/adap/flower/tree/main/examples/quickstart-huggingface>`_ .
 
-Of course, this is a very basic example, and a lot can be added or modified, 
+Of course, this is a very basic example, and a lot can be added or modified,
 it was just to showcase how simply we could federate a Hugging Face workflow using Flower.
 
 Note that in this example we used :code:`PyTorch`, but we could have very well used :code:`TensorFlow`.
diff --git a/doc/source/tutorial-quickstart-ios.rst b/doc/source/tutorial-quickstart-ios.rst
index aa94a72580c1..e4315ce569fb 100644
--- a/doc/source/tutorial-quickstart-ios.rst
+++ b/doc/source/tutorial-quickstart-ios.rst
@@ -9,7 +9,7 @@ Quickstart iOS
 
 In this tutorial we will learn how to train a Neural Network on MNIST using Flower and CoreML on iOS devices.
 
-First of all, for running the Flower Python server, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.ai/docs/recommended-env-setup.html>`_.
+First of all, for running the Flower Python server, it is recommended to create a virtual environment and run everything within a :doc:`virtualenv <contributor-how-to-set-up-a-virtual-env>`.
 For the Flower client implementation in iOS, it is recommended to use Xcode as our IDE.
 
 Our example consists of one Python *server* and two iPhone *clients* that all have the same model.
@@ -97,9 +97,9 @@ For the MNIST dataset, we need to preprocess it into :code:`MLBatchProvider` obj
                                 testBatchProvider: testBatchProvider)
 
 Since CoreML does not allow the model parameters to be seen before training, and accessing the model parameters during or after the training can only be done by specifying the layer name,
-we need to know this informations beforehand, through looking at the model specification, which are written as proto files. The implementation can be seen in :code:`MLModelInspect`.
+we need to know this information beforehand, through looking at the model specification, which are written as proto files. The implementation can be seen in :code:`MLModelInspect`.
 
-After we have all of the necessary informations, let's create our Flower client.
+After we have all of the necessary information, let's create our Flower client.
 
 .. code-block:: swift
 
diff --git a/doc/source/tutorial-quickstart-mxnet.rst b/doc/source/tutorial-quickstart-mxnet.rst
index 08304483af86..fe582f793280 100644
--- a/doc/source/tutorial-quickstart-mxnet.rst
+++ b/doc/source/tutorial-quickstart-mxnet.rst
@@ -4,14 +4,14 @@
 Quickstart MXNet
 ================
 
-.. warning:: MXNet is no longer maintained and has been moved into `Attic <https://attic.apache.org/projects/mxnet.html>`_. As a result, we would encourage you to use other ML frameworks alongise Flower, for example, PyTorch. This tutorial might be removed in future versions of Flower.
+.. warning:: MXNet is no longer maintained and has been moved into `Attic <https://attic.apache.org/projects/mxnet.html>`_. As a result, we would encourage you to use other ML frameworks alongside Flower, for example, PyTorch. This tutorial might be removed in future versions of Flower.
 
 .. meta::
    :description: Check out this Federated Learning quickstart tutorial for using Flower with MXNet to train a Sequential model on MNIST.
 
 In this tutorial, we will learn how to train a :code:`Sequential` model on MNIST using Flower and MXNet.
 
-It is recommended to create a virtual environment and run everything within this `virtualenv <https://flower.ai/docs/recommended-env-setup.html>`_.
+It is recommended to create a virtual environment and run everything within this :doc:`virtualenv <contributor-how-to-set-up-a-virtual-env>`.
 
 Our example consists of one *server* and two *clients* all having the same model.
 
diff --git a/doc/source/tutorial-quickstart-pytorch.rst b/doc/source/tutorial-quickstart-pytorch.rst
index 32f9c5ebb3a1..895590808a2b 100644
--- a/doc/source/tutorial-quickstart-pytorch.rst
+++ b/doc/source/tutorial-quickstart-pytorch.rst
@@ -12,7 +12,7 @@ Quickstart PyTorch
 
 In this tutorial we will learn how to train a Convolutional Neural Network on CIFAR10 using Flower and PyTorch.
 
-First of all, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.ai/docs/recommended-env-setup.html>`_.
+First of all, it is recommended to create a virtual environment and run everything within a :doc:`virtualenv <contributor-how-to-set-up-a-virtual-env>`.
 
 Our example consists of one *server* and two *clients* all having the same model.
 
diff --git a/doc/source/tutorial-quickstart-scikitlearn.rst b/doc/source/tutorial-quickstart-scikitlearn.rst
index b95118aa091f..d1d47dc37f19 100644
--- a/doc/source/tutorial-quickstart-scikitlearn.rst
+++ b/doc/source/tutorial-quickstart-scikitlearn.rst
@@ -9,7 +9,7 @@ Quickstart scikit-learn
 
 In this tutorial, we will learn how to train a :code:`Logistic Regression` model on MNIST using Flower and scikit-learn.
 
-It is recommended to create a virtual environment and run everything within this `virtualenv <https://flower.ai/docs/recommended-env-setup.html>`_.
+It is recommended to create a virtual environment and run everything within this :doc:`virtualenv <contributor-how-to-set-up-a-virtual-env>`.
 
 Our example consists of one *server* and two *clients* all having the same model.
 
@@ -23,7 +23,7 @@ Now that we have a rough idea of what is going on, let's get started. We first n
 
   $ pip install flwr
 
-Since we want to use scikt-learn, let's go ahead and install it:
+Since we want to use scikit-learn, let's go ahead and install it:
 
 .. code-block:: shell
 
@@ -43,7 +43,7 @@ Now that we have all our dependencies installed, let's run a simple distributed
 However, before setting up the client and server, we will define all functionalities that we need for our federated learning setup within :code:`utils.py`. The :code:`utils.py` contains different functions defining all the machine learning basics:
 
 * :code:`get_model_parameters()`
-    * Returns the paramters of a :code:`sklearn` LogisticRegression model
+    * Returns the parameters of a :code:`sklearn` LogisticRegression model
 * :code:`set_model_params()`
     * Sets the parameters of a :code:`sklean` LogisticRegression model
 * :code:`set_initial_params()`
@@ -70,7 +70,7 @@ The pre-defined functions are used in the :code:`client.py` and imported. The :c
   import utils
 
 
-We load the MNIST dataset from `OpenML <https://www.openml.org/d/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()`.
+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()`.
 
 .. code-block:: python
 
diff --git a/doc/source/tutorial-quickstart-xgboost.rst b/doc/source/tutorial-quickstart-xgboost.rst
index ec9101f4b3fd..751024db14e4 100644
--- a/doc/source/tutorial-quickstart-xgboost.rst
+++ b/doc/source/tutorial-quickstart-xgboost.rst
@@ -36,7 +36,7 @@ and then we dive into a more complex example (`full code xgboost-comprehensive <
 Environment Setup
 --------------------
 
-First of all, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.ai/docs/recommended-env-setup.html>`_.
+First of all, it is recommended to create a virtual environment and run everything within a :doc:`virtualenv <contributor-how-to-set-up-a-virtual-env>`.
 
 We first need to install Flower and Flower Datasets. You can do this by running :
 
@@ -596,7 +596,7 @@ Comprehensive Federated XGBoost
 Now that you have known how federated XGBoost work with Flower, it's time to run some more comprehensive experiments by customising the experimental settings.
 In the xgboost-comprehensive example (`full code <https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive>`_),
 we provide more options to define various experimental setups, including aggregation strategies, data partitioning and centralised/distributed evaluation.
-We also support `Flower simulation <https://flower.ai/docs/framework/how-to-run-simulations.html>`_ making it easy to simulate large client cohorts in a resource-aware manner.
+We also support :doc:`Flower simulation <how-to-run-simulations>` making it easy to simulate large client cohorts in a resource-aware manner.
 Let's take a look!
 
 Cyclic training
diff --git a/doc/source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb b/doc/source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb
index 5b2236468909..c5fc777e7f26 100644
--- a/doc/source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb
+++ b/doc/source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb
@@ -7,11 +7,11 @@
    "source": [
     "# Build a strategy from scratch\n",
     "\n",
-    "Welcome to the third part of the Flower federated learning tutorial. In previous parts of this tutorial, we introduced federated learning with PyTorch and Flower ([part 1](https://flower.dev/docs/framework/tutorial-get-started-with-flower-pytorch.html)) and we learned how strategies can be used to customize the execution on both the server and the clients ([part 2](https://flower.dev/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html)).\n",
+    "Welcome to the third part of the Flower federated learning tutorial. In previous parts of this tutorial, we introduced federated learning with PyTorch and Flower ([part 1](https://flower.ai/docs/framework/tutorial-get-started-with-flower-pytorch.html)) and we learned how strategies can be used to customize the execution on both the server and the clients ([part 2](https://flower.ai/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html)).\n",
     "\n",
-    "In this notebook, we'll continue to customize the federated learning system we built previously by creating a custom version of FedAvg (again, using [Flower](https://flower.dev/) and [PyTorch](https://pytorch.org/)).\n",
+    "In this notebook, we'll continue to customize the federated learning system we built previously by creating a custom version of FedAvg (again, using [Flower](https://flower.ai/) and [PyTorch](https://pytorch.org/)).\n",
     "\n",
-    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.dev/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
+    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.ai/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
     "\n",
     "Let's build a new `Strategy` from scratch!"
    ]
@@ -489,11 +489,11 @@
    "source": [
     "## Next steps\n",
     "\n",
-    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.dev/join-slack/)\n",
+    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.ai/join-slack/)\n",
     "\n",
     "There's a dedicated `#questions` channel if you need help, but we'd also love to hear who you are in `#introductions`!\n",
     "\n",
-    "The [Flower Federated Learning Tutorial - Part 4](https://flower.dev/docs/framework/tutorial-customize-the-client-pytorch.html) introduces `Client`, the flexible API underlying `NumPyClient`."
+    "The [Flower Federated Learning Tutorial - Part 4](https://flower.ai/docs/framework/tutorial-customize-the-client-pytorch.html) introduces `Client`, the flexible API underlying `NumPyClient`."
    ]
   }
  ],
diff --git a/doc/source/tutorial-series-customize-the-client-pytorch.ipynb b/doc/source/tutorial-series-customize-the-client-pytorch.ipynb
index bcfdeb30d3c7..ce09c6cc46c1 100644
--- a/doc/source/tutorial-series-customize-the-client-pytorch.ipynb
+++ b/doc/source/tutorial-series-customize-the-client-pytorch.ipynb
@@ -869,16 +869,16 @@
    "source": [
     "## Next steps\n",
     "\n",
-    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.dev/join-slack/)\n",
+    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.ai/join-slack/)\n",
     "\n",
     "There's a dedicated `#questions` channel if you need help, but we'd also love to hear who you are in `#introductions`!\n",
     "\n",
     "This is the final part of the Flower tutorial (for now!), congratulations! You're now well equipped to understand the rest of the documentation. There are many topics we didn't cover in the tutorial, we recommend the following resources:\n",
     "\n",
-    "- [Read Flower Docs](https://flower.dev/docs/)\n",
+    "- [Read Flower Docs](https://flower.ai/docs/)\n",
     "- [Check out Flower Code Examples](https://github.com/adap/flower/tree/main/examples)\n",
-    "- [Use Flower Baselines for your research](https://flower.dev/docs/baselines/)\n",
-    "- [Watch Flower Summit 2023 videos](https://flower.dev/conf/flower-summit-2023/)\n"
+    "- [Use Flower Baselines for your research](https://flower.ai/docs/baselines/)\n",
+    "- [Watch Flower Summit 2023 videos](https://flower.ai/conf/flower-summit-2023/)\n"
    ]
   }
  ],
diff --git a/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb b/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb
index f4b8acaa5bb8..205531c54ee6 100644
--- a/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb
+++ b/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb
@@ -605,11 +605,11 @@
    "source": [
     "## Next steps\n",
     "\n",
-    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.dev/join-slack/)\n",
+    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.ai/join-slack/)\n",
     "\n",
     "There's a dedicated `#questions` channel if you need help, but we'd also love to hear who you are in `#introductions`!\n",
     "\n",
-    "The [Flower Federated Learning Tutorial - Part 2](https://flower.dev/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html) goes into more depth about strategies and all the advanced things you can build with them.\n"
+    "The [Flower Federated Learning Tutorial - Part 2](https://flower.ai/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html) goes into more depth about strategies and all the advanced things you can build with them.\n"
    ]
   }
  ],
diff --git a/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb b/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb
index c758b8f637b0..e20a8d83f674 100644
--- a/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb
+++ b/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb
@@ -614,11 +614,11 @@
    "source": [
     "## Next steps\n",
     "\n",
-    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.dev/join-slack/)\n",
+    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.ai/join-slack/)\n",
     "\n",
     "There's a dedicated `#questions` channel if you need help, but we'd also love to hear who you are in `#introductions`!\n",
     "\n",
-    "The [Flower Federated Learning Tutorial - Part 3](https://flower.dev/docs/framework/tutorial-build-a-strategy-from-scratch-pytorch.html) shows how to build a fully custom `Strategy` from scratch."
+    "The [Flower Federated Learning Tutorial - Part 3](https://flower.ai/docs/framework/tutorial-build-a-strategy-from-scratch-pytorch.html) shows how to build a fully custom `Strategy` from scratch."
    ]
   }
  ],
diff --git a/doc/source/tutorial-series-what-is-federated-learning.ipynb b/doc/source/tutorial-series-what-is-federated-learning.ipynb
index 3f7e383b9fbc..d77182838f21 100755
--- a/doc/source/tutorial-series-what-is-federated-learning.ipynb
+++ b/doc/source/tutorial-series-what-is-federated-learning.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "🧑‍🏫 This tutorial starts at zero and expects no familiarity with federated learning. Only a basic understanding of data science and Python programming is assumed.\n",
     "\n",
-    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the open-source Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.dev/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
+    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the open-source Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.ai/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
     "\n",
     "Let's get started!"
    ]
@@ -217,11 +217,11 @@
    "source": [
     "## Next steps\n",
     "\n",
-    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.dev/join-slack/)\n",
+    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.ai/join-slack/)\n",
     "\n",
     "There's a dedicated `#questions` channel if you need help, but we'd also love to hear who you are in `#introductions`!\n",
     "\n",
-    "The [Flower Federated Learning Tutorial - Part 1](https://flower.dev/docs/framework/tutorial-get-started-with-flower-pytorch.html) shows how to build a simple federated learning system with PyTorch and Flower."
+    "The [Flower Federated Learning Tutorial - Part 1](https://flower.ai/docs/framework/tutorial-get-started-with-flower-pytorch.html) shows how to build a simple federated learning system with PyTorch and Flower."
    ]
   }
  ],
diff --git a/src/py/flwr/server/driver/driver.py b/src/py/flwr/server/driver/driver.py
index aa8dc1ddd6c0..bcaac1f61b85 100644
--- a/src/py/flwr/server/driver/driver.py
+++ b/src/py/flwr/server/driver/driver.py
@@ -44,6 +44,7 @@ class Driver:
         Tuple containing root certificate, server certificate, and private key
         to start a secure SSL-enabled server. The tuple is expected to have
         three bytes elements in the following order:
+
             * CA certificate.
             * server certificate.
             * server private key.

From 27ae762491abe07ac9160614d090b92415827139 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Mon, 26 Feb 2024 14:37:38 +0100
Subject: [PATCH 058/102] Add build/process/terminate tests for `RayBackend`
 (#3011)

---
 .../superlink/fleet/vce/backend/raybackend.py |   3 +-
 .../fleet/vce/backend/raybackend_test.py      | 141 ++++++++++++++++++
 2 files changed, 142 insertions(+), 2 deletions(-)
 create mode 100644 src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py

diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
index cc3cf4348491..b29d76b239e5 100644
--- a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
@@ -14,7 +14,6 @@
 # ==============================================================================
 """Ray backend for the Fleet API using the Simulation Engine."""
 
-import asyncio
 import pathlib
 from logging import INFO
 from typing import Callable, Dict, List, Tuple, Union
@@ -145,7 +144,7 @@ async def process_message(
             (app, message, str(node_id), context),
         )
 
-        await asyncio.wait([future])
+        await future
 
         # Fetch result
         (
diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py
new file mode 100644
index 000000000000..f0cca527ab96
--- /dev/null
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py
@@ -0,0 +1,141 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test for Ray backend for the Fleet API using the Simulation Engine."""
+
+import asyncio
+from math import pi
+from typing import Callable, Dict, Optional, Tuple, Union
+from unittest import IsolatedAsyncioTestCase
+
+import ray
+
+from flwr.client import Client, NumPyClient
+from flwr.client.clientapp import ClientApp
+from flwr.common import (
+    Config,
+    ConfigsRecord,
+    Context,
+    GetPropertiesIns,
+    Message,
+    Metadata,
+    RecordSet,
+    Scalar,
+)
+from flwr.common.constant import MESSAGE_TYPE_GET_PROPERTIES
+from flwr.common.recordset_compat import getpropertiesins_to_recordset
+
+from .raybackend import RayBackend
+
+
+class DummyClient(NumPyClient):
+    """A dummy NumPyClient for tests."""
+
+    def get_properties(self, config: Config) -> Dict[str, Scalar]:
+        """Return properties by doing a simple calculation."""
+        result = float(config["factor"]) * pi
+
+        # store something in context
+        self.context.state.configs_records["result"] = ConfigsRecord({"result": result})
+        return {"result": result}
+
+
+def get_dummy_client(cid: str) -> Client:  # pylint: disable=unused-argument
+    """Return a DummyClient converted to Client type."""
+    return DummyClient().to_client()
+
+
+def _load_app() -> ClientApp:
+    return ClientApp(client_fn=get_dummy_client)
+
+
+async def backend_build_process_and_termination(
+    backend: RayBackend,
+    process_args: Optional[Tuple[Callable[[], ClientApp], Message, Context]] = None,
+) -> Union[Tuple[Message, Context], None]:
+    """Build, process job and terminate RayBackend."""
+    await backend.build()
+    to_return = None
+
+    if process_args:
+        to_return = await backend.process_message(*process_args)
+
+    await backend.terminate()
+
+    ray.shutdown()
+
+    return to_return
+
+
+class AsyncTestRayBackend(IsolatedAsyncioTestCase):
+    """A basic class that allows runnig multliple asyncio tests."""
+
+    def test_backend_creation_and_termination(self) -> None:
+        """Test creation of RayBackend and its termination."""
+        backend = RayBackend(backend_config={}, work_dir="")
+        asyncio.run(
+            backend_build_process_and_termination(backend=backend, process_args=None)
+        )
+
+    def test_backend_creation_submit_and_termination(self) -> None:
+        """Test submit."""
+        backend = RayBackend(backend_config={}, work_dir="")
+
+        # Define ClientApp
+        client_app_callable = _load_app
+
+        # Construct a Message
+        mult_factor = 2024
+        getproperties_ins = GetPropertiesIns(config={"factor": mult_factor})
+        recordset = getpropertiesins_to_recordset(getproperties_ins)
+        message = Message(
+            content=recordset,
+            metadata=Metadata(
+                run_id=0,
+                message_id="",
+                group_id="",
+                src_node_id=0,
+                dst_node_id=0,
+                reply_to_message="",
+                ttl="",
+                message_type=MESSAGE_TYPE_GET_PROPERTIES,
+            ),
+        )
+
+        # Construct emtpy Context
+        context = Context(state=RecordSet())
+
+        res = asyncio.run(
+            backend_build_process_and_termination(
+                backend=backend, process_args=(client_app_callable, message, context)
+            )
+        )
+
+        if res is None:
+            raise AssertionError("This shouldn't happen")
+
+        out_mssg, updated_context = res
+
+        # Verify message content is as expected
+        content = out_mssg.content
+        assert (
+            content.configs_records["getpropertiesres.properties"]["result"]
+            == pi * mult_factor
+        )
+
+        # Verify context is correct
+        obtained_result_in_context = updated_context.state.configs_records["result"][
+            "result"
+        ]
+        assert obtained_result_in_context == pi * mult_factor

From 514f670948776ee3468388ca22540e8e4d64ee4f Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Mon, 26 Feb 2024 22:03:20 +0100
Subject: [PATCH 059/102] Make `InMemoryState` thread-safe when handling
 `TaskIns` (#3012)

---
 .../server/superlink/state/in_memory_state.py | 41 +++++++++++--------
 1 file changed, 23 insertions(+), 18 deletions(-)

diff --git a/src/py/flwr/server/superlink/state/in_memory_state.py b/src/py/flwr/server/superlink/state/in_memory_state.py
index ecb39f18300a..690fadc032d7 100644
--- a/src/py/flwr/server/superlink/state/in_memory_state.py
+++ b/src/py/flwr/server/superlink/state/in_memory_state.py
@@ -16,6 +16,7 @@
 
 
 import os
+import threading
 from datetime import datetime, timedelta
 from logging import ERROR
 from typing import Dict, List, Optional, Set
@@ -35,6 +36,7 @@ def __init__(self) -> None:
         self.run_ids: Set[int] = set()
         self.task_ins_store: Dict[UUID, TaskIns] = {}
         self.task_res_store: Dict[UUID, TaskRes] = {}
+        self.lock = threading.Lock()
 
     def store_task_ins(self, task_ins: TaskIns) -> Optional[UUID]:
         """Store one TaskIns."""
@@ -57,7 +59,8 @@ def store_task_ins(self, task_ins: TaskIns) -> Optional[UUID]:
         task_ins.task_id = str(task_id)
         task_ins.task.created_at = created_at.isoformat()
         task_ins.task.ttl = ttl.isoformat()
-        self.task_ins_store[task_id] = task_ins
+        with self.lock:
+            self.task_ins_store[task_id] = task_ins
 
         # Return the new task_id
         return task_id
@@ -71,22 +74,23 @@ def get_task_ins(
 
         # Find TaskIns for node_id that were not delivered yet
         task_ins_list: List[TaskIns] = []
-        for _, task_ins in self.task_ins_store.items():
-            # pylint: disable=too-many-boolean-expressions
-            if (
-                node_id is not None  # Not anonymous
-                and task_ins.task.consumer.anonymous is False
-                and task_ins.task.consumer.node_id == node_id
-                and task_ins.task.delivered_at == ""
-            ) or (
-                node_id is None  # Anonymous
-                and task_ins.task.consumer.anonymous is True
-                and task_ins.task.consumer.node_id == 0
-                and task_ins.task.delivered_at == ""
-            ):
-                task_ins_list.append(task_ins)
-            if limit and len(task_ins_list) == limit:
-                break
+        with self.lock:
+            for _, task_ins in self.task_ins_store.items():
+                # pylint: disable=too-many-boolean-expressions
+                if (
+                    node_id is not None  # Not anonymous
+                    and task_ins.task.consumer.anonymous is False
+                    and task_ins.task.consumer.node_id == node_id
+                    and task_ins.task.delivered_at == ""
+                ) or (
+                    node_id is None  # Anonymous
+                    and task_ins.task.consumer.anonymous is True
+                    and task_ins.task.consumer.node_id == 0
+                    and task_ins.task.delivered_at == ""
+                ):
+                    task_ins_list.append(task_ins)
+                if limit and len(task_ins_list) == limit:
+                    break
 
         # Mark all of them as delivered
         delivered_at = now().isoformat()
@@ -164,7 +168,8 @@ def delete_tasks(self, task_ids: Set[UUID]) -> None:
                 task_res_to_be_deleted.add(task_res_id)
 
         for task_id in task_ins_to_be_deleted:
-            del self.task_ins_store[task_id]
+            with self.lock:
+                del self.task_ins_store[task_id]
         for task_id in task_res_to_be_deleted:
             del self.task_res_store[task_id]
 

From 39ef78bf6e742ea7f7a9098ef7e265cb8d60372d Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Mon, 26 Feb 2024 22:28:40 +0100
Subject: [PATCH 060/102] Fix incorrect link in FedPara README (#3014)

---
 baselines/fedpara/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/baselines/fedpara/README.md b/baselines/fedpara/README.md
index 0ff9203a9a58..89cca76a2aa2 100644
--- a/baselines/fedpara/README.md
+++ b/baselines/fedpara/README.md
@@ -93,7 +93,7 @@ As for the parameters ratio ($\gamma$) we use the following model sizes. As in t
 ## Environment Setup
 To construct the Python environment follow these steps:
 
-It is assumed that `pyenv` is installed, `poetry` is installed and python 3.10.6 is installed using `pyenv`. Refer to this [documentation](https://flower.ai/docs/baselines/how-to-usef-baselines.html#setting-up-your-machine) to ensure that your machine is ready.
+It is assumed that `pyenv` is installed, `poetry` is installed and python 3.10.6 is installed using `pyenv`. Refer to this [documentation](https://flower.ai/docs/baselines/how-to-use-baselines.html#setting-up-your-machine) to ensure that your machine is ready.
 
 ```bash
 # Set Python 3.10

From ccb0b35ce0477a9048162a1a43069eabc951c66b Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Mon, 26 Feb 2024 22:46:20 +0100
Subject: [PATCH 061/102] Add `partition_id` to `Metadata` (#3013)

---
 .../client/message_handler/message_handler.py |  2 +-
 .../message_handler/message_handler_test.py   |  2 ++
 src/py/flwr/common/message.py                 | 19 ++++++++++++++++++-
 .../ray_transport/ray_client_proxy.py         |  1 +
 .../ray_transport/ray_client_proxy_test.py    |  3 ++-
 5 files changed, 24 insertions(+), 3 deletions(-)

diff --git a/src/py/flwr/client/message_handler/message_handler.py b/src/py/flwr/client/message_handler/message_handler.py
index e7e6c7e05c71..87cace88ec27 100644
--- a/src/py/flwr/client/message_handler/message_handler.py
+++ b/src/py/flwr/client/message_handler/message_handler.py
@@ -98,7 +98,7 @@ def handle_legacy_message_from_msgtype(
     client_fn: ClientFn, message: Message, context: Context
 ) -> Message:
     """Handle legacy message in the inner most mod."""
-    client = client_fn(str(message.metadata.dst_node_id))
+    client = client_fn(str(message.metadata.partition_id))
 
     # Check if NumPyClient is returend
     if isinstance(client, NumPyClient):
diff --git a/src/py/flwr/client/message_handler/message_handler_test.py b/src/py/flwr/client/message_handler/message_handler_test.py
index 9fc126f27923..c24b51972f30 100644
--- a/src/py/flwr/client/message_handler/message_handler_test.py
+++ b/src/py/flwr/client/message_handler/message_handler_test.py
@@ -269,6 +269,8 @@ def test_invalid_message_run_id(self) -> None:
         invalid_metadata_list: List[Metadata] = []
         attrs = list(vars(self.valid_out_metadata).keys())
         for attr in attrs:
+            if attr == "_partition_id":
+                continue
             if attr == "_ttl":  # Skip configurable ttl
                 continue
             # Make an invalid metadata
diff --git a/src/py/flwr/common/message.py b/src/py/flwr/common/message.py
index 14dae0f6ee57..1e1132e42e27 100644
--- a/src/py/flwr/common/message.py
+++ b/src/py/flwr/common/message.py
@@ -14,7 +14,6 @@
 # ==============================================================================
 """Message."""
 
-
 from __future__ import annotations
 
 from dataclasses import dataclass
@@ -46,6 +45,10 @@ class Metadata:  # pylint: disable=too-many-instance-attributes
     message_type : str
         A string that encodes the action to be executed on
         the receiving end.
+    partition_id : Optional[int]
+        An identifier that can be used when loading a particular
+        data partition for a ClientApp. Making use of this identifier
+        is more relevant when conducting simulations.
     """
 
     _run_id: int
@@ -56,6 +59,7 @@ class Metadata:  # pylint: disable=too-many-instance-attributes
     _group_id: str
     _ttl: str
     _message_type: str
+    _partition_id: int | None
 
     def __init__(  # pylint: disable=too-many-arguments
         self,
@@ -67,6 +71,7 @@ def __init__(  # pylint: disable=too-many-arguments
         group_id: str,
         ttl: str,
         message_type: str,
+        partition_id: int | None = None,
     ) -> None:
         self._run_id = run_id
         self._message_id = message_id
@@ -76,6 +81,7 @@ def __init__(  # pylint: disable=too-many-arguments
         self._group_id = group_id
         self._ttl = ttl
         self._message_type = message_type
+        self._partition_id = partition_id
 
     @property
     def run_id(self) -> int:
@@ -137,6 +143,16 @@ def message_type(self, value: str) -> None:
         """Set message_type."""
         self._message_type = value
 
+    @property
+    def partition_id(self) -> int | None:
+        """An identifier telling which data partition a ClientApp should use."""
+        return self._partition_id
+
+    @partition_id.setter
+    def partition_id(self, value: int) -> None:
+        """Set patition_id."""
+        self._partition_id = value
+
 
 @dataclass
 class Message:
@@ -202,6 +218,7 @@ def create_reply(self, content: RecordSet, ttl: str) -> Message:
                 group_id=self.metadata.group_id,
                 ttl=ttl,
                 message_type=self.metadata.message_type,
+                partition_id=self.metadata.partition_id,
             ),
             content=content,
         )
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
index 405e0920c5a4..a45321ed2368 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
@@ -111,6 +111,7 @@ def _wrap_recordset_in_message(
                 reply_to_message="",
                 ttl=str(timeout) if timeout else "",
                 message_type=message_type,
+                partition_id=int(self.cid),
             ),
         )
 
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
index 3eeabe0292c9..24fe3546e7d9 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
@@ -198,10 +198,11 @@ def _load_app() -> ClientApp:
                 message_id="",
                 group_id="",
                 src_node_id=0,
-                dst_node_id=int(cid),
+                dst_node_id=12345,
                 reply_to_message="",
                 ttl="",
                 message_type=MESSAGE_TYPE_GET_PROPERTIES,
+                partition_id=int(cid),
             ),
         )
         pool.submit_client_job(

From 7bfc58ad98eef114f6c876e6a364082e5507f38f Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Mon, 26 Feb 2024 22:57:29 +0100
Subject: [PATCH 062/102] Fix incorrect URLs for baseline doc page (#3015)

---
 doc/locales/fr/LC_MESSAGES/framework-docs.po      | 6 +++---
 doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po | 2 +-
 doc/source/ref-changelog.md                       | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/doc/locales/fr/LC_MESSAGES/framework-docs.po b/doc/locales/fr/LC_MESSAGES/framework-docs.po
index 920a47abab3b..ba5ea5ec070d 100644
--- a/doc/locales/fr/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/fr/LC_MESSAGES/framework-docs.po
@@ -1325,7 +1325,7 @@ msgid ""
 msgstr ""
 "Si tu n'es pas familier avec les Flower Baselines, tu devrais "
 "probablement consulter notre `guide de contribution pour les baselines "
-"<https://flower.ai/docs/contributing-baselines.html>`_."
+"<https://flower.ai/docs/baselines/how-to-contribute-baselines.html>`_."
 
 #: ../../source/contributor-ref-good-first-contributions.rst:27
 msgid ""
@@ -15862,7 +15862,7 @@ msgstr ""
 "l'utilisation de [Flower Baselines](https://flower.ai/docs/using-"
 "baselines.html). Avec cette première version préliminaire, nous invitons "
 "également la communauté à [contribuer à leurs propres lignes de "
-"base](https://flower.ai/docs/contributing-baselines.html)."
+"base](https://flower.ai/docs/baselines/how-to-contribute-baselines.html)."
 
 #: ../../source/ref-changelog.md:662
 msgid ""
@@ -25474,7 +25474,7 @@ msgstr ""
 #~ " papers. If you want to add a"
 #~ " new baseline or experiment, please "
 #~ "check the `Contributing Baselines "
-#~ "<https://flower.ai/docs/contributing-baselines.html>`_ "
+#~ "<https://flower.ai/docs/baselines/how-to-contribute-baselines.html>`_ "
 #~ "section."
 #~ msgstr ""
 
diff --git a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
index ab1c8dc39e64..b6c32f994597 100644
--- a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
@@ -1230,7 +1230,7 @@ msgid ""
 "/contributing-baselines.html>`_."
 msgstr ""
 "如果您对 Flower Baselines 还不熟悉，也许可以看看我们的 `Baselines贡献指南 "
-"<https://flower.ai/docs/contributing-baselines.html>`_。"
+"<https://flower.ai/docs/baselines/how-to-contribute-baselines.html>`_。"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:27
 msgid ""
diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 41dc91873c6c..54092e15a564 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -657,7 +657,7 @@ We would like to give our **special thanks** to all the contributors who made Fl
 
 - **Flower Baselines (preview): FedOpt, FedBN, FedAvgM** ([#919](https://github.com/adap/flower/pull/919), [#1127](https://github.com/adap/flower/pull/1127), [#914](https://github.com/adap/flower/pull/914))
 
-  The first preview release of Flower Baselines has arrived! We're kickstarting Flower Baselines with implementations of FedOpt (FedYogi, FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how to use [Flower Baselines](https://flower.ai/docs/using-baselines.html). With this first preview release we're also inviting the community to [contribute their own baselines](https://flower.ai/docs/contributing-baselines.html).
+  The first preview release of Flower Baselines has arrived! We're kickstarting Flower Baselines with implementations of FedOpt (FedYogi, FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how to use [Flower Baselines](https://flower.ai/docs/using-baselines.html). With this first preview release we're also inviting the community to [contribute their own baselines](https://flower.ai/docs/baselines/how-to-contribute-baselines.html).
 
 - **C++ client SDK (preview) and code example** ([#1111](https://github.com/adap/flower/pull/1111))
 

From 4abfd066b444e6dbc01b2944ba5934dfa3e39b03 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Tue, 27 Feb 2024 10:27:19 +0100
Subject: [PATCH 063/102] Add DirichletPartitioner (#2795)

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 .../flwr_datasets/partitioner/__init__.py     |   2 +
 .../partitioner/dirichlet_partitioner.py      | 323 ++++++++++++++++++
 .../partitioner/dirichlet_partitioner_test.py | 170 +++++++++
 3 files changed, 495 insertions(+)
 create mode 100644 datasets/flwr_datasets/partitioner/dirichlet_partitioner.py
 create mode 100644 datasets/flwr_datasets/partitioner/dirichlet_partitioner_test.py

diff --git a/datasets/flwr_datasets/partitioner/__init__.py b/datasets/flwr_datasets/partitioner/__init__.py
index 5e7c86718f67..6a85f8a11749 100644
--- a/datasets/flwr_datasets/partitioner/__init__.py
+++ b/datasets/flwr_datasets/partitioner/__init__.py
@@ -15,6 +15,7 @@
 """Flower Datasets Partitioner package."""
 
 
+from .dirichlet_partitioner import DirichletPartitioner
 from .exponential_partitioner import ExponentialPartitioner
 from .iid_partitioner import IidPartitioner
 from .linear_partitioner import LinearPartitioner
@@ -27,6 +28,7 @@
     "IidPartitioner",
     "Partitioner",
     "NaturalIdPartitioner",
+    "DirichletPartitioner",
     "SizePartitioner",
     "LinearPartitioner",
     "SquarePartitioner",
diff --git a/datasets/flwr_datasets/partitioner/dirichlet_partitioner.py b/datasets/flwr_datasets/partitioner/dirichlet_partitioner.py
new file mode 100644
index 000000000000..5f1df71991bb
--- /dev/null
+++ b/datasets/flwr_datasets/partitioner/dirichlet_partitioner.py
@@ -0,0 +1,323 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Dirichlet partitioner class that works with Hugging Face Datasets."""
+
+
+import warnings
+from typing import Dict, List, Optional, Union
+
+import numpy as np
+
+import datasets
+from flwr_datasets.common.typing import NDArrayFloat
+from flwr_datasets.partitioner.partitioner import Partitioner
+
+
+# pylint: disable=R0902, R0912
+class DirichletPartitioner(Partitioner):
+    """Partitioner based on Dirichlet distribution.
+
+    Implementation based on Bayesian Nonparametric Federated Learning of Neural Networks
+    https://arxiv.org/abs/1905.12022.
+
+    The algorithm sequentially divides the data with each label. The fractions of the
+    data with each label is drawn from Dirichlet distribution and adjusted in case of
+    balancing. The data is assigned. In case the `min_partition_size` is not satisfied
+    the algorithm is run again (the fractions will change since it is a random process
+    even though the alpha stays the same).
+
+    The notion of balancing is explicitly introduced here (not mentioned in paper but
+    implemented in the code). It is a mechanism that excludes the node from
+    assigning new samples to it if the current number of samples on that node exceeds
+    the average number that the node would get in case of even data distribution.
+    It is controlled by`self_balancing` parameter.
+
+    Parameters
+    ----------
+    num_partitions : int
+        The total number of partitions that the data will be divided into.
+    partition_by : str
+        Column name of the labels (targets) based on which Dirichlet sampling works.
+    alpha : Union[int, float, List[float], NDArrayFloat]
+        Concentration parameter to the Dirichlet distribution
+    min_partition_size : int
+        The minimum number of samples that each partitions will have (the sampling
+        process is repeated if any partition is too small).
+    self_balancing : bool
+        Whether assign further samples to a partition after the number of samples
+        exceeded the average number of samples per partition. (True in the original
+        paper's code although not mentioned in paper itself).
+    shuffle: bool
+        Whether to randomize the order of samples. Shuffling applied after the
+        samples assignment to nodes.
+    seed: int
+        Seed used for dataset shuffling. It has no effect if `shuffle` is False.
+
+    Examples
+    --------
+    >>> from flwr_datasets import FederatedDataset
+    >>> from flwr_datasets.partitioner import DirichletPartitioner
+    >>>
+    >>> partitioner = DirichletPartitioner(num_partitions=10, partition_by="label",
+    >>>                                    alpha=0.5, min_partition_size=10,
+    >>>                                    self_balancing=True)
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
+    >>> partition = fds.load_partition(0)
+    >>> print(partition[0])  # Print the first example
+    {'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x127B92170>,
+    'label': 4}
+    >>> partition_sizes = [len(fds.load_partition(node_id)) for node_id in range(10)]
+    >>> print(sorted(partition_sizes))
+    [2134, 2615, 3646, 6011, 6170, 6386, 6715, 7653, 8435, 10235]
+    """
+
+    def __init__(  # pylint: disable=R0913
+        self,
+        num_partitions: int,
+        partition_by: str,
+        alpha: Union[int, float, List[float], NDArrayFloat],
+        min_partition_size: int = 10,
+        self_balancing: bool = True,
+        shuffle: bool = True,
+        seed: Optional[int] = 42,
+    ) -> None:
+        super().__init__()
+        # Attributes based on the constructor
+        self._num_partitions = num_partitions
+        self._check_num_partitions_greater_than_zero()
+        self._alpha: NDArrayFloat = self._initialize_alpha(alpha)
+        self._partition_by = partition_by
+        self._min_partition_size: int = min_partition_size
+        self._self_balancing = self_balancing
+        self._shuffle = shuffle
+        self._seed = seed
+        self._rng = np.random.default_rng(seed=self._seed)  # NumPy random generator
+
+        # Utility attributes
+        # The attributes below are determined during the first call to load_partition
+        self._avg_num_of_samples_per_node: Optional[float] = None
+        self._unique_classes: Optional[Union[List[int], List[str]]] = None
+        self._node_id_to_indices: Dict[int, List[int]] = {}
+        self._node_id_to_indices_determined = False
+
+    def load_partition(self, node_id: int) -> datasets.Dataset:
+        """Load a partition based on the partition index.
+
+        Parameters
+        ----------
+        node_id : int
+            the index that corresponds to the requested partition
+
+        Returns
+        -------
+        dataset_partition : Dataset
+            single partition of a dataset
+        """
+        # The partitioning is done lazily - only when the first partition is
+        # requested. Only the first call creates the indices assignments for all the
+        # partition indices.
+        self._check_num_partitions_correctness_if_needed()
+        self._determine_node_id_to_indices_if_needed()
+        return self.dataset.select(self._node_id_to_indices[node_id])
+
+    def _initialize_alpha(
+        self, alpha: Union[int, float, List[float], NDArrayFloat]
+    ) -> NDArrayFloat:
+        """Convert alpha to the used format in the code a NDArrayFloat.
+
+        The alpha can be provided in constructor can be in different format for user
+        convenience. The format into which it's transformed here is used throughout the
+        code for computation.
+
+        Parameters
+        ----------
+            alpha : Union[int, float, List[float], NDArrayFloat]
+                Concentration parameter to the Dirichlet distribution
+
+        Returns
+        -------
+        alpha : NDArrayFloat
+            Concentration parameter in a format ready to used in computation.
+        """
+        if isinstance(alpha, int):
+            alpha = np.array([float(alpha)], dtype=float).repeat(self._num_partitions)
+        elif isinstance(alpha, float):
+            alpha = np.array([alpha], dtype=float).repeat(self._num_partitions)
+        elif isinstance(alpha, List):
+            if len(alpha) != self._num_partitions:
+                raise ValueError(
+                    "If passing alpha as a List, it needs to be of length of equal to "
+                    "num_partitions."
+                )
+            alpha = np.asarray(alpha)
+        elif isinstance(alpha, np.ndarray):
+            # pylint: disable=R1720
+            if alpha.ndim == 1 and alpha.shape[0] != self._num_partitions:
+                raise ValueError(
+                    "If passing alpha as an NDArray, its length needs to be of length "
+                    "equal to num_partitions."
+                )
+            elif alpha.ndim == 2:
+                alpha = alpha.flatten()
+                if alpha.shape[0] != self._num_partitions:
+                    raise ValueError(
+                        "If passing alpha as an NDArray, its size needs to be of length"
+                        " equal to num_partitions."
+                    )
+        else:
+            raise ValueError("The given alpha format is not supported.")
+        if not (alpha > 0).all():
+            raise ValueError(
+                f"Alpha values should be strictly greater than zero. "
+                f"Instead it'd be converted to {alpha}"
+            )
+        return alpha
+
+    def _determine_node_id_to_indices_if_needed(self) -> None:  # pylint: disable=R0914
+        """Create an assignment of indices to the partition indices."""
+        if self._node_id_to_indices_determined:
+            return
+
+        # Generate information needed for Dirichlet partitioning
+        self._unique_classes = self.dataset.unique(self._partition_by)
+        assert self._unique_classes is not None
+        # This is needed only if self._self_balancing is True (the default option)
+        self._avg_num_of_samples_per_node = self.dataset.num_rows / self._num_partitions
+
+        # Change targets list data type to numpy
+        targets = np.array(self.dataset[self._partition_by])
+
+        # Repeat the sampling procedure based on the Dirichlet distribution until the
+        # min_partition_size is reached.
+        sampling_try = 0
+        while True:
+            # Prepare data structure to store indices assigned to node ids
+            node_id_to_indices: Dict[int, List[int]] = {}
+            for nid in range(self._num_partitions):
+                node_id_to_indices[nid] = []
+
+            # Iterated over all unique labels (they are not necessarily of type int)
+            for k in self._unique_classes:
+                # Access all the indices associated with class k
+                indices_representing_class_k = np.nonzero(targets == k)[0]
+                # Determine division (the fractions) of the data representing class k
+                # among the partitions
+                class_k_division_proportions = self._rng.dirichlet(self._alpha)
+                nid_to_proportion_of_k_samples = {}
+                for nid in range(self._num_partitions):
+                    nid_to_proportion_of_k_samples[nid] = class_k_division_proportions[
+                        nid
+                    ]
+                # Balancing (not mentioned in the paper but implemented)
+                # Do not assign additional samples to the node if it already has more
+                # than the average numbers of samples per partition. Note that it might
+                # especially affect classes that are later in the order. This is the
+                # reason for more sparse division that the alpha might suggest.
+                if self._self_balancing:
+                    assert self._avg_num_of_samples_per_node is not None
+                    for nid in nid_to_proportion_of_k_samples.copy():
+                        if (
+                            len(node_id_to_indices[nid])
+                            > self._avg_num_of_samples_per_node
+                        ):
+                            nid_to_proportion_of_k_samples[nid] = 0
+
+                    # Normalize the proportions such that they sum up to 1
+                    sum_proportions = sum(nid_to_proportion_of_k_samples.values())
+                    for nid, prop in nid_to_proportion_of_k_samples.copy().items():
+                        nid_to_proportion_of_k_samples[nid] = prop / sum_proportions
+
+                # Determine the split indices
+                cumsum_division_fractions = np.cumsum(
+                    list(nid_to_proportion_of_k_samples.values())
+                )
+                cumsum_division_numbers = cumsum_division_fractions * len(
+                    indices_representing_class_k
+                )
+                # [:-1] is because the np.split requires the division indices but the
+                # last element represents the sum = total number of samples
+                indices_on_which_split = cumsum_division_numbers.astype(int)[:-1]
+
+                split_indices = np.split(
+                    indices_representing_class_k, indices_on_which_split
+                )
+
+                # Append new indices (coming from class k) to the existing indices
+                for nid, indices in node_id_to_indices.items():
+                    indices.extend(split_indices[nid].tolist())
+
+            # Determine if the indices assignment meets the min_partition_size
+            # If it does not mean the requirement repeat the Dirichlet sampling process
+            # Otherwise break the while loop
+            min_sample_size_on_client = min(
+                len(indices) for indices in node_id_to_indices.values()
+            )
+            if min_sample_size_on_client >= self._min_partition_size:
+                break
+            sample_sizes = [len(indices) for indices in node_id_to_indices.values()]
+            alpha_not_met = [
+                self._alpha[i]
+                for i, ss in enumerate(sample_sizes)
+                if ss == min(sample_sizes)
+            ]
+            mssg_list_alphas = (
+                (
+                    "Generating partitions by sampling from a list of very wide range "
+                    "of alpha values can be hard to achieve. Try reducing the range "
+                    f"between maximum ({max(self._alpha)}) and minimum alpha "
+                    f"({min(self._alpha)}) values or increasing all the values."
+                )
+                if len(self._alpha.flatten().tolist()) > 0
+                else ""
+            )
+            warnings.warn(
+                f"The specified min_partition_size ({self._min_partition_size}) was "
+                f"not satisfied for alpha ({alpha_not_met}) after "
+                f"{sampling_try} attempts at sampling from the Dirichlet "
+                f"distribution. The probability sampling from the Dirichlet "
+                f"distribution will be repeated. Note: This is not a desired "
+                f"behavior. It is recommended to adjust the alpha or "
+                f"min_partition_size instead. {mssg_list_alphas}",
+                stacklevel=1,
+            )
+            if sampling_try == 10:
+                raise ValueError(
+                    "The max number of attempts (10) was reached. "
+                    "Please update the values of alpha and try again."
+                )
+            sampling_try += 1
+
+        # Shuffle the indices not to have the datasets with targets in sequences like
+        # [00000, 11111, ...]) if the shuffle is True
+        if self._shuffle:
+            for indices in node_id_to_indices.values():
+                # In place shuffling
+                self._rng.shuffle(indices)
+        self._node_id_to_indices = node_id_to_indices
+        self._node_id_to_indices_determined = True
+
+    def _check_num_partitions_correctness_if_needed(self) -> None:
+        """Test num_partitions when the dataset is given (in load_partition)."""
+        if not self._node_id_to_indices_determined:
+            if self._num_partitions > self.dataset.num_rows:
+                raise ValueError(
+                    "The number of partitions needs to be smaller than the number of "
+                    "samples in the dataset."
+                )
+
+    def _check_num_partitions_greater_than_zero(self) -> None:
+        """Test num_partition left sides correctness."""
+        if not self._num_partitions > 0:
+            raise ValueError("The number of partitions needs to be greater than zero.")
diff --git a/datasets/flwr_datasets/partitioner/dirichlet_partitioner_test.py b/datasets/flwr_datasets/partitioner/dirichlet_partitioner_test.py
new file mode 100644
index 000000000000..c123f84effb7
--- /dev/null
+++ b/datasets/flwr_datasets/partitioner/dirichlet_partitioner_test.py
@@ -0,0 +1,170 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test DirichletPartitioner."""
+
+
+# pylint: disable=W0212
+import unittest
+from typing import Tuple, Union
+
+import numpy as np
+from numpy.typing import NDArray
+from parameterized import parameterized
+
+from datasets import Dataset
+from flwr_datasets.partitioner.dirichlet_partitioner import DirichletPartitioner
+
+
+def _dummy_setup(
+    num_partitions: int,
+    alpha: Union[float, NDArray[np.float_]],
+    num_rows: int,
+    partition_by: str,
+    self_balancing: bool = True,
+) -> Tuple[Dataset, DirichletPartitioner]:
+    """Create a dummy dataset and partitioner for testing."""
+    data = {
+        partition_by: [i % 3 for i in range(num_rows)],
+        "features": list(range(num_rows)),
+    }
+    dataset = Dataset.from_dict(data)
+    partitioner = DirichletPartitioner(
+        num_partitions=num_partitions,
+        alpha=alpha,
+        partition_by=partition_by,
+        self_balancing=self_balancing,
+    )
+    partitioner.dataset = dataset
+    return dataset, partitioner
+
+
+class TestDirichletPartitionerSuccess(unittest.TestCase):
+    """Test DirichletPartitioner used with no exceptions."""
+
+    @parameterized.expand(  # type: ignore
+        [
+            # num_partitions, alpha, num_rows, partition_by
+            (3, 0.5, 100, "labels"),
+            (5, 1.0, 150, "labels"),
+        ]
+    )
+    def test_valid_initialization(
+        self, num_partitions: int, alpha: float, num_rows: int, partition_by: str
+    ) -> None:
+        """Test if alpha is correct scaled based on the given num_partitions."""
+        _, partitioner = _dummy_setup(num_partitions, alpha, num_rows, partition_by)
+        self.assertEqual(
+            (
+                partitioner._num_partitions,
+                len(partitioner._alpha),
+                partitioner._partition_by,
+            ),
+            (num_partitions, num_partitions, partition_by),
+        )
+
+    def test_min_partition_size_requirement(self) -> None:
+        """Test if partitions are created with min partition size required."""
+        _, partitioner = _dummy_setup(3, 0.5, 100, "labels")
+        partition_list = [partitioner.load_partition(node_id) for node_id in [0, 1, 2]]
+        self.assertTrue(
+            all(len(p) > partitioner._min_partition_size for p in partition_list)
+        )
+
+    def test_alpha_in_ndarray_initialization(self) -> None:
+        """Test alpha does not change when in NDArrayFloat format."""
+        _, partitioner = _dummy_setup(3, np.array([1.0, 1.0, 1.0]), 100, "labels")
+        self.assertTrue(np.all(partitioner._alpha == np.array([1.0, 1.0, 1.0])))
+
+    def test__determine_node_id_to_indices(self) -> None:
+        """Test the determine_nod_id_to_indices matches the flag after the call."""
+        num_partitions, alpha, num_rows, partition_by = 3, 0.5, 100, "labels"
+        _, partitioner = _dummy_setup(num_partitions, alpha, num_rows, partition_by)
+        partitioner._determine_node_id_to_indices_if_needed()
+        self.assertTrue(
+            partitioner._node_id_to_indices_determined
+            and len(partitioner._node_id_to_indices) == num_partitions
+        )
+
+
+class TestDirichletPartitionerFailure(unittest.TestCase):
+    """Test DirichletPartitioner failures (exceptions) by incorrect usage."""
+
+    @parameterized.expand([(-2,), (-1,), (3,), (4,), (100,)])  # type: ignore
+    def test_load_invalid_partition_index(self, partition_id):
+        """Test if raises when the load_partition is above the num_partitions."""
+        _, partitioner = _dummy_setup(3, 0.5, 100, "labels")
+        with self.assertRaises(KeyError):
+            partitioner.load_partition(partition_id)
+
+    @parameterized.expand(  # type: ignore
+        [
+            # alpha, num_partitions
+            (-0.5, 1),
+            (-0.5, 2),
+            (-0.5, 3),
+            (-0.5, 10),
+            ([0.5, 0.5, -0.5], 3),
+            ([-0.5, 0.5, -0.5], 3),
+            ([-0.5, 0.5, 0.5], 3),
+            ([-0.5, -0.5, -0.5], 3),
+            ([0.5, 0.5, -0.5, -0.5, 0.5], 5),
+            (np.array([0.5, 0.5, -0.5]), 3),
+            (np.array([-0.5, 0.5, -0.5]), 3),
+            (np.array([-0.5, 0.5, 0.5]), 3),
+            (np.array([-0.5, -0.5, -0.5]), 3),
+            (np.array([0.5, 0.5, -0.5, -0.5, 0.5]), 5),
+        ]
+    )
+    def test_negative_values_in_alpha(self, alpha, num_partitions):
+        """Test if giving the negative value of alpha raises error."""
+        num_rows, partition_by = 100, "labels"
+        with self.assertRaises(ValueError):
+            _, _ = _dummy_setup(num_partitions, alpha, num_rows, partition_by)
+
+    @parameterized.expand(  # type: ignore
+        [
+            # alpha, num_partitions
+            # alpha greater than the num_partitions
+            ([0.5, 0.5], 1),
+            ([0.5, 0.5, 0.5], 2),
+            (np.array([0.5, 0.5]), 1),
+            (np.array([0.5, 0.5, 0.5]), 2),
+            (np.array([0.5, 0.5, 0.5, 0.5]), 3),
+        ]
+    )
+    def test_incorrect_alpha_shape(self, alpha, num_partitions):
+        """Test alpha list len not matching the num_partitions."""
+        with self.assertRaises(ValueError):
+            DirichletPartitioner(
+                num_partitions=num_partitions, alpha=alpha, partition_by="labels"
+            )
+
+    @parameterized.expand(  # type: ignore
+        [(0,), (-1,), (11,), (100,)]
+    )  # num_partitions,
+    def test_invalid_num_partitions(self, num_partitions):
+        """Test if 0 is invalid num_partitions."""
+        with self.assertRaises(ValueError):
+            _, partitioner = _dummy_setup(
+                num_partitions=num_partitions,
+                alpha=1.0,
+                num_rows=10,
+                partition_by="labels",
+            )
+            partitioner.load_partition(0)
+
+
+if __name__ == "__main__":
+    unittest.main()

From 65f77a98bfb8fe418ddf96641f4cdbf1ba3a07f0 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Tue, 27 Feb 2024 10:44:22 +0100
Subject: [PATCH 064/102] Add ShardPartitioner (#2792)

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 .../flwr_datasets/partitioner/__init__.py     |   2 +
 .../partitioner/shard_partitioner.py          | 354 ++++++++++++++++
 .../partitioner/shard_partitioner_test.py     | 392 ++++++++++++++++++
 3 files changed, 748 insertions(+)
 create mode 100644 datasets/flwr_datasets/partitioner/shard_partitioner.py
 create mode 100644 datasets/flwr_datasets/partitioner/shard_partitioner_test.py

diff --git a/datasets/flwr_datasets/partitioner/__init__.py b/datasets/flwr_datasets/partitioner/__init__.py
index 6a85f8a11749..73d048ddf3ff 100644
--- a/datasets/flwr_datasets/partitioner/__init__.py
+++ b/datasets/flwr_datasets/partitioner/__init__.py
@@ -21,6 +21,7 @@
 from .linear_partitioner import LinearPartitioner
 from .natural_id_partitioner import NaturalIdPartitioner
 from .partitioner import Partitioner
+from .shard_partitioner import ShardPartitioner
 from .size_partitioner import SizePartitioner
 from .square_partitioner import SquarePartitioner
 
@@ -32,5 +33,6 @@
     "SizePartitioner",
     "LinearPartitioner",
     "SquarePartitioner",
+    "ShardPartitioner",
     "ExponentialPartitioner",
 ]
diff --git a/datasets/flwr_datasets/partitioner/shard_partitioner.py b/datasets/flwr_datasets/partitioner/shard_partitioner.py
new file mode 100644
index 000000000000..7c86570fe487
--- /dev/null
+++ b/datasets/flwr_datasets/partitioner/shard_partitioner.py
@@ -0,0 +1,354 @@
+# Copyright 2023 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Shard partitioner class."""
+
+
+# pylint: disable=R0912
+import math
+from typing import Dict, List, Optional
+
+import numpy as np
+
+import datasets
+from flwr_datasets.partitioner.partitioner import Partitioner
+
+
+class ShardPartitioner(Partitioner):  # pylint: disable=R0902
+    """Partitioner based on shard of (typically) unique classes.
+
+    The algorithm works as follows: the dataset is sorted by label e.g. [samples with
+    label 1, samples with labels 2 ...], then the shards are created, with each
+    shard of size = `shard_size` if provided or automatically calculated:
+    shards_size = len(dataset) / `num_partitions` * `num_shards_per_node`.
+
+    A shard is just a block (chunk) of a `dataset` that contains `shard_size`
+    consecutive samples. There might be shards that contain samples associated with more
+    than a single unique label. The first case is (remember the preprocessing step sorts
+    the dataset by label) when a shard is constructed from samples at the boundaries of
+    the sorted dataset and therefore belonging to different classes e.g. the "leftover"
+    of samples of class 1 and the majority of class 2. The another scenario when a shard
+    has samples with more than one unique label is when the shard size is bigger than
+    the  number of samples of a certain class.
+
+    Each partition is created from `num_shards_per_node` that are chosen randomly.
+
+    There are a few ways of partitioning data that result in certain properties
+    (depending on the parameters specification):
+    1) same number of shards per nodes + the same shard size (specify:
+    a) `num_shards_per_nodes`, `shard_size`; or b) `num_shards_per_node`)
+    In case of b the `shard_size` is calculated as floor(len(dataset) /
+    (`num_shards_per_nodes` * `num_partitions`))
+    2) possibly different number of shards per node (use nearly all data) + the same
+    shard size (specify: `shard_size` + `keep_incomplete_shard=False`)
+    3) possibly different number of shards per node (use all data) + possibly different
+    shard size (specify: `shard_size` + `keep_incomplete_shard=True`)
+
+
+    Algorithm based on the description in Communication-Efficient Learning of Deep
+    Networks from Decentralized Data https://arxiv.org/abs/1602.05629. This
+    implementation expands on the initial idea by enabling more hyperparameters
+    specification therefore providing more control on how partitions are created.
+    It enables the division obtained in original paper.
+
+    Parameters
+    ----------
+    num_partitions : int
+        The total number of partitions that the data will be divided into.
+    partition_by : str
+        Column name of the labels (targets) based on which Dirichlet sampling works.
+    num_shards_per_node : Optional[int]
+        Number of shards to assign to a single partitioner. It's an alternative to
+        `num_partitions`.
+    shard_size : Optional[int]
+        Size of a single shards (a partition has one or more shards). If the size is not
+        given it will be automatically computed.
+    keep_incomplete_shard : bool
+        Whether to drop the last shard which might be incomplete (smaller than the
+        others). If it is dropped each shard is equal size. (It does not mean that each
+        client gets equal number of shards, which only happens if
+        `num_partitions` % `num_shards` = 0). This parameter has no effect if
+        `num_shards_per_nodes` and `shard_size` are specified.
+    shuffle: bool
+        Whether to randomize the order of samples. Shuffling applied after the
+        samples assignment to nodes.
+    seed: int
+        Seed used for dataset shuffling. It has no effect if `shuffle` is False.
+
+    Examples
+    --------
+    1) If you need same number of shards per nodes + the same shard size (and you know
+    both of these values)
+    >>> from flwr_datasets import FederatedDataset
+    >>> from flwr_datasets.partitioner import ShardPartitioner
+    >>>
+    >>> partitioner = ShardPartitioner(num_partitions=10, partition_by="label",
+    >>>                                num_shards_per_node=2, shard_size=1_000)
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
+    >>> partition = fds.load_partition(0)
+    >>> print(partition[0])  # Print the first example
+    {'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x15F616C50>,
+    'label': 3}
+    >>> partition_sizes = [len(fds.load_partition(node_id)) for node_id in range(10)]
+    >>> print(partition_sizes)
+    [2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000]
+
+    2) If you want to use nearly all the data and do not need to have the number of
+    shard per each node to be the same
+    >>> from flwr_datasets import FederatedDataset
+    >>> from flwr_datasets.partitioner import ShardPartitioner
+    >>>
+    >>> partitioner = ShardPartitioner(num_partitions=9, partition_by="label",
+    >>>                                shard_size=1_000)
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
+    >>> partition_sizes = [len(fds.load_partition(node_id)) for node_id in range(9)]
+    >>> print(partition_sizes)
+    [7000, 7000, 7000, 7000, 7000, 7000, 6000, 6000, 6000]
+
+    3) If you want to use all the data
+        >>> from flwr_datasets import FederatedDataset
+    >>> from flwr_datasets.partitioner import ShardPartitioner
+    >>>
+    >>> partitioner = ShardPartitioner(num_partitions=10, partition_by="label",
+    >>>                                shard_size=990, keep_incomplete_shard=True)
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
+    >>> partition_sizes = [len(fds.load_partition(node_id)) for node_id in range(10)]
+    >>> print(sorted(partition_sizes))
+    [5550, 5940, 5940, 5940, 5940, 5940, 5940, 5940, 5940, 6930]
+    """
+
+    def __init__(  # pylint: disable=R0913
+        self,
+        num_partitions: int,
+        partition_by: str,
+        num_shards_per_node: Optional[int] = None,
+        shard_size: Optional[int] = None,
+        keep_incomplete_shard: bool = False,
+        shuffle: bool = True,
+        seed: Optional[int] = 42,
+    ) -> None:
+        super().__init__()
+        # Attributes based on the constructor
+        _check_if_natual_number(num_partitions, "num_partitions")
+        self._num_partitions = num_partitions
+        self._partition_by = partition_by
+        _check_if_natual_number(num_shards_per_node, "num_shards_per_node", True)
+        self._num_shards_per_node = num_shards_per_node
+        self._num_shards_used: Optional[int] = None
+        _check_if_natual_number(shard_size, "shard_size", True)
+        self._shard_size = shard_size
+        self._keep_incomplete_shard = keep_incomplete_shard
+        self._shuffle = shuffle
+        self._seed = seed
+
+        # Utility attributes
+        self._rng = np.random.default_rng(seed=self._seed)  # NumPy random generator
+        self._node_id_to_indices: Dict[int, List[int]] = {}
+        self._node_id_to_indices_determined = False
+
+    def load_partition(self, node_id: int) -> datasets.Dataset:
+        """Load a partition based on the partition index.
+
+        Parameters
+        ----------
+        node_id : int
+            the index that corresponds to the requested partition
+
+        Returns
+        -------
+        dataset_partition : Dataset
+            single partition of a dataset
+        """
+        # The partitioning is done lazily - only when the first partition is
+        # requested. Only the first call creates the indices assignments for all the
+        # partition indices.
+        self._check_num_partitions_correctness_if_needed()
+        self._check_possibility_of_partitions_creation()
+        self._sort_dataset_if_needed()
+        self._determine_node_id_to_indices_if_needed()
+        return self.dataset.select(self._node_id_to_indices[node_id])
+
+    def _determine_node_id_to_indices_if_needed(self) -> None:  # pylint: disable=R0914
+        """Assign sample indices to each node id.
+
+        This method works on sorted datasets. A "shard" is a part of the dataset of
+        consecutive samples (if self._keep_incomplete_shard is False, each shard is same
+        size).
+        """
+        # No need to do anything if that node_id_to_indices are already determined
+        if self._node_id_to_indices_determined:
+            return
+
+        # One of the specification allows to skip the `num_shards_per_node` param
+        if self._num_shards_per_node is not None:
+            self._num_shards_used = int(
+                self._num_partitions * self._num_shards_per_node
+            )
+            num_shards_per_node_array = (
+                np.ones(self._num_partitions) * self._num_shards_per_node
+            )
+            if self._shard_size is None:
+                self._compute_shard_size_if_missing()
+                assert self._shard_size is not None
+                if self._keep_incomplete_shard:
+                    num_usable_shards_in_dataset = int(
+                        math.ceil(len(self.dataset) / self._shard_size)
+                    )
+                else:
+                    num_usable_shards_in_dataset = int(
+                        math.floor(len(self.dataset) / self._shard_size)
+                    )
+            else:
+                num_usable_shards_in_dataset = int(
+                    math.floor(len(self.dataset) / self._shard_size)
+                )
+        elif self._num_shards_per_node is None:
+            if self._shard_size is None:
+                raise ValueError(
+                    "The shard_size needs to be specified if the "
+                    "num_shards_per_node is None"
+                )
+            if self._keep_incomplete_shard is False:
+                self._num_shards_used = int(
+                    math.floor(len(self.dataset) / self._shard_size)
+                )
+                num_usable_shards_in_dataset = self._num_shards_used
+            elif self._keep_incomplete_shard is True:
+                self._num_shards_used = int(
+                    math.ceil(len(self.dataset) / self._shard_size)
+                )
+                num_usable_shards_in_dataset = self._num_shards_used
+                if num_usable_shards_in_dataset < self._num_partitions:
+                    raise ValueError(
+                        "Based on the given arguments the creation of the partitions "
+                        "is impossible. The implied number of partitions that can be "
+                        "used is lower than the number of requested partitions "
+                        "resulting in empty partitions. Please decrease the size of "
+                        "shards: `shard_size`."
+                    )
+            else:
+                raise ValueError(
+                    "The keep_incomplete_shards need to be specified "
+                    "when _num_shards_per_node is None."
+                )
+            num_shards_per_node = int(self._num_shards_used / self._num_partitions)
+            # Assign the shards per nodes (so far, the same as in ideal case)
+            num_shards_per_node_array = (
+                np.ones(self._num_partitions) * num_shards_per_node
+            )
+            num_shards_assigned = self._num_partitions * num_shards_per_node
+            num_shards_to_assign = self._num_shards_used - num_shards_assigned
+            # Assign the "missing" shards
+            for i in range(num_shards_to_assign):
+                num_shards_per_node_array[i] += 1
+
+        else:
+            raise ValueError(
+                "The specification of nm_shards_per_node and "
+                "keep_incomplete_shards is not correct."
+            )
+
+        if num_usable_shards_in_dataset < self._num_partitions:
+            raise ValueError(
+                "The specified configuration results in empty partitions because the "
+                "number of usable shards is smaller that the number partitions. "
+                "Try decreasing the shard size or the number of partitions. "
+            )
+
+        indices_on_which_to_split_shards = np.cumsum(
+            num_shards_per_node_array, dtype=int
+        )
+
+        shard_indices_array = self._rng.permutation(num_usable_shards_in_dataset)[
+            : self._num_shards_used
+        ]
+        # Randomly assign shards to node_id
+        nid_to_shard_indices = np.split(
+            shard_indices_array, indices_on_which_to_split_shards
+        )[:-1]
+        node_id_to_indices: Dict[int, List[int]] = {
+            cid: [] for cid in range(self._num_partitions)
+        }
+        # Compute node_id to sample indices based on the shard indices
+        for node_id in range(self._num_partitions):
+            for shard_idx in nid_to_shard_indices[node_id]:
+                start_id = int(shard_idx * self._shard_size)
+                end_id = min(int((shard_idx + 1) * self._shard_size), len(self.dataset))
+                node_id_to_indices[node_id].extend(list(range(start_id, end_id)))
+        if self._shuffle:
+            for indices in node_id_to_indices.values():
+                # In place shuffling
+                self._rng.shuffle(indices)
+        self._node_id_to_indices = node_id_to_indices
+        self._node_id_to_indices_determined = True
+
+    def _check_num_partitions_correctness_if_needed(self) -> None:
+        """Test num_partitions when the dataset is given (in load_partition)."""
+        if not self._node_id_to_indices_determined:
+            if self._num_partitions > self.dataset.num_rows:
+                raise ValueError(
+                    "The number of partitions needs to be smaller than the number of "
+                    "samples in the dataset."
+                )
+
+    def _sort_dataset_if_needed(self) -> None:
+        """Sort dataset prior to determining the partitions.
+
+        Operation only needed to be performed one time. It's required for the creation
+        of shards with the same labels.
+        """
+        if self._node_id_to_indices_determined:
+            return
+        self._dataset = self.dataset.sort(self._partition_by)
+
+    def _compute_shard_size_if_missing(self) -> None:
+        """Compute the parameters needed to perform sharding.
+
+        This method should be called after the dataset is assigned.
+        """
+        if self._shard_size is None:
+            # If shard size is not specified it needs to be computed
+            num_rows = self.dataset.num_rows
+            self._shard_size = int(num_rows / self._num_shards_used)
+
+    def _check_possibility_of_partitions_creation(self) -> None:
+        if self._shard_size is not None and self._num_shards_per_node is not None:
+            implied_min_dataset_size = (
+                self._shard_size * self._num_shards_per_node * self._num_partitions
+            )
+            if implied_min_dataset_size > len(self.dataset):
+                raise ValueError(
+                    f"Based on the given arguments the creation of the "
+                    "partitions is impossible. The implied minimum dataset"
+                    f"size is {implied_min_dataset_size} but the dataset"
+                    f"size is {len(self.dataset)}"
+                )
+
+
+def _check_if_natual_number(
+    number: Optional[int], parameter_name: str, none_acceptable: bool = False
+) -> None:
+    if none_acceptable and number is None:
+        return
+    if not isinstance(number, int):
+        raise TypeError(
+            f"The expected type of {parameter_name} is int but given: {number} of type "
+            f"{type(number)}. Please specify the correct type."
+        )
+    if not number >= 1:
+        raise ValueError(
+            f"The expected value of {parameter_name} is >= 1 (greater or equal to 1) "
+            f"but given: {number} which does not meet this condition. Please "
+            f"provide a correct number."
+        )
diff --git a/datasets/flwr_datasets/partitioner/shard_partitioner_test.py b/datasets/flwr_datasets/partitioner/shard_partitioner_test.py
new file mode 100644
index 000000000000..47968699bba7
--- /dev/null
+++ b/datasets/flwr_datasets/partitioner/shard_partitioner_test.py
@@ -0,0 +1,392 @@
+# Copyright 2023 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test ShardPartitioner."""
+
+
+# pylint: disable=W0212, R0913
+import unittest
+from typing import Optional, Tuple
+
+from datasets import Dataset
+from flwr_datasets.partitioner.shard_partitioner import ShardPartitioner
+
+
+def _dummy_setup(
+    num_rows: int,
+    partition_by: str,
+    num_partitions: int,
+    num_shards_per_node: Optional[int],
+    shard_size: Optional[int],
+    keep_incomplete_shard: bool = False,
+) -> Tuple[Dataset, ShardPartitioner]:
+    """Create a dummy dataset for testing."""
+    data = {
+        partition_by: [i % 3 for i in range(num_rows)],
+        "features": list(range(num_rows)),
+    }
+    dataset = Dataset.from_dict(data)
+    partitioner = ShardPartitioner(
+        num_partitions=num_partitions,
+        num_shards_per_node=num_shards_per_node,
+        partition_by=partition_by,
+        shard_size=shard_size,
+        keep_incomplete_shard=keep_incomplete_shard,
+    )
+    partitioner.dataset = dataset
+    return dataset, partitioner
+
+
+class TestShardPartitionerSpec1(unittest.TestCase):
+    """Test first possible initialization of ShardPartitioner.
+
+    Specify num_shards_per_node and shard_size arguments.
+    """
+
+    def test_correct_num_partitions(self) -> None:
+        """Test the correct number of partitions is created."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_node = 3
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_node,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        _ = partitioner.load_partition(0)
+        num_partitions_created = len(partitioner._node_id_to_indices.keys())
+        self.assertEqual(num_partitions_created, num_partitions)
+
+    def test_correct_partition_sizes(self) -> None:
+        """Test if the partitions sizes are as theoretically calculated."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_node = 3
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_node,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        sizes = [len(partitioner.load_partition(i)) for i in range(num_partitions)]
+        sizes = sorted(sizes)
+        self.assertEqual(sizes, [30, 30, 30])
+
+    def test_unique_samples(self) -> None:
+        """Test if each partition has unique samples.
+
+        (No duplicates along partitions).
+        """
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_node = 3
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_node,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        partitions = [
+            partitioner.load_partition(i)["features"] for i in range(num_partitions)
+        ]
+        combined_list = [item for sublist in partitions for item in sublist]
+        combined_set = set(combined_list)
+        self.assertEqual(len(combined_list), len(combined_set))
+
+
+class TestShardPartitionerSpec2(unittest.TestCase):
+    """Test second possible initialization of ShardPartitioner.
+
+    Specify shard_size and keep_incomplete_shard=False. This setting creates partitions
+    that might have various sizes (each shard is same size).
+    """
+
+    def test_correct_num_partitions(self) -> None:
+        """Test the correct number of partitions is created."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_node = None
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_node,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        _ = partitioner.load_partition(0)
+        num_partitions_created = len(partitioner._node_id_to_indices.keys())
+        self.assertEqual(num_partitions_created, num_partitions)
+
+    def test_correct_partition_sizes(self) -> None:
+        """Test if the partitions sizes are as theoretically calculated."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_node = None
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_node,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        sizes = [len(partitioner.load_partition(i)) for i in range(num_partitions)]
+        sizes = sorted(sizes)
+        self.assertEqual(sizes, [30, 40, 40])
+
+    def test_unique_samples(self) -> None:
+        """Test if each partition has unique samples.
+
+        (No duplicates along partitions).
+        """
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_node = None
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_node,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        partitions = [
+            partitioner.load_partition(i)["features"] for i in range(num_partitions)
+        ]
+        combined_list = [item for sublist in partitions for item in sublist]
+        combined_set = set(combined_list)
+        self.assertEqual(len(combined_list), len(combined_set))
+
+
+class TestShardPartitionerSpec3(unittest.TestCase):
+    """Test third possible initialization of ShardPartitioner.
+
+    Specify shard_size and keep_incomplete_shard=True. This setting creates partitions
+    that might have various sizes (each shard is same size).
+    """
+
+    def test_correct_num_partitions(self) -> None:
+        """Test the correct number of partitions is created."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_node = None
+        shard_size = 10
+        keep_incomplete_shard = True
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_node,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        _ = partitioner.load_partition(0)
+        num_partitions_created = len(partitioner._node_id_to_indices.keys())
+        self.assertEqual(num_partitions_created, num_partitions)
+
+    def test_correct_partition_sizes(self) -> None:
+        """Test if the partitions sizes are as theoretically calculated."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_node = None
+        shard_size = 10
+        keep_incomplete_shard = True
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_node,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        sizes = [len(partitioner.load_partition(i)) for i in range(num_partitions)]
+        sizes = sorted(sizes)
+        self.assertEqual(sizes, [33, 40, 40])
+
+    def test_unique_samples(self) -> None:
+        """Test if each partition has unique samples.
+
+        (No duplicates along partitions).
+        """
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_node = None
+        shard_size = 10
+        keep_incomplete_shard = True
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_node,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        partitions = [
+            partitioner.load_partition(i)["features"] for i in range(num_partitions)
+        ]
+        combined_list = [item for sublist in partitions for item in sublist]
+        combined_set = set(combined_list)
+        self.assertEqual(len(combined_list), len(combined_set))
+
+
+class TestShardPartitionerSpec4(unittest.TestCase):
+    """Test fourth possible initialization of ShardPartitioner.
+
+    Specify num_shards_per_node but not shard_size arguments.
+    """
+
+    def test_correct_num_partitions(self) -> None:
+        """Test the correct number of partitions is created."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_node = 3
+        shard_size = None
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_node,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        _ = partitioner.load_partition(0)
+        num_partitions_created = len(partitioner._node_id_to_indices.keys())
+        self.assertEqual(num_partitions_created, num_partitions)
+
+    def test_correct_partition_sizes(self) -> None:
+        """Test if the partitions sizes are as theoretically calculated."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_node = 3
+        shard_size = None
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_node,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        sizes = [len(partitioner.load_partition(i)) for i in range(num_partitions)]
+        sizes = sorted(sizes)
+        self.assertEqual(sizes, [36, 36, 36])
+
+    def test_unique_samples(self) -> None:
+        """Test if each partition has unique samples.
+
+        (No duplicates along partitions).
+        """
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_node = 3
+        shard_size = None
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_node,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        partitions = [
+            partitioner.load_partition(i)["features"] for i in range(num_partitions)
+        ]
+        combined_list = [item for sublist in partitions for item in sublist]
+        combined_set = set(combined_list)
+        self.assertEqual(len(combined_list), len(combined_set))
+
+
+class TestShardPartitionerIncorrectSpec(unittest.TestCase):
+    """Test the incorrect specification cases.
+
+    The lack of correctness can be caused by the num_partitions, shard_size and
+    num_shards_per_partition can create.
+    """
+
+    def test_incorrect_specification(self) -> None:
+        """Test if the given specification makes the partitioning possible."""
+        partition_by = "label"
+        num_rows = 10
+        num_partitions = 3
+        num_shards_per_node = 2
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_node,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        with self.assertRaises(ValueError):
+            _ = partitioner.load_partition(0)
+
+    def test_too_big_shard_size(self) -> None:
+        """Test if it is impossible to create an empty partition."""
+        partition_by = "label"
+        num_rows = 20
+        num_partitions = 3
+        num_shards_per_node = None
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_node,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        with self.assertRaises(ValueError):
+            _ = partitioner.load_partition(2).num_rows
+
+
+if __name__ == "__main__":
+    unittest.main()

From 5a3679a93137bab6f120c06a44983ae2f5a92ed3 Mon Sep 17 00:00:00 2001
From: mohammadnaseri <mohammad@flower.dev>
Date: Tue, 27 Feb 2024 13:08:45 +0000
Subject: [PATCH 065/102] Introduce central DP (client-side fixed clipping) 
 (#2893)

---
 src/py/flwr/client/mod/__init__.py            |   2 +
 src/py/flwr/client/mod/centraldp_mods.py      |  76 +++++++++
 .../common/differential_privacy_constants.py  |   2 +
 src/py/flwr/server/strategy/__init__.py       |   6 +-
 .../flwr/server/strategy/dp_fixed_clipping.py | 158 +++++++++++++++++-
 5 files changed, 241 insertions(+), 3 deletions(-)
 create mode 100644 src/py/flwr/client/mod/centraldp_mods.py

diff --git a/src/py/flwr/client/mod/__init__.py b/src/py/flwr/client/mod/__init__.py
index a181865614df..8ed6f52ef788 100644
--- a/src/py/flwr/client/mod/__init__.py
+++ b/src/py/flwr/client/mod/__init__.py
@@ -15,10 +15,12 @@
 """Mods."""
 
 
+from .centraldp_mods import fixedclipping_mod
 from .secure_aggregation.secaggplus_mod import secaggplus_mod
 from .utils import make_ffn
 
 __all__ = [
     "make_ffn",
     "secaggplus_mod",
+    "fixedclipping_mod",
 ]
diff --git a/src/py/flwr/client/mod/centraldp_mods.py b/src/py/flwr/client/mod/centraldp_mods.py
new file mode 100644
index 000000000000..76bfe1b06e55
--- /dev/null
+++ b/src/py/flwr/client/mod/centraldp_mods.py
@@ -0,0 +1,76 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Clipping modifiers for central DP with client-side clipping."""
+
+
+from flwr.client.typing import ClientAppCallable
+from flwr.common import ndarrays_to_parameters, parameters_to_ndarrays
+from flwr.common import recordset_compat as compat
+from flwr.common.constant import MESSAGE_TYPE_FIT
+from flwr.common.context import Context
+from flwr.common.differential_privacy import compute_clip_model_update
+from flwr.common.differential_privacy_constants import KEY_CLIPPING_NORM
+from flwr.common.message import Message
+
+
+def fixedclipping_mod(
+    msg: Message, ctxt: Context, call_next: ClientAppCallable
+) -> Message:
+    """Client-side fixed clipping modifier.
+
+    This mod needs to be used with the DifferentialPrivacyClientSideFixedClipping
+    server-side strategy wrapper.
+
+    The wrapper sends the clipping_norm value to the client.
+
+    This mod clips the client model updates before sending them to the server.
+
+    It operates on messages with type MESSAGE_TYPE_FIT.
+
+    Notes
+    -----
+    Consider the order of mods when using multiple.
+
+    Typically, fixedclipping_mod should be the last to operate on params.
+    """
+    if msg.metadata.message_type != MESSAGE_TYPE_FIT:
+        return call_next(msg, ctxt)
+    fit_ins = compat.recordset_to_fitins(msg.content, keep_input=True)
+    if KEY_CLIPPING_NORM not in fit_ins.config:
+        raise KeyError(
+            f"The {KEY_CLIPPING_NORM} value is not supplied by the "
+            f"DifferentialPrivacyClientSideFixedClipping wrapper at"
+            f" the server side."
+        )
+
+    clipping_norm = float(fit_ins.config[KEY_CLIPPING_NORM])
+    server_to_client_params = parameters_to_ndarrays(fit_ins.parameters)
+
+    # Call inner app
+    out_msg = call_next(msg, ctxt)
+    fit_res = compat.recordset_to_fitres(out_msg.content, keep_input=True)
+
+    client_to_server_params = parameters_to_ndarrays(fit_res.parameters)
+
+    # Clip the client update
+    compute_clip_model_update(
+        client_to_server_params,
+        server_to_client_params,
+        clipping_norm,
+    )
+
+    fit_res.parameters = ndarrays_to_parameters(client_to_server_params)
+    out_msg.content = compat.fitres_to_recordset(fit_res, keep_input=True)
+    return out_msg
diff --git a/src/py/flwr/common/differential_privacy_constants.py b/src/py/flwr/common/differential_privacy_constants.py
index 9ec080975aa3..61ba3ba9a958 100644
--- a/src/py/flwr/common/differential_privacy_constants.py
+++ b/src/py/flwr/common/differential_privacy_constants.py
@@ -14,6 +14,8 @@
 # ==============================================================================
 """Constants for differential privacy."""
 
+
+KEY_CLIPPING_NORM = "clipping_norm"
 CLIENTS_DISCREPANCY_WARNING = (
     "The number of clients returning parameters (%s)"
     " differs from the number of sampled clients (%s)."
diff --git a/src/py/flwr/server/strategy/__init__.py b/src/py/flwr/server/strategy/__init__.py
index a31f5d48b77c..6b058c3df05b 100644
--- a/src/py/flwr/server/strategy/__init__.py
+++ b/src/py/flwr/server/strategy/__init__.py
@@ -16,7 +16,10 @@
 
 
 from .bulyan import Bulyan as Bulyan
-from .dp_fixed_clipping import DifferentialPrivacyServerSideFixedClipping
+from .dp_fixed_clipping import (
+    DifferentialPrivacyClientSideFixedClipping,
+    DifferentialPrivacyServerSideFixedClipping,
+)
 from .dpfedavg_adaptive import DPFedAvgAdaptive as DPFedAvgAdaptive
 from .dpfedavg_fixed import DPFedAvgFixed as DPFedAvgFixed
 from .fault_tolerant_fedavg import FaultTolerantFedAvg as FaultTolerantFedAvg
@@ -59,4 +62,5 @@
     "DPFedAvgFixed",
     "Strategy",
     "DifferentialPrivacyServerSideFixedClipping",
+    "DifferentialPrivacyClientSideFixedClipping",
 ]
diff --git a/src/py/flwr/server/strategy/dp_fixed_clipping.py b/src/py/flwr/server/strategy/dp_fixed_clipping.py
index d18a6b2079d9..fd1809125d3e 100644
--- a/src/py/flwr/server/strategy/dp_fixed_clipping.py
+++ b/src/py/flwr/server/strategy/dp_fixed_clipping.py
@@ -36,7 +36,10 @@
     add_gaussian_noise_to_params,
     compute_clip_model_update,
 )
-from flwr.common.differential_privacy_constants import CLIENTS_DISCREPANCY_WARNING
+from flwr.common.differential_privacy_constants import (
+    CLIENTS_DISCREPANCY_WARNING,
+    KEY_CLIPPING_NORM,
+)
 from flwr.common.logger import log
 from flwr.server.client_manager import ClientManager
 from flwr.server.client_proxy import ClientProxy
@@ -44,7 +47,8 @@
 
 
 class DifferentialPrivacyServerSideFixedClipping(Strategy):
-    """Wrapper for Central DP with Server Side Fixed Clipping.
+    """Strategy wrapper for central differential privacy with server-side fixed
+    clipping.
 
     Parameters
     ----------
@@ -185,3 +189,153 @@ def evaluate(
     ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
         """Evaluate model parameters using an evaluation function from the strategy."""
         return self.strategy.evaluate(server_round, parameters)
+
+
+class DifferentialPrivacyClientSideFixedClipping(Strategy):
+    """Strategy wrapper for central differential privacy with client-side fixed
+    clipping.
+
+    Use `fixedclipping_mod` modifier at the client side.
+
+    In comparison to `DifferentialPrivacyServerSideFixedClipping`,
+    which performs clipping on the server-side, `DifferentialPrivacyClientSideFixedClipping`
+    expects clipping to happen on the client-side, usually by using the built-in
+    `fixedclipping_mod `.
+
+    Parameters
+    ----------
+    strategy : Strategy
+        The strategy to which DP functionalities will be added by this wrapper.
+    noise_multiplier : float
+        The noise multiplier for the Gaussian mechanism for model updates.
+        A value of 1.0 or higher is recommended for strong privacy.
+    clipping_norm : float
+        The value of the clipping norm.
+    num_sampled_clients : int
+        The number of clients that are sampled on each round.
+
+    Examples
+    --------
+    Create a strategy:
+
+    >>> strategy = fl.server.strategy.FedAvg(...)
+
+    Wrap the strategy with the `DifferentialPrivacyServerSideFixedClipping` wrapper:
+
+    >>> DifferentialPrivacyClientSideFixedClipping(
+    >>>     strategy, cfg.noise_multiplier, cfg.clipping_norm, cfg.num_sampled_clients
+    >>> )
+
+    On the client, add the `fixedclipping_mod` to the client-side mods:
+
+    >>> app = fl.client.ClientApp(
+    >>>     client_fn=FlowerClient().to_client(), mods=[fixedclipping_mod]
+    >>> )
+    """
+
+    # pylint: disable=too-many-arguments,too-many-instance-attributes
+    def __init__(
+        self,
+        strategy: Strategy,
+        noise_multiplier: float,
+        clipping_norm: float,
+        num_sampled_clients: int,
+    ) -> None:
+        super().__init__()
+
+        self.strategy = strategy
+
+        if noise_multiplier < 0:
+            raise ValueError("The noise multiplier should be a non-negative value.")
+
+        if clipping_norm <= 0:
+            raise ValueError("The clipping threshold should be a positive value.")
+
+        if num_sampled_clients <= 0:
+            raise ValueError(
+                "The number of sampled clients should be a positive value."
+            )
+
+        self.noise_multiplier = noise_multiplier
+        self.clipping_norm = clipping_norm
+        self.num_sampled_clients = num_sampled_clients
+
+    def __repr__(self) -> str:
+        """Compute a string representation of the strategy."""
+        rep = "Differential Privacy Strategy Wrapper (Client-Side Fixed Clipping)"
+        return rep
+
+    def initialize_parameters(
+        self, client_manager: ClientManager
+    ) -> Optional[Parameters]:
+        """Initialize global model parameters using given strategy."""
+        return self.strategy.initialize_parameters(client_manager)
+
+    def configure_fit(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, FitIns]]:
+        """Configure the next round of training."""
+        additional_config = {KEY_CLIPPING_NORM: self.clipping_norm}
+        inner_strategy_config_result = self.strategy.configure_fit(
+            server_round, parameters, client_manager
+        )
+        for _, fit_ins in inner_strategy_config_result:
+            fit_ins.config.update(additional_config)
+
+        return inner_strategy_config_result
+
+    def configure_evaluate(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, EvaluateIns]]:
+        """Configure the next round of evaluation."""
+        return self.strategy.configure_evaluate(
+            server_round, parameters, client_manager
+        )
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Add noise to the aggregated parameters."""
+        if failures:
+            return None, {}
+
+        if len(results) != self.num_sampled_clients:
+            log(
+                WARNING,
+                CLIENTS_DISCREPANCY_WARNING,
+                len(results),
+                self.num_sampled_clients,
+            )
+
+        # Pass the new parameters for aggregation
+        aggregated_params, metrics = self.strategy.aggregate_fit(
+            server_round, results, failures
+        )
+
+        # Add Gaussian noise to the aggregated parameters
+        if aggregated_params:
+            aggregated_params = add_gaussian_noise_to_params(
+                aggregated_params,
+                self.noise_multiplier,
+                self.clipping_norm,
+                self.num_sampled_clients,
+            )
+        return aggregated_params, metrics
+
+    def aggregate_evaluate(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, EvaluateRes]],
+        failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+    ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+        """Aggregate evaluation losses using the given strategy."""
+        return self.strategy.aggregate_evaluate(server_round, results, failures)
+
+    def evaluate(
+        self, server_round: int, parameters: Parameters
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """Evaluate model parameters using an evaluation function from the strategy."""
+        return self.strategy.evaluate(server_round, parameters)

From 19d0b7097ffd566d0d7ea818e0e4e7faba130e57 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Tue, 27 Feb 2024 16:16:45 +0100
Subject: [PATCH 066/102] fix (#3022)

Co-authored-by: Taner Topal <taner@flower.ai>
---
 src/py/flwr/cli/new/new_test.py | 34 +++++++++++++++++++--------------
 1 file changed, 20 insertions(+), 14 deletions(-)

diff --git a/src/py/flwr/cli/new/new_test.py b/src/py/flwr/cli/new/new_test.py
index 39717bc67ab3..7a4832013b0c 100644
--- a/src/py/flwr/cli/new/new_test.py
+++ b/src/py/flwr/cli/new/new_test.py
@@ -77,17 +77,23 @@ def test_new(tmp_path: str) -> None:
         "client.py",
     }
 
-    # Change into the temprorary directory
-    os.chdir(tmp_path)
-
-    # Execute
-    new(project_name=project_name, framework=framework)
-
-    # Assert
-    file_list = os.listdir(os.path.join(tmp_path, project_name.lower()))
-    assert set(file_list) == expected_files_top_level
-
-    file_list = os.listdir(
-        os.path.join(tmp_path, project_name.lower(), project_name.lower())
-    )
-    assert set(file_list) == expected_files_module
+    # Current directory
+    origin = os.getcwd()
+
+    try:
+        # Change into the temprorary directory
+        os.chdir(tmp_path)
+
+        # Execute
+        new(project_name=project_name, framework=framework)
+
+        # Assert
+        file_list = os.listdir(os.path.join(tmp_path, project_name.lower()))
+        assert set(file_list) == expected_files_top_level
+
+        file_list = os.listdir(
+            os.path.join(tmp_path, project_name.lower(), project_name.lower())
+        )
+        assert set(file_list) == expected_files_module
+    finally:
+        os.chdir(origin)

From 06a7c57b0b90df0ab49565a5dbc695f654a59614 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Tue, 27 Feb 2024 16:28:55 +0100
Subject: [PATCH 067/102] Add new common function for registering exit handlers
 (#3021)

---
 src/py/flwr/client/app.py           |  3 +-
 src/py/flwr/common/exit_handlers.py | 87 +++++++++++++++++++++++++++++
 src/py/flwr/server/app.py           | 61 +++-----------------
 3 files changed, 96 insertions(+), 55 deletions(-)
 create mode 100644 src/py/flwr/common/exit_handlers.py

diff --git a/src/py/flwr/client/app.py b/src/py/flwr/client/app.py
index 6c3c3a99680d..2d49b3a44f08 100644
--- a/src/py/flwr/client/app.py
+++ b/src/py/flwr/client/app.py
@@ -34,6 +34,7 @@
     TRANSPORT_TYPE_REST,
     TRANSPORT_TYPES,
 )
+from flwr.common.exit_handlers import register_exit_handlers
 from flwr.common.logger import log, warn_deprecated_feature, warn_experimental_feature
 
 from .clientapp import load_client_app
@@ -104,7 +105,7 @@ def _load() -> ClientApp:
         root_certificates=root_certificates,
         insecure=args.insecure,
     )
-    event(EventType.RUN_CLIENT_APP_LEAVE)
+    register_exit_handlers(event_type=EventType.RUN_CLIENT_APP_LEAVE)
 
 
 def _parse_args_run_client_app() -> argparse.ArgumentParser:
diff --git a/src/py/flwr/common/exit_handlers.py b/src/py/flwr/common/exit_handlers.py
new file mode 100644
index 000000000000..30750c28a450
--- /dev/null
+++ b/src/py/flwr/common/exit_handlers.py
@@ -0,0 +1,87 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Common function to register exit handlers for server and client."""
+
+
+import sys
+from signal import SIGINT, SIGTERM, signal
+from threading import Thread
+from types import FrameType
+from typing import List, Optional
+
+from grpc import Server
+
+from flwr.common.telemetry import EventType, event
+
+
+def register_exit_handlers(
+    event_type: EventType,
+    grpc_servers: Optional[List[Server]] = None,
+    bckg_threads: Optional[List[Thread]] = None,
+) -> None:
+    """Register exit handlers for `SIGINT` and `SIGTERM` signals.
+
+    Parameters
+    ----------
+    event_type : EventType
+        The telemetry event that should be logged before exit.
+    grpc_servers: Optional[List[Server]] (default: None)
+        An otpional list of gRPC servers that need to be gracefully
+        terminated before exiting.
+    bckg_threads: Optional[List[Thread]] (default: None)
+        An optional list of threads that need to be gracefully
+        terminated before exiting.
+    """
+    default_handlers = {
+        SIGINT: None,
+        SIGTERM: None,
+    }
+
+    def graceful_exit_handler(  # type: ignore
+        signalnum,
+        frame: FrameType,  # pylint: disable=unused-argument
+    ) -> None:
+        """Exit handler to be registered with `signal.signal`.
+
+        When called will reset signal handler to original signal handler from
+        default_handlers.
+        """
+        # Reset to default handler
+        signal(signalnum, default_handlers[signalnum])
+
+        event_res = event(event_type=event_type)
+
+        if grpc_servers is not None:
+            for grpc_server in grpc_servers:
+                grpc_server.stop(grace=1)
+
+        if bckg_threads is not None:
+            for bckg_thread in bckg_threads:
+                bckg_thread.join()
+
+        # Ensure event has happend
+        event_res.result()
+
+        # Setup things for graceful exit
+        sys.exit(0)
+
+    default_handlers[SIGINT] = signal(  # type: ignore
+        SIGINT,
+        graceful_exit_handler,  # type: ignore
+    )
+    default_handlers[SIGTERM] = signal(  # type: ignore
+        SIGTERM,
+        graceful_exit_handler,  # type: ignore
+    )
diff --git a/src/py/flwr/server/app.py b/src/py/flwr/server/app.py
index ac7a8339b31d..a4913d51315b 100644
--- a/src/py/flwr/server/app.py
+++ b/src/py/flwr/server/app.py
@@ -22,8 +22,6 @@
 from logging import ERROR, INFO, WARN
 from os.path import isfile
 from pathlib import Path
-from signal import SIGINT, SIGTERM, signal
-from types import FrameType
 from typing import List, Optional, Tuple
 
 import grpc
@@ -36,6 +34,7 @@
     TRANSPORT_TYPE_REST,
     TRANSPORT_TYPE_VCE,
 )
+from flwr.common.exit_handlers import register_exit_handlers
 from flwr.common.logger import log
 from flwr.proto.driver_pb2_grpc import (  # pylint: disable=E0611
     add_DriverServicer_to_server,
@@ -212,10 +211,10 @@ def run_driver_api() -> None:
     )
 
     # Graceful shutdown
-    _register_exit_handlers(
+    register_exit_handlers(
+        event_type=EventType.RUN_DRIVER_API_LEAVE,
         grpc_servers=[grpc_server],
         bckg_threads=[],
-        event_type=EventType.RUN_DRIVER_API_LEAVE,
     )
 
     # Block
@@ -280,10 +279,10 @@ def run_fleet_api() -> None:
         raise ValueError(f"Unknown fleet_api_type: {args.fleet_api_type}")
 
     # Graceful shutdown
-    _register_exit_handlers(
+    register_exit_handlers(
+        event_type=EventType.RUN_FLEET_API_LEAVE,
         grpc_servers=grpc_servers,
         bckg_threads=bckg_threads,
-        event_type=EventType.RUN_FLEET_API_LEAVE,
     )
 
     # Block
@@ -375,10 +374,10 @@ def run_superlink() -> None:
         raise ValueError(f"Unknown fleet_api_type: {args.fleet_api_type}")
 
     # Graceful shutdown
-    _register_exit_handlers(
+    register_exit_handlers(
+        event_type=EventType.RUN_SUPERLINK_LEAVE,
         grpc_servers=grpc_servers,
         bckg_threads=bckg_threads,
-        event_type=EventType.RUN_SUPERLINK_LEAVE,
     )
 
     # Block
@@ -413,52 +412,6 @@ def _try_obtain_certificates(
     return certificates
 
 
-def _register_exit_handlers(
-    grpc_servers: List[grpc.Server],
-    bckg_threads: List[threading.Thread],
-    event_type: EventType,
-) -> None:
-    default_handlers = {
-        SIGINT: None,
-        SIGTERM: None,
-    }
-
-    def graceful_exit_handler(  # type: ignore
-        signalnum,
-        frame: FrameType,  # pylint: disable=unused-argument
-    ) -> None:
-        """Exit handler to be registered with signal.signal.
-
-        When called will reset signal handler to original signal handler from
-        default_handlers.
-        """
-        # Reset to default handler
-        signal(signalnum, default_handlers[signalnum])
-
-        event_res = event(event_type=event_type)
-
-        for grpc_server in grpc_servers:
-            grpc_server.stop(grace=1)
-
-        for bckg_thread in bckg_threads:
-            bckg_thread.join()
-
-        # Ensure event has happend
-        event_res.result()
-
-        # Setup things for graceful exit
-        sys.exit(0)
-
-    default_handlers[SIGINT] = signal(  # type: ignore
-        SIGINT,
-        graceful_exit_handler,  # type: ignore
-    )
-    default_handlers[SIGTERM] = signal(  # type: ignore
-        SIGTERM,
-        graceful_exit_handler,  # type: ignore
-    )
-
-
 def _run_driver_api_grpc(
     address: str,
     state_factory: StateFactory,

From 81fadda74398fd7f9999023df32239c8a5ccfc36 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Tue, 27 Feb 2024 20:28:18 +0100
Subject: [PATCH 068/102] Rename `clientapp` to `client_app` (#3020)

---
 doc/locales/fr/LC_MESSAGES/framework-docs.po       | 14 +++++++-------
 doc/locales/pt_BR/LC_MESSAGES/framework-docs.po    | 12 ++++++------
 doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po  | 12 ++++++------
 src/py/flwr/client/__init__.py                     |  2 +-
 src/py/flwr/client/app.py                          |  4 ++--
 src/py/flwr/client/{clientapp.py => client_app.py} |  0
 .../server/superlink/fleet/vce/backend/backend.py  |  2 +-
 .../superlink/fleet/vce/backend/raybackend.py      |  2 +-
 .../superlink/fleet/vce/backend/raybackend_test.py |  2 +-
 src/py/flwr/server/superlink/fleet/vce/vce_api.py  |  2 +-
 src/py/flwr/simulation/ray_transport/ray_actor.py  |  2 +-
 .../simulation/ray_transport/ray_client_proxy.py   |  2 +-
 .../ray_transport/ray_client_proxy_test.py         |  2 +-
 13 files changed, 29 insertions(+), 29 deletions(-)
 rename src/py/flwr/client/{clientapp.py => client_app.py} (100%)

diff --git a/doc/locales/fr/LC_MESSAGES/framework-docs.po b/doc/locales/fr/LC_MESSAGES/framework-docs.po
index ba5ea5ec070d..cace04c6980f 100644
--- a/doc/locales/fr/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/fr/LC_MESSAGES/framework-docs.po
@@ -8316,10 +8316,10 @@ msgid ""
 msgstr ""
 
 #: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
-#: flwr.client.clientapp.ClientApp:1 of
+#: flwr.client.client_app.ClientApp:1 of
 #, fuzzy
 msgid "Flower ClientApp."
-msgstr "Client de Flower"
+msgstr "Flower ClientApp."
 
 #: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
 msgid ":py:obj:`NumPyClient <flwr.client.NumPyClient>`\\ \\(\\)"
@@ -8623,7 +8623,7 @@ msgstr ""
 msgid "ClientApp"
 msgstr "client"
 
-#: flwr.client.clientapp.ClientApp:1 flwr.common.typing.ClientMessage:1
+#: flwr.client.client_app.ClientApp:1 flwr.common.typing.ClientMessage:1
 #: flwr.common.typing.DisconnectRes:1 flwr.common.typing.EvaluateIns:1
 #: flwr.common.typing.EvaluateRes:1 flwr.common.typing.FitIns:1
 #: flwr.common.typing.FitRes:1 flwr.common.typing.GetParametersIns:1
@@ -8638,25 +8638,25 @@ msgid "Bases: :py:class:`object`"
 msgstr ""
 
 #: flwr.client.app.start_client:33 flwr.client.app.start_numpy_client:36
-#: flwr.client.clientapp.ClientApp:4 flwr.server.app.start_server:41
+#: flwr.client.client_app.ClientApp:4 flwr.server.app.start_server:41
 #: flwr.server.driver.app.start_driver:30 of
 #, fuzzy
 msgid "Examples"
 msgstr "Exemples de PyTorch"
 
-#: flwr.client.clientapp.ClientApp:5 of
+#: flwr.client.client_app.ClientApp:5 of
 msgid ""
 "Assuming a typical `Client` implementation named `FlowerClient`, you can "
 "wrap it in a `ClientApp` as follows:"
 msgstr ""
 
-#: flwr.client.clientapp.ClientApp:16 of
+#: flwr.client.client_app.ClientApp:16 of
 msgid ""
 "If the above code is in a Python module called `client`, it can be "
 "started as follows:"
 msgstr ""
 
-#: flwr.client.clientapp.ClientApp:21 of
+#: flwr.client.client_app.ClientApp:21 of
 msgid ""
 "In this `client:app` example, `client` refers to the Python module "
 "`client.py` in which the previous code lives in and `app` refers to the "
diff --git a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
index 359458e8db57..df354f7a76cc 100644
--- a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
@@ -6480,7 +6480,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
-#: flwr.client.clientapp.ClientApp:1 of
+#: flwr.client.client_app.ClientApp:1 of
 msgid "Flower ClientApp."
 msgstr ""
 
@@ -6779,7 +6779,7 @@ msgstr ""
 msgid "ClientApp"
 msgstr ""
 
-#: flwr.client.clientapp.ClientApp:1 flwr.common.typing.ClientMessage:1
+#: flwr.client.client_app.ClientApp:1 flwr.common.typing.ClientMessage:1
 #: flwr.common.typing.DisconnectRes:1 flwr.common.typing.EvaluateIns:1
 #: flwr.common.typing.EvaluateRes:1 flwr.common.typing.FitIns:1
 #: flwr.common.typing.FitRes:1 flwr.common.typing.GetParametersIns:1
@@ -6794,24 +6794,24 @@ msgid "Bases: :py:class:`object`"
 msgstr ""
 
 #: flwr.client.app.start_client:33 flwr.client.app.start_numpy_client:36
-#: flwr.client.clientapp.ClientApp:4 flwr.server.app.start_server:41
+#: flwr.client.client_app.ClientApp:4 flwr.server.app.start_server:41
 #: flwr.server.driver.app.start_driver:30 of
 msgid "Examples"
 msgstr ""
 
-#: flwr.client.clientapp.ClientApp:5 of
+#: flwr.client.client_app.ClientApp:5 of
 msgid ""
 "Assuming a typical `Client` implementation named `FlowerClient`, you can "
 "wrap it in a `ClientApp` as follows:"
 msgstr ""
 
-#: flwr.client.clientapp.ClientApp:16 of
+#: flwr.client.client_app.ClientApp:16 of
 msgid ""
 "If the above code is in a Python module called `client`, it can be "
 "started as follows:"
 msgstr ""
 
-#: flwr.client.clientapp.ClientApp:21 of
+#: flwr.client.client_app.ClientApp:21 of
 msgid ""
 "In this `client:app` example, `client` refers to the Python module "
 "`client.py` in which the previous code lives in and `app` refers to the "
diff --git a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
index b6c32f994597..a4255e51f4b6 100644
--- a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
@@ -7209,7 +7209,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
-#: flwr.client.clientapp.ClientApp:1 of
+#: flwr.client.client_app.ClientApp:1 of
 #, fuzzy
 msgid "Flower ClientApp."
 msgstr "Flower 客户端。"
@@ -7511,7 +7511,7 @@ msgstr "当前客户端属性。"
 msgid "ClientApp"
 msgstr "客户端"
 
-#: flwr.client.clientapp.ClientApp:1 flwr.common.typing.ClientMessage:1
+#: flwr.client.client_app.ClientApp:1 flwr.common.typing.ClientMessage:1
 #: flwr.common.typing.DisconnectRes:1 flwr.common.typing.EvaluateIns:1
 #: flwr.common.typing.EvaluateRes:1 flwr.common.typing.FitIns:1
 #: flwr.common.typing.FitRes:1 flwr.common.typing.GetParametersIns:1
@@ -7526,24 +7526,24 @@ msgid "Bases: :py:class:`object`"
 msgstr ""
 
 #: flwr.client.app.start_client:33 flwr.client.app.start_numpy_client:36
-#: flwr.client.clientapp.ClientApp:4 flwr.server.app.start_server:41
+#: flwr.client.client_app.ClientApp:4 flwr.server.app.start_server:41
 #: flwr.server.driver.app.start_driver:30 of
 msgid "Examples"
 msgstr "实例"
 
-#: flwr.client.clientapp.ClientApp:5 of
+#: flwr.client.client_app.ClientApp:5 of
 msgid ""
 "Assuming a typical `Client` implementation named `FlowerClient`, you can "
 "wrap it in a `ClientApp` as follows:"
 msgstr ""
 
-#: flwr.client.clientapp.ClientApp:16 of
+#: flwr.client.client_app.ClientApp:16 of
 msgid ""
 "If the above code is in a Python module called `client`, it can be "
 "started as follows:"
 msgstr ""
 
-#: flwr.client.clientapp.ClientApp:21 of
+#: flwr.client.client_app.ClientApp:21 of
 msgid ""
 "In this `client:app` example, `client` refers to the Python module "
 "`client.py` in which the previous code lives in and `app` refers to the "
diff --git a/src/py/flwr/client/__init__.py b/src/py/flwr/client/__init__.py
index f359fb472cbe..a721fb584164 100644
--- a/src/py/flwr/client/__init__.py
+++ b/src/py/flwr/client/__init__.py
@@ -19,7 +19,7 @@
 from .app import start_client as start_client
 from .app import start_numpy_client as start_numpy_client
 from .client import Client as Client
-from .clientapp import ClientApp as ClientApp
+from .client_app import ClientApp as ClientApp
 from .numpy_client import NumPyClient as NumPyClient
 from .typing import ClientFn as ClientFn
 
diff --git a/src/py/flwr/client/app.py b/src/py/flwr/client/app.py
index 2d49b3a44f08..93d654379cfc 100644
--- a/src/py/flwr/client/app.py
+++ b/src/py/flwr/client/app.py
@@ -23,7 +23,7 @@
 from typing import Callable, ContextManager, Optional, Tuple, Union
 
 from flwr.client.client import Client
-from flwr.client.clientapp import ClientApp
+from flwr.client.client_app import ClientApp
 from flwr.client.typing import ClientFn
 from flwr.common import GRPC_MAX_MESSAGE_LENGTH, EventType, Message, event
 from flwr.common.address import parse_address
@@ -37,7 +37,7 @@
 from flwr.common.exit_handlers import register_exit_handlers
 from flwr.common.logger import log, warn_deprecated_feature, warn_experimental_feature
 
-from .clientapp import load_client_app
+from .client_app import load_client_app
 from .grpc_client.connection import grpc_connection
 from .grpc_rere_client.connection import grpc_request_response
 from .message_handler.message_handler import handle_control_message
diff --git a/src/py/flwr/client/clientapp.py b/src/py/flwr/client/client_app.py
similarity index 100%
rename from src/py/flwr/client/clientapp.py
rename to src/py/flwr/client/client_app.py
diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/backend.py b/src/py/flwr/server/superlink/fleet/vce/backend/backend.py
index f2796a5758a0..1d5e3a6a51ad 100644
--- a/src/py/flwr/server/superlink/fleet/vce/backend/backend.py
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/backend.py
@@ -18,7 +18,7 @@
 from abc import ABC, abstractmethod
 from typing import Callable, Dict, Tuple
 
-from flwr.client.clientapp import ClientApp
+from flwr.client.client_app import ClientApp
 from flwr.common.context import Context
 from flwr.common.message import Message
 from flwr.common.typing import ConfigsRecordValues
diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
index b29d76b239e5..602fd94bea51 100644
--- a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
@@ -18,7 +18,7 @@
 from logging import INFO
 from typing import Callable, Dict, List, Tuple, Union
 
-from flwr.client.clientapp import ClientApp
+from flwr.client.client_app import ClientApp
 from flwr.common.context import Context
 from flwr.common.logger import log
 from flwr.common.message import Message
diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py
index f0cca527ab96..e75e01ba3af0 100644
--- a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py
@@ -22,7 +22,7 @@
 import ray
 
 from flwr.client import Client, NumPyClient
-from flwr.client.clientapp import ClientApp
+from flwr.client.client_app import ClientApp
 from flwr.common import (
     Config,
     ConfigsRecord,
diff --git a/src/py/flwr/server/superlink/fleet/vce/vce_api.py b/src/py/flwr/server/superlink/fleet/vce/vce_api.py
index 666e7e7d9ec3..5d194632541e 100644
--- a/src/py/flwr/server/superlink/fleet/vce/vce_api.py
+++ b/src/py/flwr/server/superlink/fleet/vce/vce_api.py
@@ -19,7 +19,7 @@
 from logging import ERROR, INFO
 from typing import Dict, Optional
 
-from flwr.client.clientapp import ClientApp, load_client_app
+from flwr.client.client_app import ClientApp, load_client_app
 from flwr.client.node_state import NodeState
 from flwr.common.logger import log
 from flwr.server.superlink.state import StateFactory
diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py
index 5ac0b2c27484..2b8a683cd893 100644
--- a/src/py/flwr/simulation/ray_transport/ray_actor.py
+++ b/src/py/flwr/simulation/ray_transport/ray_actor.py
@@ -25,7 +25,7 @@
 from ray import ObjectRef
 from ray.util.actor_pool import ActorPool
 
-from flwr.client.clientapp import ClientApp
+from flwr.client.client_app import ClientApp
 from flwr.common import Context, Message
 from flwr.common.logger import log
 
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
index a45321ed2368..6d04fa875201 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
@@ -21,7 +21,7 @@
 
 from flwr import common
 from flwr.client import ClientFn
-from flwr.client.clientapp import ClientApp
+from flwr.client.client_app import ClientApp
 from flwr.client.node_state import NodeState
 from flwr.common import Message, Metadata, RecordSet
 from flwr.common.constant import (
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
index 24fe3546e7d9..684fad60bf74 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
@@ -22,7 +22,7 @@
 import ray
 
 from flwr.client import Client, NumPyClient
-from flwr.client.clientapp import ClientApp
+from flwr.client.client_app import ClientApp
 from flwr.common import (
     Config,
     ConfigsRecord,

From 3b686d5748ae84c5cfd46e3935638cd21c6773d4 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Tue, 27 Feb 2024 20:50:15 +0100
Subject: [PATCH 069/102] Handle loading of non-existing `ClientApp` in
 `RayBackend` (#3018)

---
 .../superlink/fleet/vce/backend/raybackend.py |  45 +++++--
 .../fleet/vce/backend/raybackend_test.py      | 125 +++++++++++++-----
 .../simulation/ray_transport/ray_actor.py     |   5 +-
 3 files changed, 127 insertions(+), 48 deletions(-)

diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
index 602fd94bea51..409deb077f1d 100644
--- a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
@@ -15,10 +15,12 @@
 """Ray backend for the Fleet API using the Simulation Engine."""
 
 import pathlib
-from logging import INFO
+from logging import ERROR, INFO
 from typing import Callable, Dict, List, Tuple, Union
 
-from flwr.client.client_app import ClientApp
+import ray
+
+from flwr.client.client_app import ClientApp, LoadClientAppError
 from flwr.common.context import Context
 from flwr.common.logger import log
 from flwr.common.message import Message
@@ -46,6 +48,9 @@ def __init__(
         log(INFO, "Initialising: %s", self.__class__.__name__)
         log(INFO, "Backend config: %s", backend_config)
 
+        if not pathlib.Path(work_dir).exists():
+            raise ValueError(f"Specified work_dir {work_dir} does not exist.")
+
         # Init ray and append working dir if needed
         runtime_env = (
             self._configure_runtime_env(work_dir=work_dir) if work_dir else None
@@ -138,22 +143,34 @@ async def process_message(
         """
         node_id = message.metadata.dst_node_id
 
-        # Submite a task to the pool
-        future = await self.pool.submit(
-            lambda a, a_fn, mssg, cid, state: a.run.remote(a_fn, mssg, cid, state),
-            (app, message, str(node_id), context),
-        )
+        try:
+            # Submite a task to the pool
+            future = await self.pool.submit(
+                lambda a, a_fn, mssg, cid, state: a.run.remote(a_fn, mssg, cid, state),
+                (app, message, str(node_id), context),
+            )
 
-        await future
+            await future
 
-        # Fetch result
-        (
-            out_mssg,
-            updated_context,
-        ) = await self.pool.fetch_result_and_return_actor_to_pool(future)
+            # Fetch result
+            (
+                out_mssg,
+                updated_context,
+            ) = await self.pool.fetch_result_and_return_actor_to_pool(future)
 
-        return out_mssg, updated_context
+            return out_mssg, updated_context
+
+        except LoadClientAppError as load_ex:
+            log(
+                ERROR,
+                "An exception was raised when processing a message. Terminating %s",
+                self.__class__.__name__,
+            )
+            await self.terminate()
+            raise load_ex
 
     async def terminate(self) -> None:
         """Terminate all actors in actor pool."""
         await self.pool.terminate_all_actors()
+        ray.shutdown()
+        log(INFO, "Terminated %s", self.__class__.__name__)
diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py
index e75e01ba3af0..fd246b5fc2af 100644
--- a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py
@@ -16,13 +16,12 @@
 
 import asyncio
 from math import pi
+from pathlib import Path
 from typing import Callable, Dict, Optional, Tuple, Union
 from unittest import IsolatedAsyncioTestCase
 
-import ray
-
 from flwr.client import Client, NumPyClient
-from flwr.client.client_app import ClientApp
+from flwr.client.client_app import ClientApp, LoadClientAppError, load_client_app
 from flwr.common import (
     Config,
     ConfigsRecord,
@@ -35,8 +34,7 @@
 )
 from flwr.common.constant import MESSAGE_TYPE_GET_PROPERTIES
 from flwr.common.recordset_compat import getpropertiesins_to_recordset
-
-from .raybackend import RayBackend
+from flwr.server.superlink.fleet.vce.backend.raybackend import RayBackend
 
 
 class DummyClient(NumPyClient):
@@ -60,6 +58,19 @@ def _load_app() -> ClientApp:
     return ClientApp(client_fn=get_dummy_client)
 
 
+client_app = ClientApp(
+    client_fn=get_dummy_client,
+)
+
+
+def _load_from_module(client_app_module_name: str) -> Callable[[], ClientApp]:
+    def _load_app() -> ClientApp:
+        app: ClientApp = load_client_app(client_app_module_name)
+        return app
+
+    return _load_app
+
+
 async def backend_build_process_and_termination(
     backend: RayBackend,
     process_args: Optional[Tuple[Callable[[], ClientApp], Message, Context]] = None,
@@ -73,11 +84,38 @@ async def backend_build_process_and_termination(
 
     await backend.terminate()
 
-    ray.shutdown()
-
     return to_return
 
 
+def _create_message_and_context() -> Tuple[Message, Context, float]:
+
+    # Construct a Message
+    mult_factor = 2024
+    getproperties_ins = GetPropertiesIns(config={"factor": mult_factor})
+    recordset = getpropertiesins_to_recordset(getproperties_ins)
+    message = Message(
+        content=recordset,
+        metadata=Metadata(
+            run_id=0,
+            message_id="",
+            group_id="",
+            src_node_id=0,
+            dst_node_id=0,
+            reply_to_message="",
+            ttl="",
+            message_type=MESSAGE_TYPE_GET_PROPERTIES,
+        ),
+    )
+
+    # Construct emtpy Context
+    context = Context(state=RecordSet())
+
+    # Expected output
+    expected_output = pi * mult_factor
+
+    return message, context, expected_output
+
+
 class AsyncTestRayBackend(IsolatedAsyncioTestCase):
     """A basic class that allows runnig multliple asyncio tests."""
 
@@ -88,33 +126,18 @@ def test_backend_creation_and_termination(self) -> None:
             backend_build_process_and_termination(backend=backend, process_args=None)
         )
 
-    def test_backend_creation_submit_and_termination(self) -> None:
-        """Test submit."""
-        backend = RayBackend(backend_config={}, work_dir="")
+    def test_backend_creation_submit_and_termination(
+        self,
+        client_app_loader: Callable[[], ClientApp] = _load_app,
+        workdir: str = "",
+    ) -> None:
+        """Test submitting a message to a given ClientApp."""
+        backend = RayBackend(backend_config={}, work_dir=workdir)
 
         # Define ClientApp
-        client_app_callable = _load_app
-
-        # Construct a Message
-        mult_factor = 2024
-        getproperties_ins = GetPropertiesIns(config={"factor": mult_factor})
-        recordset = getpropertiesins_to_recordset(getproperties_ins)
-        message = Message(
-            content=recordset,
-            metadata=Metadata(
-                run_id=0,
-                message_id="",
-                group_id="",
-                src_node_id=0,
-                dst_node_id=0,
-                reply_to_message="",
-                ttl="",
-                message_type=MESSAGE_TYPE_GET_PROPERTIES,
-            ),
-        )
+        client_app_callable = client_app_loader
 
-        # Construct emtpy Context
-        context = Context(state=RecordSet())
+        message, context, expected_output = _create_message_and_context()
 
         res = asyncio.run(
             backend_build_process_and_termination(
@@ -131,11 +154,47 @@ def test_backend_creation_submit_and_termination(self) -> None:
         content = out_mssg.content
         assert (
             content.configs_records["getpropertiesres.properties"]["result"]
-            == pi * mult_factor
+            == expected_output
         )
 
         # Verify context is correct
         obtained_result_in_context = updated_context.state.configs_records["result"][
             "result"
         ]
-        assert obtained_result_in_context == pi * mult_factor
+        assert obtained_result_in_context == expected_output
+
+    def test_backend_creation_submit_and_termination_non_existing_client_app(
+        self,
+    ) -> None:
+        """Testing with ClientApp module that does not exist."""
+        with self.assertRaises(LoadClientAppError):
+            self.test_backend_creation_submit_and_termination(
+                client_app_loader=_load_from_module("a_non_existing_module:app")
+            )
+
+    def test_backend_creation_submit_and_termination_existing_client_app(
+        self,
+    ) -> None:
+        """Testing with ClientApp module that exist."""
+        # Resolve what should be the workdir to pass upon Backend initialisation
+        file_path = Path(__file__)
+        working_dir = Path.cwd()
+        rel_workdir = file_path.relative_to(working_dir)
+
+        # Susbtract last element
+        rel_workdir_str = str(rel_workdir.parent)
+
+        self.test_backend_creation_submit_and_termination(
+            client_app_loader=_load_from_module("raybackend_test:client_app"),
+            workdir=rel_workdir_str,
+        )
+
+    def test_backend_creation_submit_and_termination_existing_client_app_unsetworkdir(
+        self,
+    ) -> None:
+        """Testing with ClientApp module that exist but the passed workdir does not."""
+        with self.assertRaises(ValueError):
+            self.test_backend_creation_submit_and_termination(
+                client_app_loader=_load_from_module("raybackend_test:client_app"),
+                workdir="/?&%$^#%@$!",
+            )
diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py
index 2b8a683cd893..08d0576e39f0 100644
--- a/src/py/flwr/simulation/ray_transport/ray_actor.py
+++ b/src/py/flwr/simulation/ray_transport/ray_actor.py
@@ -25,7 +25,7 @@
 from ray import ObjectRef
 from ray.util.actor_pool import ActorPool
 
-from flwr.client.client_app import ClientApp
+from flwr.client.client_app import ClientApp, LoadClientAppError
 from flwr.common import Context, Message
 from flwr.common.logger import log
 
@@ -67,6 +67,9 @@ def run(
             # Handle task message
             out_message = app(message=message, context=context)
 
+        except LoadClientAppError as load_ex:
+            raise load_ex
+
         except Exception as ex:
             client_trace = traceback.format_exc()
             mssg = (

From 4910520eaa39090476cb38ec503379a3d1559932 Mon Sep 17 00:00:00 2001
From: Heng Pan <pan@flower.ai>
Date: Wed, 28 Feb 2024 10:51:18 +0000
Subject: [PATCH 070/102] rename node_id to partition_id (#3023)

---
 examples/advanced-pytorch/utils.py                 |  4 ++--
 examples/embedded-devices/client_pytorch.py        |  4 ++--
 examples/embedded-devices/client_tf.py             |  4 ++--
 .../pytorch-from-centralized-to-federated/cifar.py |  4 ++--
 .../client.py                                      |  4 ++--
 .../pytorch-from-centralized-to-federated/run.sh   |  2 +-
 examples/quickstart-huggingface/README.md          |  4 ++--
 examples/quickstart-huggingface/client.py          | 14 +++++++-------
 examples/quickstart-huggingface/run.sh             |  2 +-
 examples/quickstart-mlx/README.md                  |  8 ++++----
 examples/quickstart-mlx/client.py                  |  4 ++--
 examples/quickstart-mlx/run.sh                     |  2 +-
 examples/quickstart-pandas/README.md               |  4 ++--
 examples/quickstart-pandas/client.py               |  6 +++---
 examples/quickstart-pandas/run.sh                  |  2 +-
 examples/quickstart-pytorch-lightning/README.md    |  6 +++---
 examples/quickstart-pytorch-lightning/client.py    |  6 +++---
 examples/quickstart-pytorch-lightning/run.sh       |  2 +-
 examples/quickstart-pytorch/README.md              |  6 +++---
 examples/quickstart-pytorch/client.py              | 12 ++++++------
 examples/quickstart-pytorch/run.sh                 |  2 +-
 examples/quickstart-sklearn-tabular/README.md      |  6 +++---
 examples/quickstart-sklearn-tabular/client.py      |  4 ++--
 examples/quickstart-sklearn-tabular/run.sh         |  2 +-
 examples/quickstart-tensorflow/client.py           |  4 ++--
 examples/quickstart-tensorflow/run.sh              |  2 +-
 examples/sklearn-logreg-mnist/README.md            |  4 ++--
 examples/sklearn-logreg-mnist/client.py            |  4 ++--
 examples/sklearn-logreg-mnist/run.sh               |  2 +-
 examples/xgboost-comprehensive/README.md           |  4 ++--
 examples/xgboost-comprehensive/client.py           |  4 ++--
 examples/xgboost-comprehensive/run_bagging.sh      |  2 +-
 examples/xgboost-comprehensive/run_cyclic.sh       |  2 +-
 examples/xgboost-comprehensive/sim.py              |  6 +++---
 examples/xgboost-comprehensive/utils.py            |  4 ++--
 examples/xgboost-quickstart/README.md              |  4 ++--
 examples/xgboost-quickstart/client.py              | 10 +++++-----
 examples/xgboost-quickstart/run.sh                 |  2 +-
 38 files changed, 84 insertions(+), 84 deletions(-)

diff --git a/examples/advanced-pytorch/utils.py b/examples/advanced-pytorch/utils.py
index 186f079010dc..4a0f6918cdd6 100644
--- a/examples/advanced-pytorch/utils.py
+++ b/examples/advanced-pytorch/utils.py
@@ -9,10 +9,10 @@
 warnings.filterwarnings("ignore")
 
 
-def load_partition(node_id, toy: bool = False):
+def load_partition(partition_id, toy: bool = False):
     """Load partition CIFAR10 data."""
     fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
-    partition = fds.load_partition(node_id)
+    partition = fds.load_partition(partition_id)
     # Divide data on each node: 80% train, 20% test
     partition_train_test = partition.train_test_split(test_size=0.2)
     partition_train_test = partition_train_test.with_transform(apply_transforms)
diff --git a/examples/embedded-devices/client_pytorch.py b/examples/embedded-devices/client_pytorch.py
index f326db7c678c..3f1e6c7d51b7 100644
--- a/examples/embedded-devices/client_pytorch.py
+++ b/examples/embedded-devices/client_pytorch.py
@@ -105,8 +105,8 @@ def apply_transforms(batch):
 
     trainsets = []
     validsets = []
-    for node_id in range(NUM_CLIENTS):
-        partition = fds.load_partition(node_id, "train")
+    for partition_id in range(NUM_CLIENTS):
+        partition = fds.load_partition(partition_id, "train")
         # Divide data on each node: 90% train, 10% test
         partition = partition.train_test_split(test_size=0.1)
         partition = partition.with_transform(apply_transforms)
diff --git a/examples/embedded-devices/client_tf.py b/examples/embedded-devices/client_tf.py
index ae793ecd81e0..d59b31ab1569 100644
--- a/examples/embedded-devices/client_tf.py
+++ b/examples/embedded-devices/client_tf.py
@@ -40,8 +40,8 @@ def prepare_dataset(use_mnist: bool):
         fds = FederatedDataset(dataset="cifar10", partitioners={"train": NUM_CLIENTS})
         img_key = "img"
     partitions = []
-    for node_id in range(NUM_CLIENTS):
-        partition = fds.load_partition(node_id, "train")
+    for partition_id in range(NUM_CLIENTS):
+        partition = fds.load_partition(partition_id, "train")
         partition.set_format("numpy")
         # Divide data on each node: 90% train, 10% test
         partition = partition.train_test_split(test_size=0.1)
diff --git a/examples/pytorch-from-centralized-to-federated/cifar.py b/examples/pytorch-from-centralized-to-federated/cifar.py
index e8f3ec3fd724..277a21da2e70 100644
--- a/examples/pytorch-from-centralized-to-federated/cifar.py
+++ b/examples/pytorch-from-centralized-to-federated/cifar.py
@@ -51,10 +51,10 @@ def forward(self, x: Tensor) -> Tensor:
         return x
 
 
-def load_data(node_id: int):
+def load_data(partition_id: int):
     """Load partition CIFAR10 data."""
     fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
-    partition = fds.load_partition(node_id)
+    partition = fds.load_partition(partition_id)
     # Divide data on each node: 80% train, 20% test
     partition_train_test = partition.train_test_split(test_size=0.2)
     pytorch_transforms = Compose(
diff --git a/examples/pytorch-from-centralized-to-federated/client.py b/examples/pytorch-from-centralized-to-federated/client.py
index 2a3bbccae2bb..9df4739e0aab 100644
--- a/examples/pytorch-from-centralized-to-federated/client.py
+++ b/examples/pytorch-from-centralized-to-federated/client.py
@@ -81,11 +81,11 @@ def evaluate(
 def main() -> None:
     """Load data, start CifarClient."""
     parser = argparse.ArgumentParser(description="Flower")
-    parser.add_argument("--node-id", type=int, required=True, choices=range(0, 10))
+    parser.add_argument("--partition-id", type=int, required=True, choices=range(0, 10))
     args = parser.parse_args()
 
     # Load data
-    trainloader, testloader = cifar.load_data(args.node_id)
+    trainloader, testloader = cifar.load_data(args.partition_id)
 
     # Load model
     model = cifar.Net().to(DEVICE).train()
diff --git a/examples/pytorch-from-centralized-to-federated/run.sh b/examples/pytorch-from-centralized-to-federated/run.sh
index 1ed51dd787ac..6ddf6ad476b4 100755
--- a/examples/pytorch-from-centralized-to-federated/run.sh
+++ b/examples/pytorch-from-centralized-to-federated/run.sh
@@ -6,7 +6,7 @@ sleep 3  # Sleep for 3s to give the server enough time to start
 
 for i in $(seq 0 1); do
     echo "Starting client $i"
-    python client.py --node-id $i &
+    python client.py --partition-id $i &
 done
 
 # This will allow you to use CTRL+C to stop all background processes
diff --git a/examples/quickstart-huggingface/README.md b/examples/quickstart-huggingface/README.md
index 5fdba887f181..ce7790cd4af5 100644
--- a/examples/quickstart-huggingface/README.md
+++ b/examples/quickstart-huggingface/README.md
@@ -62,13 +62,13 @@ Now you are ready to start the Flower clients which will participate in the lear
 Start client 1 in the first terminal:
 
 ```shell
-python3 client.py --node-id 0
+python3 client.py --partition-id 0
 ```
 
 Start client 2 in the second terminal:
 
 ```shell
-python3 client.py --node-id 1
+python3 client.py --partition-id 1
 ```
 
 You will see that PyTorch is starting a federated training.
diff --git a/examples/quickstart-huggingface/client.py b/examples/quickstart-huggingface/client.py
index 5dc461d30536..9be08d0cbcf4 100644
--- a/examples/quickstart-huggingface/client.py
+++ b/examples/quickstart-huggingface/client.py
@@ -17,10 +17,10 @@
 CHECKPOINT = "distilbert-base-uncased"  # transformer model checkpoint
 
 
-def load_data(node_id):
+def load_data(partition_id):
     """Load IMDB data (training and eval)"""
     fds = FederatedDataset(dataset="imdb", partitioners={"train": 1_000})
-    partition = fds.load_partition(node_id)
+    partition = fds.load_partition(partition_id)
     # Divide data: 80% train, 20% test
     partition_train_test = partition.train_test_split(test_size=0.2)
 
@@ -78,12 +78,12 @@ def test(net, testloader):
     return loss, accuracy
 
 
-def main(node_id):
+def main(partition_id):
     net = AutoModelForSequenceClassification.from_pretrained(
         CHECKPOINT, num_labels=2
     ).to(DEVICE)
 
-    trainloader, testloader = load_data(node_id)
+    trainloader, testloader = load_data(partition_id)
 
     # Flower client
     class IMDBClient(fl.client.NumPyClient):
@@ -116,12 +116,12 @@ def evaluate(self, parameters, config):
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Flower")
     parser.add_argument(
-        "--node-id",
+        "--partition-id",
         choices=list(range(1_000)),
         required=True,
         type=int,
         help="Partition of the dataset divided into 1,000 iid partitions created "
         "artificially.",
     )
-    node_id = parser.parse_args().node_id
-    main(node_id)
+    partition_id = parser.parse_args().partition_id
+    main(partition_id)
diff --git a/examples/quickstart-huggingface/run.sh b/examples/quickstart-huggingface/run.sh
index e722a24a21a9..fa989eab1471 100755
--- a/examples/quickstart-huggingface/run.sh
+++ b/examples/quickstart-huggingface/run.sh
@@ -6,7 +6,7 @@ sleep 3  # Sleep for 3s to give the server enough time to start
 
 for i in `seq 0 1`; do
     echo "Starting client $i"
-    python client.py --node-id ${i}&
+    python client.py --partition-id ${i}&
 done
 
 # This will allow you to use CTRL+C to stop all background processes
diff --git a/examples/quickstart-mlx/README.md b/examples/quickstart-mlx/README.md
index d94a87a014f7..cca55bcb946a 100644
--- a/examples/quickstart-mlx/README.md
+++ b/examples/quickstart-mlx/README.md
@@ -66,19 +66,19 @@ following commands.
 Start a first client in the first terminal:
 
 ```shell
-python3 client.py --node-id 0
+python3 client.py --partition-id 0
 ```
 
 And another one in the second terminal:
 
 ```shell
-python3 client.py --node-id 1
+python3 client.py --partition-id 1
 ```
 
 If you want to utilize your GPU, you can use the `--gpu` argument:
 
 ```shell
-python3 client.py --gpu --node-id 2
+python3 client.py --gpu --partition-id 2
 ```
 
 Note that you can start many more clients if you want, but each will have to be in its own terminal.
@@ -96,7 +96,7 @@ We will use `flwr_datasets` to easily download and partition the `MNIST` dataset
 
 ```python
 fds = FederatedDataset(dataset="mnist", partitioners={"train": 3})
-partition = fds.load_partition(node_id = args.node_id)
+partition = fds.load_partition(partition_id = args.partition_id)
 partition_splits = partition.train_test_split(test_size=0.2)
 
 partition_splits['train'].set_format("numpy")
diff --git a/examples/quickstart-mlx/client.py b/examples/quickstart-mlx/client.py
index 3b506399a5f1..faba2b94d6bd 100644
--- a/examples/quickstart-mlx/client.py
+++ b/examples/quickstart-mlx/client.py
@@ -89,7 +89,7 @@ def evaluate(self, parameters, config):
     parser = argparse.ArgumentParser("Train a simple MLP on MNIST with MLX.")
     parser.add_argument("--gpu", action="store_true", help="Use the Metal back-end.")
     parser.add_argument(
-        "--node-id",
+        "--partition-id",
         choices=[0, 1, 2],
         type=int,
         help="Partition of the dataset divided into 3 iid partitions created artificially.",
@@ -106,7 +106,7 @@ def evaluate(self, parameters, config):
     learning_rate = 1e-1
 
     fds = FederatedDataset(dataset="mnist", partitioners={"train": 3})
-    partition = fds.load_partition(node_id=args.node_id)
+    partition = fds.load_partition(partition_id=args.partition_id)
     partition_splits = partition.train_test_split(test_size=0.2)
 
     partition_splits["train"].set_format("numpy")
diff --git a/examples/quickstart-mlx/run.sh b/examples/quickstart-mlx/run.sh
index 70281049517d..40d211848c07 100755
--- a/examples/quickstart-mlx/run.sh
+++ b/examples/quickstart-mlx/run.sh
@@ -8,7 +8,7 @@ sleep 3  # Sleep for 3s to give the server enough time to start
 
 for i in $(seq 0 1); do
     echo "Starting client $i"
-    python client.py --node-id $i &
+    python client.py --partition-id $i &
 done
 
 # Enable CTRL+C to stop all background processes
diff --git a/examples/quickstart-pandas/README.md b/examples/quickstart-pandas/README.md
index efcda43cf34d..dd69f3ead3cb 100644
--- a/examples/quickstart-pandas/README.md
+++ b/examples/quickstart-pandas/README.md
@@ -70,13 +70,13 @@ Now you are ready to start the Flower clients which will participate in the lear
 Start client 1 in the first terminal:
 
 ```shell
-$ python3 client.py --node-id 0
+$ python3 client.py --partition-id 0
 ```
 
 Start client 2 in the second terminal:
 
 ```shell
-$ python3 client.py --node-id 1
+$ python3 client.py --partition-id 1
 ```
 
 You will see that the server is printing aggregated statistics about the dataset distributed amongst clients. Have a look to the [Flower Quickstarter documentation](https://flower.ai/docs/quickstart-pandas.html) for a detailed explanation.
diff --git a/examples/quickstart-pandas/client.py b/examples/quickstart-pandas/client.py
index 8585922e4572..c52b7c65b04c 100644
--- a/examples/quickstart-pandas/client.py
+++ b/examples/quickstart-pandas/client.py
@@ -42,14 +42,14 @@ def fit(
 
     parser = argparse.ArgumentParser(description="Flower")
     parser.add_argument(
-        "--node-id",
+        "--partition-id",
         type=int,
         choices=range(0, N_CLIENTS),
         required=True,
-        help="Specifies the node id of artificially partitioned datasets.",
+        help="Specifies the partition id of artificially partitioned datasets.",
     )
     args = parser.parse_args()
-    partition_id = args.node_id
+    partition_id = args.partition_id
 
     # Load the partition data
     fds = FederatedDataset(dataset="hitorilabs/iris", partitioners={"train": N_CLIENTS})
diff --git a/examples/quickstart-pandas/run.sh b/examples/quickstart-pandas/run.sh
index 571fa8bfb3e4..2ae1e582b8cf 100755
--- a/examples/quickstart-pandas/run.sh
+++ b/examples/quickstart-pandas/run.sh
@@ -4,7 +4,7 @@ sleep 3  # Sleep for 3s to give the server enough time to start
 
 for i in `seq 0 1`; do
     echo "Starting client $i"
-    python client.py --node-id ${i} &
+    python client.py --partition-id ${i} &
 done
 
 # This will allow you to use CTRL+C to stop all background processes
diff --git a/examples/quickstart-pytorch-lightning/README.md b/examples/quickstart-pytorch-lightning/README.md
index 1d404a5d714f..fb29c7e9e9ea 100644
--- a/examples/quickstart-pytorch-lightning/README.md
+++ b/examples/quickstart-pytorch-lightning/README.md
@@ -57,20 +57,20 @@ Afterwards you are ready to start the Flower server as well as the clients. You
 python server.py
 ```
 
-Now you are ready to start the Flower clients which will participate in the learning. We need to specify the node id to
+Now you are ready to start the Flower clients which will participate in the learning. We need to specify the partition id to
 use different partitions of the data on different nodes.  To do so simply open two more terminal windows and run the
 following commands.
 
 Start client 1 in the first terminal:
 
 ```shell
-python client.py --node-id 0
+python client.py --partition-id 0
 ```
 
 Start client 2 in the second terminal:
 
 ```shell
-python client.py --node-id 1
+python client.py --partition-id 1
 ```
 
 You will see that PyTorch is starting a federated training. Look at the [code](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch) for a detailed explanation.
diff --git a/examples/quickstart-pytorch-lightning/client.py b/examples/quickstart-pytorch-lightning/client.py
index fc5f1ee03cfe..6e21259cc492 100644
--- a/examples/quickstart-pytorch-lightning/client.py
+++ b/examples/quickstart-pytorch-lightning/client.py
@@ -58,18 +58,18 @@ def _set_parameters(model, parameters):
 def main() -> None:
     parser = argparse.ArgumentParser(description="Flower")
     parser.add_argument(
-        "--node-id",
+        "--partition-id",
         type=int,
         choices=range(0, 10),
         required=True,
         help="Specifies the artificial data partition",
     )
     args = parser.parse_args()
-    node_id = args.node_id
+    partition_id = args.partition_id
 
     # Model and data
     model = mnist.LitAutoEncoder()
-    train_loader, val_loader, test_loader = mnist.load_data(node_id)
+    train_loader, val_loader, test_loader = mnist.load_data(partition_id)
 
     # Flower client
     client = FlowerClient(model, train_loader, val_loader, test_loader).to_client()
diff --git a/examples/quickstart-pytorch-lightning/run.sh b/examples/quickstart-pytorch-lightning/run.sh
index 60893a9a055b..62a1dac199bd 100755
--- a/examples/quickstart-pytorch-lightning/run.sh
+++ b/examples/quickstart-pytorch-lightning/run.sh
@@ -6,7 +6,7 @@ sleep 3 # Sleep for 3s to give the server enough time to start
 
 for i in $(seq 0 1); do
     echo "Starting client $i"
-    python client.py --node-id "${i}" &
+    python client.py --partition-id "${i}" &
 done
 
 # This will allow you to use CTRL+C to stop all background processes
diff --git a/examples/quickstart-pytorch/README.md b/examples/quickstart-pytorch/README.md
index 3b9b9b310608..02c9b4b38498 100644
--- a/examples/quickstart-pytorch/README.md
+++ b/examples/quickstart-pytorch/README.md
@@ -55,20 +55,20 @@ Afterwards you are ready to start the Flower server as well as the clients. You
 python3 server.py
 ```
 
-Now you are ready to start the Flower clients which will participate in the learning. We need to specify the node id to
+Now you are ready to start the Flower clients which will participate in the learning. We need to specify the partition id to
 use different partitions of the data on different nodes.  To do so simply open two more terminal windows and run the
 following commands.
 
 Start client 1 in the first terminal:
 
 ```shell
-python3 client.py --node-id 0
+python3 client.py --partition-id 0
 ```
 
 Start client 2 in the second terminal:
 
 ```shell
-python3 client.py --node-id 1
+python3 client.py --partition-id 1
 ```
 
 You will see that PyTorch is starting a federated training. Look at the [code](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch) for a detailed explanation.
diff --git a/examples/quickstart-pytorch/client.py b/examples/quickstart-pytorch/client.py
index b5ea4c94dd21..e640ce111dff 100644
--- a/examples/quickstart-pytorch/client.py
+++ b/examples/quickstart-pytorch/client.py
@@ -69,10 +69,10 @@ def test(net, testloader):
     return loss, accuracy
 
 
-def load_data(node_id):
+def load_data(partition_id):
     """Load partition CIFAR10 data."""
     fds = FederatedDataset(dataset="cifar10", partitioners={"train": 3})
-    partition = fds.load_partition(node_id)
+    partition = fds.load_partition(partition_id)
     # Divide data on each node: 80% train, 20% test
     partition_train_test = partition.train_test_split(test_size=0.2)
     pytorch_transforms = Compose(
@@ -94,20 +94,20 @@ def apply_transforms(batch):
 # 2. Federation of the pipeline with Flower
 # #############################################################################
 
-# Get node id
+# Get partition id
 parser = argparse.ArgumentParser(description="Flower")
 parser.add_argument(
-    "--node-id",
+    "--partition-id",
     choices=[0, 1, 2],
     required=True,
     type=int,
     help="Partition of the dataset divided into 3 iid partitions created artificially.",
 )
-node_id = parser.parse_args().node_id
+partition_id = parser.parse_args().partition_id
 
 # Load model and data (simple CNN, CIFAR-10)
 net = Net().to(DEVICE)
-trainloader, testloader = load_data(node_id=node_id)
+trainloader, testloader = load_data(partition_id=partition_id)
 
 
 # Define Flower client
diff --git a/examples/quickstart-pytorch/run.sh b/examples/quickstart-pytorch/run.sh
index cdace99bb8df..6ca9c8cafec9 100755
--- a/examples/quickstart-pytorch/run.sh
+++ b/examples/quickstart-pytorch/run.sh
@@ -8,7 +8,7 @@ sleep 3  # Sleep for 3s to give the server enough time to start
 
 for i in $(seq 0 1); do
     echo "Starting client $i"
-    python client.py --node-id "$i" &
+    python client.py --partition-id "$i" &
 done
 
 # Enable CTRL+C to stop all background processes
diff --git a/examples/quickstart-sklearn-tabular/README.md b/examples/quickstart-sklearn-tabular/README.md
index 373aaea5999c..a975a9392800 100644
--- a/examples/quickstart-sklearn-tabular/README.md
+++ b/examples/quickstart-sklearn-tabular/README.md
@@ -63,15 +63,15 @@ poetry run python3 server.py
 Now you are ready to start the Flower clients which will participate in the learning. To do so simply open two more terminals and run the following command in each:
 
 ```shell
-poetry run python3 client.py --node-id 0 # node-id should be any of {0,1,2}
+poetry run python3 client.py --partition-id 0 # partition-id should be any of {0,1,2}
 ```
 
 Alternatively you can run all of it in one shell as follows:
 
 ```shell
 poetry run python3 server.py &
-poetry run python3 client.py --node-id 0 &
-poetry run python3 client.py --node-id 1
+poetry run python3 client.py --partition-id 0 &
+poetry run python3 client.py --partition-id 1
 ```
 
 You will see that Flower is starting a federated training.
diff --git a/examples/quickstart-sklearn-tabular/client.py b/examples/quickstart-sklearn-tabular/client.py
index 5dc0e88b3c75..fcab8f5d5612 100644
--- a/examples/quickstart-sklearn-tabular/client.py
+++ b/examples/quickstart-sklearn-tabular/client.py
@@ -13,14 +13,14 @@
 
     parser = argparse.ArgumentParser(description="Flower")
     parser.add_argument(
-        "--node-id",
+        "--partition-id",
         type=int,
         choices=range(0, N_CLIENTS),
         required=True,
         help="Specifies the artificial data partition",
     )
     args = parser.parse_args()
-    partition_id = args.node_id
+    partition_id = args.partition_id
 
     # Load the partition data
     fds = FederatedDataset(dataset="hitorilabs/iris", partitioners={"train": N_CLIENTS})
diff --git a/examples/quickstart-sklearn-tabular/run.sh b/examples/quickstart-sklearn-tabular/run.sh
index 48cee1b41b74..f770ca05f8f4 100755
--- a/examples/quickstart-sklearn-tabular/run.sh
+++ b/examples/quickstart-sklearn-tabular/run.sh
@@ -8,7 +8,7 @@ sleep 3  # Sleep for 3s to give the server enough time to start
 
 for i in $(seq 0 1); do
     echo "Starting client $i"
-    python client.py --node-id "${i}" &
+    python client.py --partition-id "${i}" &
 done
 
 # This will allow you to use CTRL+C to stop all background processes
diff --git a/examples/quickstart-tensorflow/client.py b/examples/quickstart-tensorflow/client.py
index 37abbbcc46ec..3e2035c09311 100644
--- a/examples/quickstart-tensorflow/client.py
+++ b/examples/quickstart-tensorflow/client.py
@@ -11,7 +11,7 @@
 # Parse arguments
 parser = argparse.ArgumentParser(description="Flower")
 parser.add_argument(
-    "--node-id",
+    "--partition-id",
     type=int,
     choices=[0, 1, 2],
     required=True,
@@ -26,7 +26,7 @@
 
 # Download and partition dataset
 fds = FederatedDataset(dataset="cifar10", partitioners={"train": 3})
-partition = fds.load_partition(args.node_id, "train")
+partition = fds.load_partition(args.partition_id, "train")
 partition.set_format("numpy")
 
 # Divide data on each node: 80% train, 20% test
diff --git a/examples/quickstart-tensorflow/run.sh b/examples/quickstart-tensorflow/run.sh
index 439abea8df4b..76188f197e3e 100755
--- a/examples/quickstart-tensorflow/run.sh
+++ b/examples/quickstart-tensorflow/run.sh
@@ -6,7 +6,7 @@ sleep 3  # Sleep for 3s to give the server enough time to start
 
 for i in `seq 0 1`; do
     echo "Starting client $i"
-    python client.py --node-id $i &
+    python client.py --partition-id $i &
 done
 
 # This will allow you to use CTRL+C to stop all background processes
diff --git a/examples/sklearn-logreg-mnist/README.md b/examples/sklearn-logreg-mnist/README.md
index 50576d98ba3d..12b1a5e3bc1a 100644
--- a/examples/sklearn-logreg-mnist/README.md
+++ b/examples/sklearn-logreg-mnist/README.md
@@ -62,13 +62,13 @@ Now you are ready to start the Flower clients which will participate in the lear
 Start client 1 in the first terminal:
 
 ```shell
-python3 client.py --node-id 0 # or any integer in {0-9}
+python3 client.py --partition-id 0 # or any integer in {0-9}
 ```
 
 Start client 2 in the second terminal:
 
 ```shell
-python3 client.py --node-id 1 # or any integer in {0-9}
+python3 client.py --partition-id 1 # or any integer in {0-9}
 ```
 
 Alternatively, you can run all of it in one shell as follows:
diff --git a/examples/sklearn-logreg-mnist/client.py b/examples/sklearn-logreg-mnist/client.py
index 3d41cb6fbb21..1e9349df1acc 100644
--- a/examples/sklearn-logreg-mnist/client.py
+++ b/examples/sklearn-logreg-mnist/client.py
@@ -13,14 +13,14 @@
 
     parser = argparse.ArgumentParser(description="Flower")
     parser.add_argument(
-        "--node-id",
+        "--partition-id",
         type=int,
         choices=range(0, N_CLIENTS),
         required=True,
         help="Specifies the artificial data partition",
     )
     args = parser.parse_args()
-    partition_id = args.node_id
+    partition_id = args.partition_id
 
     # Load the partition data
     fds = FederatedDataset(dataset="mnist", partitioners={"train": N_CLIENTS})
diff --git a/examples/sklearn-logreg-mnist/run.sh b/examples/sklearn-logreg-mnist/run.sh
index 48cee1b41b74..f770ca05f8f4 100755
--- a/examples/sklearn-logreg-mnist/run.sh
+++ b/examples/sklearn-logreg-mnist/run.sh
@@ -8,7 +8,7 @@ sleep 3  # Sleep for 3s to give the server enough time to start
 
 for i in $(seq 0 1); do
     echo "Starting client $i"
-    python client.py --node-id "${i}" &
+    python client.py --partition-id "${i}" &
 done
 
 # This will allow you to use CTRL+C to stop all background processes
diff --git a/examples/xgboost-comprehensive/README.md b/examples/xgboost-comprehensive/README.md
index 01fed646d056..dc6d7e3872d6 100644
--- a/examples/xgboost-comprehensive/README.md
+++ b/examples/xgboost-comprehensive/README.md
@@ -120,10 +120,10 @@ You can also run the example without the scripts. First, launch the server:
 python server.py --train-method=bagging/cyclic --pool-size=N --num-clients-per-round=N
 ```
 
-Then run at least two clients (each on a new terminal or computer in your network) passing different `NODE_ID` and all using the same `N` (denoting the total number of clients or data partitions):
+Then run at least two clients (each on a new terminal or computer in your network) passing different `PARTITION_ID` and all using the same `N` (denoting the total number of clients or data partitions):
 
 ```bash
-python client.py --train-method=bagging/cyclic --node-id=NODE_ID --num-partitions=N
+python client.py --train-method=bagging/cyclic --partition-id=PARTITION_ID --num-partitions=N
 ```
 
 ### Flower Simulation Setup
diff --git a/examples/xgboost-comprehensive/client.py b/examples/xgboost-comprehensive/client.py
index 74fbc4f5366a..66daed449fd5 100644
--- a/examples/xgboost-comprehensive/client.py
+++ b/examples/xgboost-comprehensive/client.py
@@ -35,9 +35,9 @@
     resplitter=resplit,
 )
 
-# Load the partition for this `node_id`
+# Load the partition for this `partition_id`
 log(INFO, "Loading partition...")
-partition = fds.load_partition(node_id=args.node_id, split="train")
+partition = fds.load_partition(partition_id=args.partition_id, split="train")
 partition.set_format("numpy")
 
 if args.centralised_eval:
diff --git a/examples/xgboost-comprehensive/run_bagging.sh b/examples/xgboost-comprehensive/run_bagging.sh
index e853a4ef19cb..a6300b781a06 100755
--- a/examples/xgboost-comprehensive/run_bagging.sh
+++ b/examples/xgboost-comprehensive/run_bagging.sh
@@ -8,7 +8,7 @@ sleep 30  # Sleep for 30s to give the server enough time to start
 
 for i in `seq 0 4`; do
     echo "Starting client $i"
-    python3 client.py --node-id=$i --num-partitions=5 --partitioner-type=exponential &
+    python3 client.py --partition-id=$i --num-partitions=5 --partitioner-type=exponential &
 done
 
 # Enable CTRL+C to stop all background processes
diff --git a/examples/xgboost-comprehensive/run_cyclic.sh b/examples/xgboost-comprehensive/run_cyclic.sh
index 47e09fd8faef..258bdf2fe0d8 100755
--- a/examples/xgboost-comprehensive/run_cyclic.sh
+++ b/examples/xgboost-comprehensive/run_cyclic.sh
@@ -8,7 +8,7 @@ sleep 15  # Sleep for 15s to give the server enough time to start
 
 for i in `seq 0 4`; do
     echo "Starting client $i"
-    python3 client.py --node-id=$i --train-method=cyclic --num-partitions=5 --partitioner-type=exponential --centralised-eval &
+    python3 client.py --partition-id=$i --train-method=cyclic --num-partitions=5 --partitioner-type=exponential --centralised-eval &
 done
 
 # Enable CTRL+C to stop all background processes
diff --git a/examples/xgboost-comprehensive/sim.py b/examples/xgboost-comprehensive/sim.py
index ae2a9239a493..b72b23931929 100644
--- a/examples/xgboost-comprehensive/sim.py
+++ b/examples/xgboost-comprehensive/sim.py
@@ -98,9 +98,9 @@ def main():
 
     # Load and process all client partitions. This upfront cost is amortized soon
     # after the simulation begins since clients wont need to preprocess their partition.
-    for node_id in tqdm(range(args.pool_size), desc="Extracting client partition"):
-        # Extract partition for client with node_id
-        partition = fds.load_partition(node_id=node_id, split="train")
+    for partition_id in tqdm(range(args.pool_size), desc="Extracting client partition"):
+        # Extract partition for client with partition_id
+        partition = fds.load_partition(partition_id=partition_id, split="train")
         partition.set_format("numpy")
 
         if args.centralised_eval_client:
diff --git a/examples/xgboost-comprehensive/utils.py b/examples/xgboost-comprehensive/utils.py
index 102587f4266d..abc100da1ade 100644
--- a/examples/xgboost-comprehensive/utils.py
+++ b/examples/xgboost-comprehensive/utils.py
@@ -37,10 +37,10 @@ def client_args_parser():
         help="Partitioner types.",
     )
     parser.add_argument(
-        "--node-id",
+        "--partition-id",
         default=0,
         type=int,
-        help="Node ID used for the current client.",
+        help="Partition ID used for the current client.",
     )
     parser.add_argument(
         "--seed", default=42, type=int, help="Seed used for train/test splitting."
diff --git a/examples/xgboost-quickstart/README.md b/examples/xgboost-quickstart/README.md
index cd99cd4c2895..72dde5706e8d 100644
--- a/examples/xgboost-quickstart/README.md
+++ b/examples/xgboost-quickstart/README.md
@@ -67,13 +67,13 @@ To do so simply open two more terminal windows and run the following commands.
 Start client 1 in the first terminal:
 
 ```shell
-python3 client.py --node-id=0
+python3 client.py --partition-id=0
 ```
 
 Start client 2 in the second terminal:
 
 ```shell
-python3 client.py --node-id=1
+python3 client.py --partition-id=1
 ```
 
 You will see that XGBoost is starting a federated training.
diff --git a/examples/xgboost-quickstart/client.py b/examples/xgboost-quickstart/client.py
index 62e8a441bae1..6ac23ae15148 100644
--- a/examples/xgboost-quickstart/client.py
+++ b/examples/xgboost-quickstart/client.py
@@ -24,13 +24,13 @@
 
 warnings.filterwarnings("ignore", category=UserWarning)
 
-# Define arguments parser for the client/node ID.
+# Define arguments parser for the client/partition ID.
 parser = argparse.ArgumentParser()
 parser.add_argument(
-    "--node-id",
+    "--partition-id",
     default=0,
     type=int,
-    help="Node ID used for the current client.",
+    help="Partition ID used for the current client.",
 )
 args = parser.parse_args()
 
@@ -61,9 +61,9 @@ def transform_dataset_to_dmatrix(data: Union[Dataset, DatasetDict]) -> xgb.core.
 partitioner = IidPartitioner(num_partitions=30)
 fds = FederatedDataset(dataset="jxie/higgs", partitioners={"train": partitioner})
 
-# Load the partition for this `node_id`
+# Load the partition for this `partition_id`
 log(INFO, "Loading partition...")
-partition = fds.load_partition(node_id=args.node_id, split="train")
+partition = fds.load_partition(partition_id=args.partition_id, split="train")
 partition.set_format("numpy")
 
 # Train/test splitting
diff --git a/examples/xgboost-quickstart/run.sh b/examples/xgboost-quickstart/run.sh
index 6287145bfb5f..b35af58222ab 100755
--- a/examples/xgboost-quickstart/run.sh
+++ b/examples/xgboost-quickstart/run.sh
@@ -8,7 +8,7 @@ sleep 5  # Sleep for 5s to give the server enough time to start
 
 for i in `seq 0 1`; do
     echo "Starting client $i"
-    python3 client.py --node-id=$i &
+    python3 client.py --partition-id=$i &
 done
 
 # Enable CTRL+C to stop all background processes

From 256548ea17fc443436ed30cd34f92554ec009999 Mon Sep 17 00:00:00 2001
From: mohammadnaseri <mohammad@flower.dev>
Date: Wed, 28 Feb 2024 11:20:11 +0000
Subject: [PATCH 071/102] Update cryptography version (#3028)

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 6bd5c74f29a9..b52be36b7ff5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -65,7 +65,7 @@ python = "^3.8"
 numpy = "^1.21.0"
 grpcio = "^1.60.0"
 protobuf = "^4.25.2"
-cryptography = "^41.0.2"
+cryptography = "^42.0.4"
 pycryptodome = "^3.18.0"
 iterators = "^0.0.2"
 typer = { version = "^0.9.0", extras=["all"] }

From d663cd46cdca6cc67664ac3bb9fbe6465e93a062 Mon Sep 17 00:00:00 2001
From: Heng Pan <pan@flower.ai>
Date: Wed, 28 Feb 2024 12:12:12 +0000
Subject: [PATCH 072/102] Add `Workflow` (#3017)

Co-authored-by: Daniel J. Beutel <daniel@flower.ai>
---
 examples/app-pytorch/README.md                |  16 +-
 examples/app-pytorch/server_workflow.py       |  55 +++
 src/py/flwr/server/__init__.py                |   4 +
 src/py/flwr/server/compat/__init__.py         |   2 +
 src/py/flwr/server/compat/app.py              |  95 +----
 src/py/flwr/server/compat/app_test.py         |  84 -----
 src/py/flwr/server/compat/app_utils.py        | 102 +++++
 src/py/flwr/server/compat/app_utils_test.py   |  62 +++
 src/py/flwr/server/compat/legacy_context.py   |  55 +++
 src/py/flwr/server/run_serverapp.py           |   2 +-
 src/py/flwr/server/typing.py                  |   1 +
 src/py/flwr/server/workflow/__init__.py       |  22 ++
 .../flwr/server/workflow/default_workflows.py | 357 ++++++++++++++++++
 13 files changed, 680 insertions(+), 177 deletions(-)
 create mode 100644 examples/app-pytorch/server_workflow.py
 delete mode 100644 src/py/flwr/server/compat/app_test.py
 create mode 100644 src/py/flwr/server/compat/app_utils.py
 create mode 100644 src/py/flwr/server/compat/app_utils_test.py
 create mode 100644 src/py/flwr/server/compat/legacy_context.py
 create mode 100644 src/py/flwr/server/workflow/__init__.py
 create mode 100644 src/py/flwr/server/workflow/default_workflows.py

diff --git a/examples/app-pytorch/README.md b/examples/app-pytorch/README.md
index bcc7aa045e26..de1b6fdbb819 100644
--- a/examples/app-pytorch/README.md
+++ b/examples/app-pytorch/README.md
@@ -12,10 +12,12 @@ Let's assume the following project structure:
 ```bash
 $ tree .
 .
-├── client.py         # <-- contains `ClientApp`
-├── server.py         # <-- contains `ServerApp`
-├── task.py           # <-- task-specific code (model, data)
-└── requirements.txt  # <-- dependencies
+├── client.py           # <-- contains `ClientApp`
+├── server.py           # <-- contains `ServerApp`
+├── server_workflow.py  # <-- contains `ServerApp` with workflow
+├── server_custom.py    # <-- contains `ServerApp` with custom main function
+├── task.py             # <-- task-specific code (model, data)
+└── requirements.txt    # <-- dependencies
 ```
 
 ## Install dependencies
@@ -52,6 +54,12 @@ With both the long-running server (SuperLink) and two clients (SuperNode) up and
 flower-server-app server:app --insecure
 ```
 
+Or, to try the workflow example, run:
+
+```bash
+flower-server-app server_workflow:app --insecure
+```
+
 Or, to try the custom server function example, run:
 
 ```bash
diff --git a/examples/app-pytorch/server_workflow.py b/examples/app-pytorch/server_workflow.py
new file mode 100644
index 000000000000..920e266c99e9
--- /dev/null
+++ b/examples/app-pytorch/server_workflow.py
@@ -0,0 +1,55 @@
+from typing import List, Tuple
+
+import flwr as fl
+from flwr.common import Context, Metrics
+from flwr.server import Driver, LegacyContext
+
+
+# Define metric aggregation function
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+    examples = [num_examples for num_examples, _ in metrics]
+
+    # Multiply accuracy of each client by number of examples used
+    train_losses = [num_examples * m["train_loss"] for num_examples, m in metrics]
+    train_accuracies = [
+        num_examples * m["train_accuracy"] for num_examples, m in metrics
+    ]
+    val_losses = [num_examples * m["val_loss"] for num_examples, m in metrics]
+    val_accuracies = [num_examples * m["val_accuracy"] for num_examples, m in metrics]
+
+    # Aggregate and return custom metric (weighted average)
+    return {
+        "train_loss": sum(train_losses) / sum(examples),
+        "train_accuracy": sum(train_accuracies) / sum(examples),
+        "val_loss": sum(val_losses) / sum(examples),
+        "val_accuracy": sum(val_accuracies) / sum(examples),
+    }
+
+
+# Define strategy
+strategy = fl.server.strategy.FedAvg(
+    fraction_fit=1.0,  # Select all available clients
+    fraction_evaluate=0.0,  # Disable evaluation
+    min_available_clients=2,
+    fit_metrics_aggregation_fn=weighted_average,
+)
+
+
+# Run via `flower-server-app server_workflow:app`
+app = fl.server.ServerApp()
+
+
+@app.main()
+def main(driver: Driver, context: Context) -> None:
+    # Construct the LegacyContext
+    context = LegacyContext(
+        state=context.state,
+        config=fl.server.ServerConfig(num_rounds=3),
+        strategy=strategy,
+    )
+
+    # Create the workflow
+    workflow = fl.server.workflow.DefaultWorkflow()
+
+    # Execute
+    workflow(driver, context)
diff --git a/src/py/flwr/server/__init__.py b/src/py/flwr/server/__init__.py
index 969bea96d1fe..633bd668b520 100644
--- a/src/py/flwr/server/__init__.py
+++ b/src/py/flwr/server/__init__.py
@@ -16,12 +16,14 @@
 
 
 from . import strategy
+from . import workflow as workflow
 from .app import run_driver_api as run_driver_api
 from .app import run_fleet_api as run_fleet_api
 from .app import run_superlink as run_superlink
 from .app import start_server as start_server
 from .client_manager import ClientManager as ClientManager
 from .client_manager import SimpleClientManager as SimpleClientManager
+from .compat import LegacyContext as LegacyContext
 from .compat import start_driver as start_driver
 from .driver import Driver as Driver
 from .history import History as History
@@ -34,6 +36,7 @@
     "ClientManager",
     "Driver",
     "History",
+    "LegacyContext",
     "run_driver_api",
     "run_fleet_api",
     "run_server_app",
@@ -45,4 +48,5 @@
     "start_driver",
     "start_server",
     "strategy",
+    "workflow",
 ]
diff --git a/src/py/flwr/server/compat/__init__.py b/src/py/flwr/server/compat/__init__.py
index 3a0c2b4e83a0..7bae196ddb65 100644
--- a/src/py/flwr/server/compat/__init__.py
+++ b/src/py/flwr/server/compat/__init__.py
@@ -16,7 +16,9 @@
 
 
 from .app import start_driver as start_driver
+from .legacy_context import LegacyContext as LegacyContext
 
 __all__ = [
+    "LegacyContext",
     "start_driver",
 ]
diff --git a/src/py/flwr/server/compat/app.py b/src/py/flwr/server/compat/app.py
index 2e441db08668..203317a3e348 100644
--- a/src/py/flwr/server/compat/app.py
+++ b/src/py/flwr/server/compat/app.py
@@ -16,16 +16,13 @@
 
 
 import sys
-import threading
-import time
 from logging import INFO
 from pathlib import Path
-from typing import Dict, Optional, Union
+from typing import Optional, Union
 
 from flwr.common import EventType, event
 from flwr.common.address import parse_address
 from flwr.common.logger import log, warn_deprecated_feature
-from flwr.proto import driver_pb2  # pylint: disable=E0611
 from flwr.server.client_manager import ClientManager
 from flwr.server.history import History
 from flwr.server.server import Server, init_defaults, run_fl
@@ -33,8 +30,7 @@
 from flwr.server.strategy import Strategy
 
 from ..driver import Driver
-from ..driver.grpc_driver import GrpcDriver
-from .driver_client_proxy import DriverClientProxy
+from .app_utils import start_update_client_manager_thread
 
 DEFAULT_SERVER_ADDRESS_DRIVER = "[::]:9091"
 
@@ -104,11 +100,7 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
     """
     event(EventType.START_DRIVER_ENTER)
 
-    if driver:
-        # pylint: disable=protected-access
-        grpc_driver, _ = driver._get_grpc_driver_and_run_id()
-        # pylint: enable=protected-access
-    else:
+    if driver is None:
         # Not passing a `Driver` object is deprecated
         warn_deprecated_feature("start_driver")
 
@@ -122,12 +114,9 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
         # Create the Driver
         if isinstance(root_certificates, str):
             root_certificates = Path(root_certificates).read_bytes()
-        grpc_driver = GrpcDriver(
+        driver = Driver(
             driver_service_address=address, root_certificates=root_certificates
         )
-        grpc_driver.connect()
-
-    lock = threading.Lock()
 
     # Initialize the Driver API server and config
     initialized_server, initialized_config = init_defaults(
@@ -142,18 +131,10 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
         initialized_config,
     )
 
-    f_stop = threading.Event()
     # Start the thread updating nodes
-    thread = threading.Thread(
-        target=update_client_manager,
-        args=(
-            grpc_driver,
-            initialized_server.client_manager(),
-            lock,
-            f_stop,
-        ),
+    thread, f_stop = start_update_client_manager_thread(
+        driver, initialized_server.client_manager()
     )
-    thread.start()
 
     # Start training
     hist = run_fl(
@@ -164,72 +145,10 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
     f_stop.set()
 
     # Stop the Driver API server and the thread
-    with lock:
-        if driver:
-            del driver
-        else:
-            grpc_driver.disconnect()
+    del driver
 
     thread.join()
 
     event(EventType.START_SERVER_LEAVE)
 
     return hist
-
-
-def update_client_manager(
-    driver: GrpcDriver,
-    client_manager: ClientManager,
-    lock: threading.Lock,
-    f_stop: threading.Event,
-) -> None:
-    """Update the nodes list in the client manager.
-
-    This function periodically communicates with the associated driver to get all
-    node_ids. Each node_id is then converted into a `DriverClientProxy` instance
-    and stored in the `registered_nodes` dictionary with node_id as key.
-
-    New nodes will be added to the ClientManager via `client_manager.register()`,
-    and dead nodes will be removed from the ClientManager via
-    `client_manager.unregister()`.
-    """
-    # Request for run_id
-    run_id = driver.create_run(
-        driver_pb2.CreateRunRequest()  # pylint: disable=E1101
-    ).run_id
-
-    # Loop until the driver is disconnected
-    registered_nodes: Dict[int, DriverClientProxy] = {}
-    while not f_stop.is_set():
-        with lock:
-            # End the while loop if the driver is disconnected
-            if driver.stub is None:
-                break
-            get_nodes_res = driver.get_nodes(
-                req=driver_pb2.GetNodesRequest(run_id=run_id)  # pylint: disable=E1101
-            )
-        all_node_ids = {node.node_id for node in get_nodes_res.nodes}
-        dead_nodes = set(registered_nodes).difference(all_node_ids)
-        new_nodes = all_node_ids.difference(registered_nodes)
-
-        # Unregister dead nodes
-        for node_id in dead_nodes:
-            client_proxy = registered_nodes[node_id]
-            client_manager.unregister(client_proxy)
-            del registered_nodes[node_id]
-
-        # Register new nodes
-        for node_id in new_nodes:
-            client_proxy = DriverClientProxy(
-                node_id=node_id,
-                driver=driver,
-                anonymous=False,
-                run_id=run_id,
-            )
-            if client_manager.register(client_proxy):
-                registered_nodes[node_id] = client_proxy
-            else:
-                raise RuntimeError("Could not register node.")
-
-        # Sleep for 3 seconds
-        time.sleep(3)
diff --git a/src/py/flwr/server/compat/app_test.py b/src/py/flwr/server/compat/app_test.py
deleted file mode 100644
index 77eeda2848c1..000000000000
--- a/src/py/flwr/server/compat/app_test.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# Copyright 2022 Flower Labs GmbH. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Flower Driver app tests."""
-
-
-import threading
-import time
-import unittest
-from unittest.mock import MagicMock
-
-from flwr.proto.driver_pb2 import (  # pylint: disable=E0611
-    CreateRunResponse,
-    GetNodesResponse,
-)
-from flwr.proto.node_pb2 import Node  # pylint: disable=E0611
-from flwr.server.client_manager import SimpleClientManager
-
-from .app import update_client_manager
-
-
-class TestClientManagerWithDriver(unittest.TestCase):
-    """Tests for ClientManager.
-
-    Considering multi-threading, all tests assume that the `update_client_manager()`
-    updates the ClientManager every 3 seconds.
-    """
-
-    def test_simple_client_manager_update(self) -> None:
-        """Tests if the node update works correctly."""
-        # Prepare
-        expected_nodes = [Node(node_id=i, anonymous=False) for i in range(100)]
-        expected_updated_nodes = [
-            Node(node_id=i, anonymous=False) for i in range(80, 120)
-        ]
-        driver = MagicMock()
-        driver.stub = "driver stub"
-        driver.create_run.return_value = CreateRunResponse(run_id=1)
-        driver.get_nodes.return_value = GetNodesResponse(nodes=expected_nodes)
-        client_manager = SimpleClientManager()
-        lock = threading.Lock()
-        f_stop = threading.Event()
-        # Execute
-        thread = threading.Thread(
-            target=update_client_manager,
-            args=(driver, client_manager, lock, f_stop),
-            daemon=True,
-        )
-        thread.start()
-        # Wait until all nodes are registered via `client_manager.sample()`
-        client_manager.sample(len(expected_nodes))
-        # 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_nodes.return_value = GetNodesResponse(nodes=expected_updated_nodes)
-        while True:
-            with lock:
-                if len(client_manager.all()) == len(expected_updated_nodes):
-                    break
-            time.sleep(1.3)
-        # Retrieve all nodes in `client_manager`
-        updated_node_ids = {proxy.node_id for proxy in client_manager.all().values()}
-        # Simulate `driver.disconnect()`
-        driver.stub = None
-
-        # Assert
-        driver.create_run.assert_called_once()
-        assert node_ids == {node.node_id for node in expected_nodes}
-        assert updated_node_ids == {node.node_id for node in expected_updated_nodes}
-
-        f_stop.set()
-        # Exit
-        thread.join()
diff --git a/src/py/flwr/server/compat/app_utils.py b/src/py/flwr/server/compat/app_utils.py
new file mode 100644
index 000000000000..696ec1132c4a
--- /dev/null
+++ b/src/py/flwr/server/compat/app_utils.py
@@ -0,0 +1,102 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utility functions for the `start_driver`."""
+
+
+import threading
+import time
+from typing import Dict, Tuple
+
+from ..client_manager import ClientManager
+from ..compat.driver_client_proxy import DriverClientProxy
+from ..driver import Driver
+
+
+def start_update_client_manager_thread(
+    driver: Driver,
+    client_manager: ClientManager,
+) -> Tuple[threading.Thread, threading.Event]:
+    """Periodically update the nodes list in the client manager in a thread.
+
+    This function starts a thread that periodically uses the associated driver to
+    get all node_ids. Each node_id is then converted into a `DriverClientProxy`
+    instance and stored in the `registered_nodes` dictionary with node_id as key.
+
+    New nodes will be added to the ClientManager via `client_manager.register()`,
+    and dead nodes will be removed from the ClientManager via
+    `client_manager.unregister()`.
+
+    Parameters
+    ----------
+    driver : Driver
+        The Driver object to use.
+    client_manager : ClientManager
+        The ClientManager object to be updated.
+
+    Returns
+    -------
+    threading.Thread
+        A thread that updates the ClientManager and handles the stop event.
+    threading.Event
+        An event that, when set, signals the thread to stop.
+    """
+    f_stop = threading.Event()
+    thread = threading.Thread(
+        target=_update_client_manager,
+        args=(
+            driver,
+            client_manager,
+            f_stop,
+        ),
+    )
+    thread.start()
+
+    return thread, f_stop
+
+
+def _update_client_manager(
+    driver: Driver,
+    client_manager: ClientManager,
+    f_stop: threading.Event,
+) -> None:
+    """Update the nodes list in the client manager."""
+    # Loop until the driver is disconnected
+    registered_nodes: Dict[int, DriverClientProxy] = {}
+    while not f_stop.is_set():
+        all_node_ids = set(driver.get_node_ids())
+        dead_nodes = set(registered_nodes).difference(all_node_ids)
+        new_nodes = all_node_ids.difference(registered_nodes)
+
+        # Unregister dead nodes
+        for node_id in dead_nodes:
+            client_proxy = registered_nodes[node_id]
+            client_manager.unregister(client_proxy)
+            del registered_nodes[node_id]
+
+        # Register new nodes
+        for node_id in new_nodes:
+            client_proxy = DriverClientProxy(
+                node_id=node_id,
+                driver=driver.grpc_driver,  # type: ignore
+                anonymous=False,
+                run_id=driver.run_id,  # type: ignore
+            )
+            if client_manager.register(client_proxy):
+                registered_nodes[node_id] = client_proxy
+            else:
+                raise RuntimeError("Could not register node.")
+
+        # Sleep for 3 seconds
+        time.sleep(3)
diff --git a/src/py/flwr/server/compat/app_utils_test.py b/src/py/flwr/server/compat/app_utils_test.py
new file mode 100644
index 000000000000..7e47e6eaaf32
--- /dev/null
+++ b/src/py/flwr/server/compat/app_utils_test.py
@@ -0,0 +1,62 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for utility functions for the `start_driver`."""
+
+
+import time
+import unittest
+from unittest.mock import Mock, patch
+
+from ..client_manager import SimpleClientManager
+from .app_utils import start_update_client_manager_thread
+
+
+class TestUtils(unittest.TestCase):
+    """Tests for utility functions."""
+
+    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()
+        driver.grpc_driver = Mock()
+        driver.run_id = 123
+        driver.get_node_ids.return_value = expected_node_ids
+        client_manager = SimpleClientManager()
+
+        # 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()
+
+        # Assert
+        assert node_ids == set(expected_node_ids)
+        assert updated_node_ids == set(updated_expected_node_ids)
+
+        # Exit
+        thread.join()
diff --git a/src/py/flwr/server/compat/legacy_context.py b/src/py/flwr/server/compat/legacy_context.py
new file mode 100644
index 000000000000..0b00c98bb16d
--- /dev/null
+++ b/src/py/flwr/server/compat/legacy_context.py
@@ -0,0 +1,55 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Legacy Context."""
+
+
+from dataclasses import dataclass
+from typing import Optional
+
+from flwr.common import Context, RecordSet
+
+from ..client_manager import ClientManager, SimpleClientManager
+from ..history import History
+from ..server_config import ServerConfig
+from ..strategy import FedAvg, Strategy
+
+
+@dataclass
+class LegacyContext(Context):
+    """Legacy Context."""
+
+    config: ServerConfig
+    strategy: Strategy
+    client_manager: ClientManager
+    history: History
+
+    def __init__(
+        self,
+        state: RecordSet,
+        config: Optional[ServerConfig] = None,
+        strategy: Optional[Strategy] = None,
+        client_manager: Optional[ClientManager] = None,
+    ) -> None:
+        if config is None:
+            config = ServerConfig()
+        if strategy is None:
+            strategy = FedAvg()
+        if client_manager is None:
+            client_manager = SimpleClientManager()
+        self.config = config
+        self.strategy = strategy
+        self.client_manager = client_manager
+        self.history = History()
+        super().__init__(state)
diff --git a/src/py/flwr/server/run_serverapp.py b/src/py/flwr/server/run_serverapp.py
index e7205ebd1444..19fd16fb0c1a 100644
--- a/src/py/flwr/server/run_serverapp.py
+++ b/src/py/flwr/server/run_serverapp.py
@@ -107,7 +107,7 @@ def run_server_app() -> None:
     run(server_app_attr, driver, server_app_dir)
 
     # Clean up
-    del driver
+    driver.__del__()  # pylint: disable=unnecessary-dunder-call
 
     event(EventType.RUN_SERVER_APP_LEAVE)
 
diff --git a/src/py/flwr/server/typing.py b/src/py/flwr/server/typing.py
index fa84322bc785..01143af74392 100644
--- a/src/py/flwr/server/typing.py
+++ b/src/py/flwr/server/typing.py
@@ -22,3 +22,4 @@
 from .driver import Driver
 
 ServerAppCallable = Callable[[Driver, Context], None]
+Workflow = Callable[[Driver, Context], None]
diff --git a/src/py/flwr/server/workflow/__init__.py b/src/py/flwr/server/workflow/__init__.py
new file mode 100644
index 000000000000..098b0dbfb92f
--- /dev/null
+++ b/src/py/flwr/server/workflow/__init__.py
@@ -0,0 +1,22 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Workflows."""
+
+
+from .default_workflows import DefaultWorkflow
+
+__all__ = [
+    "DefaultWorkflow",
+]
diff --git a/src/py/flwr/server/workflow/default_workflows.py b/src/py/flwr/server/workflow/default_workflows.py
new file mode 100644
index 000000000000..5c6c1e2d114e
--- /dev/null
+++ b/src/py/flwr/server/workflow/default_workflows.py
@@ -0,0 +1,357 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Legacy default workflows."""
+
+
+import timeit
+from logging import DEBUG, INFO
+from typing import Optional, cast
+
+import flwr.common.recordset_compat as compat
+from flwr.common import ConfigsRecord, Context, GetParametersIns, log
+from flwr.common.constant import (
+    MESSAGE_TYPE_EVALUATE,
+    MESSAGE_TYPE_FIT,
+    MESSAGE_TYPE_GET_PARAMETERS,
+)
+
+from ..compat.app_utils import start_update_client_manager_thread
+from ..compat.legacy_context import LegacyContext
+from ..driver import Driver
+from ..typing import Workflow
+
+KEY_CURRENT_ROUND = "current_round"
+KEY_START_TIME = "start_time"
+CONFIGS_RECORD_KEY = "config"
+PARAMS_RECORD_KEY = "parameters"
+
+
+class DefaultWorkflow:
+    """Default workflow in Flower."""
+
+    def __init__(
+        self,
+        fit_workflow: Optional[Workflow] = None,
+        evaluate_workflow: Optional[Workflow] = None,
+    ) -> None:
+        if fit_workflow is None:
+            fit_workflow = default_fit_workflow
+        if evaluate_workflow is None:
+            evaluate_workflow = default_evaluate_workflow
+        self.fit_workflow: Workflow = fit_workflow
+        self.evaluate_workflow: Workflow = evaluate_workflow
+
+    def __call__(self, driver: Driver, context: Context) -> None:
+        """Execute the workflow."""
+        if not isinstance(context, LegacyContext):
+            raise TypeError(
+                f"Expect a LegacyContext, but get {type(context).__name__}."
+            )
+
+        # Start the thread updating nodes
+        thread, f_stop = start_update_client_manager_thread(
+            driver, context.client_manager
+        )
+
+        # Initialize parameters
+        default_init_params_workflow(driver, context)
+
+        # Run federated learning for num_rounds
+        log(INFO, "FL starting")
+        start_time = timeit.default_timer()
+        cfg = ConfigsRecord()
+        cfg[KEY_START_TIME] = start_time
+        context.state.configs_records[CONFIGS_RECORD_KEY] = cfg
+
+        for current_round in range(1, context.config.num_rounds + 1):
+            cfg[KEY_CURRENT_ROUND] = current_round
+
+            # Fit round
+            self.fit_workflow(driver, context)
+
+            # Centralized evaluation
+            default_centralized_evaluation_workflow(driver, context)
+
+            # Evaluate round
+            self.evaluate_workflow(driver, context)
+
+        # Bookkeeping
+        end_time = timeit.default_timer()
+        elapsed = end_time - start_time
+        log(INFO, "FL finished in %s", elapsed)
+
+        # Log results
+        hist = context.history
+        log(INFO, "app_fit: losses_distributed %s", str(hist.losses_distributed))
+        log(
+            INFO,
+            "app_fit: metrics_distributed_fit %s",
+            str(hist.metrics_distributed_fit),
+        )
+        log(INFO, "app_fit: metrics_distributed %s", str(hist.metrics_distributed))
+        log(INFO, "app_fit: losses_centralized %s", str(hist.losses_centralized))
+        log(INFO, "app_fit: metrics_centralized %s", str(hist.metrics_centralized))
+
+        # Terminate the thread
+        f_stop.set()
+        del driver
+        thread.join()
+
+
+def default_init_params_workflow(driver: Driver, context: Context) -> None:
+    """Execute the default workflow for parameters initialization."""
+    if not isinstance(context, LegacyContext):
+        raise TypeError(f"Expect a LegacyContext, but get {type(context).__name__}.")
+
+    log(INFO, "Initializing global parameters")
+    parameters = context.strategy.initialize_parameters(
+        client_manager=context.client_manager
+    )
+    if parameters is not None:
+        log(INFO, "Using initial parameters provided by strategy")
+        paramsrecord = compat.parameters_to_parametersrecord(
+            parameters, keep_input=True
+        )
+    else:
+        # Get initial parameters from one of the clients
+        log(INFO, "Requesting initial parameters from one random client")
+        random_client = context.client_manager.sample(1)[0]
+        # Send GetParametersIns and get the response
+        content = compat.getparametersins_to_recordset(GetParametersIns({}))
+        messages = driver.send_and_receive(
+            [
+                driver.create_message(
+                    content=content,
+                    message_type=MESSAGE_TYPE_GET_PARAMETERS,
+                    dst_node_id=random_client.node_id,
+                    group_id="",
+                    ttl="",
+                )
+            ]
+        )
+        log(INFO, "Received initial parameters from one random client")
+        msg = list(messages)[0]
+        paramsrecord = next(iter(msg.content.parameters_records.values()))
+
+    context.state.parameters_records[PARAMS_RECORD_KEY] = paramsrecord
+
+    # Evaluate initial parameters
+    log(INFO, "Evaluating initial parameters")
+    parameters = compat.parametersrecord_to_parameters(paramsrecord, keep_input=True)
+    res = context.strategy.evaluate(0, parameters=parameters)
+    if res is not None:
+        log(
+            INFO,
+            "initial parameters (loss, other metrics): %s, %s",
+            res[0],
+            res[1],
+        )
+        context.history.add_loss_centralized(server_round=0, loss=res[0])
+        context.history.add_metrics_centralized(server_round=0, metrics=res[1])
+
+
+def default_centralized_evaluation_workflow(_: Driver, context: Context) -> None:
+    """Execute the default workflow for centralized evaluation."""
+    if not isinstance(context, LegacyContext):
+        raise TypeError(f"Expect a LegacyContext, but get {type(context).__name__}.")
+
+    # Retrieve current_round and start_time from the context
+    cfg = context.state.configs_records[CONFIGS_RECORD_KEY]
+    current_round = cast(int, cfg[KEY_CURRENT_ROUND])
+    start_time = cast(float, cfg[KEY_START_TIME])
+
+    # Centralized evaluation
+    parameters = compat.parametersrecord_to_parameters(
+        record=context.state.parameters_records[PARAMS_RECORD_KEY],
+        keep_input=True,
+    )
+    res_cen = context.strategy.evaluate(current_round, parameters=parameters)
+    if res_cen is not None:
+        loss_cen, metrics_cen = res_cen
+        log(
+            INFO,
+            "fit progress: (%s, %s, %s, %s)",
+            current_round,
+            loss_cen,
+            metrics_cen,
+            timeit.default_timer() - start_time,
+        )
+        context.history.add_loss_centralized(server_round=current_round, loss=loss_cen)
+        context.history.add_metrics_centralized(
+            server_round=current_round, metrics=metrics_cen
+        )
+
+
+def default_fit_workflow(driver: Driver, context: Context) -> None:
+    """Execute the default workflow for a single fit round."""
+    if not isinstance(context, LegacyContext):
+        raise TypeError(f"Expect a LegacyContext, but get {type(context).__name__}.")
+
+    # Get current_round and parameters
+    cfg = context.state.configs_records[CONFIGS_RECORD_KEY]
+    current_round = cast(int, cfg[KEY_CURRENT_ROUND])
+    parametersrecord = context.state.parameters_records[PARAMS_RECORD_KEY]
+    parameters = compat.parametersrecord_to_parameters(
+        parametersrecord, keep_input=True
+    )
+
+    # Get clients and their respective instructions from strategy
+    client_instructions = context.strategy.configure_fit(
+        server_round=current_round,
+        parameters=parameters,
+        client_manager=context.client_manager,
+    )
+
+    if not client_instructions:
+        log(INFO, "fit_round %s: no clients selected, cancel", current_round)
+        return
+    log(
+        DEBUG,
+        "fit_round %s: strategy sampled %s clients (out of %s)",
+        current_round,
+        len(client_instructions),
+        context.client_manager.num_available(),
+    )
+
+    # Build dictionary mapping node_id to ClientProxy
+    node_id_to_proxy = {proxy.node_id: proxy for proxy, _ in client_instructions}
+
+    # Build out messages
+    out_messages = [
+        driver.create_message(
+            content=compat.fitins_to_recordset(fitins, True),
+            message_type=MESSAGE_TYPE_FIT,
+            dst_node_id=proxy.node_id,
+            group_id="",
+            ttl="",
+        )
+        for proxy, fitins in client_instructions
+    ]
+
+    # Send instructions to clients and
+    # collect `fit` results from all clients participating in this round
+    messages = list(driver.send_and_receive(out_messages))
+    del out_messages
+
+    # No exception/failure handling currently
+    log(
+        DEBUG,
+        "fit_round %s received %s results and %s failures",
+        current_round,
+        len(messages),
+        0,
+    )
+
+    # Aggregate training results
+    results = [
+        (
+            node_id_to_proxy[msg.metadata.src_node_id],
+            compat.recordset_to_fitres(msg.content, False),
+        )
+        for msg in messages
+    ]
+    aggregated_result = context.strategy.aggregate_fit(current_round, results, [])
+    parameters_aggregated, metrics_aggregated = aggregated_result
+
+    # Update the parameters and write history
+    if parameters_aggregated:
+        paramsrecord = compat.parameters_to_parametersrecord(
+            parameters_aggregated, True
+        )
+        context.state.parameters_records[PARAMS_RECORD_KEY] = paramsrecord
+        context.history.add_metrics_distributed_fit(
+            server_round=current_round, metrics=metrics_aggregated
+        )
+
+
+def default_evaluate_workflow(driver: Driver, context: Context) -> None:
+    """Execute the default workflow for a single evaluate round."""
+    if not isinstance(context, LegacyContext):
+        raise TypeError(f"Expect a LegacyContext, but get {type(context).__name__}.")
+
+    # Get current_round and parameters
+    cfg = context.state.configs_records[CONFIGS_RECORD_KEY]
+    current_round = cast(int, cfg[KEY_CURRENT_ROUND])
+    parametersrecord = context.state.parameters_records[PARAMS_RECORD_KEY]
+    parameters = compat.parametersrecord_to_parameters(
+        parametersrecord, keep_input=True
+    )
+
+    # Get clients and their respective instructions from strategy
+    client_instructions = context.strategy.configure_evaluate(
+        server_round=current_round,
+        parameters=parameters,
+        client_manager=context.client_manager,
+    )
+    if not client_instructions:
+        log(INFO, "evaluate_round %s: no clients selected, cancel", current_round)
+        return
+    log(
+        DEBUG,
+        "evaluate_round %s: strategy sampled %s clients (out of %s)",
+        current_round,
+        len(client_instructions),
+        context.client_manager.num_available(),
+    )
+
+    # Build dictionary mapping node_id to ClientProxy
+    node_id_to_proxy = {proxy.node_id: proxy for proxy, _ in client_instructions}
+
+    # Build out messages
+    out_messages = [
+        driver.create_message(
+            content=compat.evaluateins_to_recordset(evalins, True),
+            message_type=MESSAGE_TYPE_EVALUATE,
+            dst_node_id=proxy.node_id,
+            group_id="",
+            ttl="",
+        )
+        for proxy, evalins in client_instructions
+    ]
+
+    # Send instructions to clients and
+    # collect `evaluate` results from all clients participating in this round
+    messages = list(driver.send_and_receive(out_messages))
+    del out_messages
+
+    # No exception/failure handling currently
+    log(
+        DEBUG,
+        "evaluate_round %s received %s results and %s failures",
+        current_round,
+        len(messages),
+        0,
+    )
+
+    # Aggregate the evaluation results
+    results = [
+        (
+            node_id_to_proxy[msg.metadata.src_node_id],
+            compat.recordset_to_evaluateres(msg.content),
+        )
+        for msg in messages
+    ]
+    aggregated_result = context.strategy.aggregate_evaluate(current_round, results, [])
+
+    loss_aggregated, metrics_aggregated = aggregated_result
+
+    # Write history
+    if loss_aggregated is not None:
+        context.history.add_loss_distributed(
+            server_round=current_round, loss=loss_aggregated
+        )
+        context.history.add_metrics_distributed(
+            server_round=current_round, metrics=metrics_aggregated
+        )

From 2e7c5c93992c2261bf36a1720c3a10712cf8302e Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Wed, 28 Feb 2024 14:31:05 +0100
Subject: [PATCH 073/102] Add `group_id` to `ClientProxy` (#2691)

Co-authored-by: jafermarq <javier@flower.dev>
Co-authored-by: Daniel J. Beutel <daniel@flower.ai>
---
 src/py/flwr/server/client_proxy.py            |  5 +++
 src/py/flwr/server/client_proxy_test.py       |  5 +++
 .../flwr/server/compat/driver_client_proxy.py | 32 +++++++++++------
 .../server/compat/driver_client_proxy_test.py | 18 +++++-----
 src/py/flwr/server/server.py                  | 26 +++++++++-----
 src/py/flwr/server/server_test.py             | 35 ++++++++++++------
 .../fleet/grpc_bidi/grpc_client_proxy.py      |  5 +++
 .../fleet/grpc_bidi/grpc_client_proxy_test.py | 10 +++---
 .../ray_transport/ray_client_proxy.py         | 36 ++++++++++++++-----
 .../ray_transport/ray_client_proxy_test.py    |  4 ++-
 10 files changed, 126 insertions(+), 50 deletions(-)

diff --git a/src/py/flwr/server/client_proxy.py b/src/py/flwr/server/client_proxy.py
index 7d0547be304b..951e4ae992da 100644
--- a/src/py/flwr/server/client_proxy.py
+++ b/src/py/flwr/server/client_proxy.py
@@ -47,6 +47,7 @@ def get_properties(
         self,
         ins: GetPropertiesIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> GetPropertiesRes:
         """Return the client's properties."""
 
@@ -55,6 +56,7 @@ def get_parameters(
         self,
         ins: GetParametersIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> GetParametersRes:
         """Return the current local model parameters."""
 
@@ -63,6 +65,7 @@ def fit(
         self,
         ins: FitIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> FitRes:
         """Refine the provided parameters using the locally held dataset."""
 
@@ -71,6 +74,7 @@ def evaluate(
         self,
         ins: EvaluateIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> EvaluateRes:
         """Evaluate the provided parameters using the locally held dataset."""
 
@@ -79,5 +83,6 @@ def reconnect(
         self,
         ins: ReconnectIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> DisconnectRes:
         """Disconnect and (optionally) reconnect later."""
diff --git a/src/py/flwr/server/client_proxy_test.py b/src/py/flwr/server/client_proxy_test.py
index 266e4cbeb266..685698558e3a 100644
--- a/src/py/flwr/server/client_proxy_test.py
+++ b/src/py/flwr/server/client_proxy_test.py
@@ -42,6 +42,7 @@ def get_properties(
         self,
         ins: GetPropertiesIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> GetPropertiesRes:
         """Return the client's properties."""
         return GetPropertiesRes(status=Status(code=Code.OK, message=""), properties={})
@@ -50,6 +51,7 @@ def get_parameters(
         self,
         ins: GetParametersIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> GetParametersRes:
         """Return the current local model parameters."""
         return GetParametersRes(
@@ -61,6 +63,7 @@ def fit(
         self,
         ins: FitIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> FitRes:
         """Refine the provided weights using the locally held dataset."""
         return FitRes(
@@ -74,6 +77,7 @@ def evaluate(
         self,
         ins: EvaluateIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> EvaluateRes:
         """Evaluate the provided weights using the locally held dataset."""
         return EvaluateRes(
@@ -84,6 +88,7 @@ def reconnect(
         self,
         ins: ReconnectIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> DisconnectRes:
         """Disconnect and (optionally) reconnect later."""
         return DisconnectRes(reason="")
diff --git a/src/py/flwr/server/compat/driver_client_proxy.py b/src/py/flwr/server/compat/driver_client_proxy.py
index a52bd229892d..46b2d92d9f04 100644
--- a/src/py/flwr/server/compat/driver_client_proxy.py
+++ b/src/py/flwr/server/compat/driver_client_proxy.py
@@ -47,57 +47,68 @@ def __init__(self, node_id: int, driver: GrpcDriver, anonymous: bool, run_id: in
         self.anonymous = anonymous
 
     def get_properties(
-        self, ins: common.GetPropertiesIns, timeout: Optional[float]
+        self,
+        ins: common.GetPropertiesIns,
+        timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.GetPropertiesRes:
         """Return client's properties."""
         # Ins to RecordSet
         out_recordset = compat.getpropertiesins_to_recordset(ins)
         # Fetch response
         in_recordset = self._send_receive_recordset(
-            out_recordset, MESSAGE_TYPE_GET_PROPERTIES, timeout
+            out_recordset, MESSAGE_TYPE_GET_PROPERTIES, timeout, group_id
         )
         # RecordSet to Res
         return compat.recordset_to_getpropertiesres(in_recordset)
 
     def get_parameters(
-        self, ins: common.GetParametersIns, timeout: Optional[float]
+        self,
+        ins: common.GetParametersIns,
+        timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.GetParametersRes:
         """Return the current local model parameters."""
         # Ins to RecordSet
         out_recordset = compat.getparametersins_to_recordset(ins)
         # Fetch response
         in_recordset = self._send_receive_recordset(
-            out_recordset, MESSAGE_TYPE_GET_PARAMETERS, timeout
+            out_recordset, MESSAGE_TYPE_GET_PARAMETERS, timeout, group_id
         )
         # RecordSet to Res
         return compat.recordset_to_getparametersres(in_recordset, False)
 
-    def fit(self, ins: common.FitIns, timeout: Optional[float]) -> common.FitRes:
+    def fit(
+        self, ins: common.FitIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> common.FitRes:
         """Train model parameters on the locally held dataset."""
         # Ins to RecordSet
         out_recordset = compat.fitins_to_recordset(ins, keep_input=True)
         # Fetch response
         in_recordset = self._send_receive_recordset(
-            out_recordset, MESSAGE_TYPE_FIT, timeout
+            out_recordset, MESSAGE_TYPE_FIT, timeout, group_id
         )
         # RecordSet to Res
         return compat.recordset_to_fitres(in_recordset, keep_input=False)
 
     def evaluate(
-        self, ins: common.EvaluateIns, timeout: Optional[float]
+        self, ins: common.EvaluateIns, timeout: Optional[float], group_id: Optional[int]
     ) -> common.EvaluateRes:
         """Evaluate model parameters on the locally held dataset."""
         # Ins to RecordSet
         out_recordset = compat.evaluateins_to_recordset(ins, keep_input=True)
         # Fetch response
         in_recordset = self._send_receive_recordset(
-            out_recordset, MESSAGE_TYPE_EVALUATE, timeout
+            out_recordset, MESSAGE_TYPE_EVALUATE, timeout, group_id
         )
         # RecordSet to Res
         return compat.recordset_to_evaluateres(in_recordset)
 
     def reconnect(
-        self, ins: common.ReconnectIns, timeout: Optional[float]
+        self,
+        ins: common.ReconnectIns,
+        timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.DisconnectRes:
         """Disconnect and (optionally) reconnect later."""
         return common.DisconnectRes(reason="")  # Nothing to do here (yet)
@@ -107,10 +118,11 @@ def _send_receive_recordset(
         recordset: RecordSet,
         task_type: str,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> RecordSet:
         task_ins = task_pb2.TaskIns(  # pylint: disable=E1101
             task_id="",
-            group_id="",
+            group_id=str(group_id) if group_id is not None else "",
             run_id=self.run_id,
             task=task_pb2.Task(  # pylint: disable=E1101
                 producer=node_pb2.Node(  # pylint: disable=E1101
diff --git a/src/py/flwr/server/compat/driver_client_proxy_test.py b/src/py/flwr/server/compat/driver_client_proxy_test.py
index de6566622b74..91f14553e5c2 100644
--- a/src/py/flwr/server/compat/driver_client_proxy_test.py
+++ b/src/py/flwr/server/compat/driver_client_proxy_test.py
@@ -103,7 +103,7 @@ def test_get_properties(self) -> None:
                 task_res_list=[
                     task_pb2.TaskRes(  # pylint: disable=E1101
                         task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
-                        group_id="",
+                        group_id=str(0),
                         run_id=0,
                         task=_make_task(
                             GetPropertiesRes(
@@ -123,7 +123,9 @@ def test_get_properties(self) -> None:
         )
 
         # Execute
-        value: flwr.common.GetPropertiesRes = client.get_properties(ins, timeout=None)
+        value: flwr.common.GetPropertiesRes = client.get_properties(
+            ins, timeout=None, group_id=0
+        )
 
         # Assert
         assert value.properties["tensor_type"] == "numpy.ndarray"
@@ -141,7 +143,7 @@ def test_get_parameters(self) -> None:
                 task_res_list=[
                     task_pb2.TaskRes(  # pylint: disable=E1101
                         task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
-                        group_id="",
+                        group_id=str(0),
                         run_id=0,
                         task=_make_task(
                             GetParametersRes(
@@ -160,7 +162,7 @@ def test_get_parameters(self) -> None:
 
         # Execute
         value: flwr.common.GetParametersRes = client.get_parameters(
-            ins=get_parameters_ins, timeout=None
+            ins=get_parameters_ins, timeout=None, group_id=0
         )
 
         # Assert
@@ -179,7 +181,7 @@ def test_fit(self) -> None:
                 task_res_list=[
                     task_pb2.TaskRes(  # pylint: disable=E1101
                         task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
-                        group_id="",
+                        group_id=str(1),
                         run_id=0,
                         task=_make_task(
                             FitRes(
@@ -200,7 +202,7 @@ def test_fit(self) -> None:
         ins: flwr.common.FitIns = flwr.common.FitIns(parameters, {})
 
         # Execute
-        fit_res = client.fit(ins=ins, timeout=None)
+        fit_res = client.fit(ins=ins, timeout=None, group_id=1)
 
         # Assert
         assert fit_res.parameters.tensor_type == "np"
@@ -220,7 +222,7 @@ def test_evaluate(self) -> None:
                 task_res_list=[
                     task_pb2.TaskRes(  # pylint: disable=E1101
                         task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
-                        group_id="",
+                        group_id=str(1),
                         run_id=0,
                         task=_make_task(
                             EvaluateRes(
@@ -241,7 +243,7 @@ def test_evaluate(self) -> None:
         evaluate_ins = EvaluateIns(parameters, {})
 
         # Execute
-        evaluate_res = client.evaluate(evaluate_ins, timeout=None)
+        evaluate_res = client.evaluate(evaluate_ins, timeout=None, group_id=1)
 
         # Assert
         assert 0.0 == evaluate_res.loss
diff --git a/src/py/flwr/server/server.py b/src/py/flwr/server/server.py
index ea62587b7deb..dc7040193101 100644
--- a/src/py/flwr/server/server.py
+++ b/src/py/flwr/server/server.py
@@ -89,7 +89,7 @@ def fit(self, num_rounds: int, timeout: Optional[float]) -> History:
 
         # Initialize parameters
         log(INFO, "Initializing global parameters")
-        self.parameters = self._get_initial_parameters(timeout=timeout)
+        self.parameters = self._get_initial_parameters(server_round=0, timeout=timeout)
         log(INFO, "Evaluating initial parameters")
         res = self.strategy.evaluate(0, parameters=self.parameters)
         if res is not None:
@@ -185,6 +185,7 @@ def evaluate_round(
             client_instructions,
             max_workers=self.max_workers,
             timeout=timeout,
+            group_id=server_round,
         )
         log(
             DEBUG,
@@ -234,6 +235,7 @@ def fit_round(
             client_instructions=client_instructions,
             max_workers=self.max_workers,
             timeout=timeout,
+            group_id=server_round,
         )
         log(
             DEBUG,
@@ -264,7 +266,9 @@ def disconnect_all_clients(self, timeout: Optional[float]) -> None:
             timeout=timeout,
         )
 
-    def _get_initial_parameters(self, timeout: Optional[float]) -> Parameters:
+    def _get_initial_parameters(
+        self, server_round: int, timeout: Optional[float]
+    ) -> Parameters:
         """Get initial parameters from one of the available clients."""
         # Server-side parameter initialization
         parameters: Optional[Parameters] = self.strategy.initialize_parameters(
@@ -278,7 +282,9 @@ def _get_initial_parameters(self, timeout: Optional[float]) -> Parameters:
         log(INFO, "Requesting initial parameters from one random client")
         random_client = self._client_manager.sample(1)[0]
         ins = GetParametersIns(config={})
-        get_parameters_res = random_client.get_parameters(ins=ins, timeout=timeout)
+        get_parameters_res = random_client.get_parameters(
+            ins=ins, timeout=timeout, group_id=server_round
+        )
         log(INFO, "Received initial parameters from one random client")
         return get_parameters_res.parameters
 
@@ -321,6 +327,7 @@ def reconnect_client(
     disconnect = client.reconnect(
         reconnect,
         timeout=timeout,
+        group_id=None,
     )
     return client, disconnect
 
@@ -329,11 +336,12 @@ def fit_clients(
     client_instructions: List[Tuple[ClientProxy, FitIns]],
     max_workers: Optional[int],
     timeout: Optional[float],
+    group_id: int,
 ) -> FitResultsAndFailures:
     """Refine parameters concurrently on all selected clients."""
     with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
         submitted_fs = {
-            executor.submit(fit_client, client_proxy, ins, timeout)
+            executor.submit(fit_client, client_proxy, ins, timeout, group_id)
             for client_proxy, ins in client_instructions
         }
         finished_fs, _ = concurrent.futures.wait(
@@ -352,10 +360,10 @@ def fit_clients(
 
 
 def fit_client(
-    client: ClientProxy, ins: FitIns, timeout: Optional[float]
+    client: ClientProxy, ins: FitIns, timeout: Optional[float], group_id: int
 ) -> Tuple[ClientProxy, FitRes]:
     """Refine parameters on a single client."""
-    fit_res = client.fit(ins, timeout=timeout)
+    fit_res = client.fit(ins, timeout=timeout, group_id=group_id)
     return client, fit_res
 
 
@@ -388,11 +396,12 @@ def evaluate_clients(
     client_instructions: List[Tuple[ClientProxy, EvaluateIns]],
     max_workers: Optional[int],
     timeout: Optional[float],
+    group_id: int,
 ) -> EvaluateResultsAndFailures:
     """Evaluate parameters concurrently on all selected clients."""
     with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
         submitted_fs = {
-            executor.submit(evaluate_client, client_proxy, ins, timeout)
+            executor.submit(evaluate_client, client_proxy, ins, timeout, group_id)
             for client_proxy, ins in client_instructions
         }
         finished_fs, _ = concurrent.futures.wait(
@@ -414,9 +423,10 @@ def evaluate_client(
     client: ClientProxy,
     ins: EvaluateIns,
     timeout: Optional[float],
+    group_id: int,
 ) -> Tuple[ClientProxy, EvaluateRes]:
     """Evaluate parameters on a single client."""
-    evaluate_res = client.evaluate(ins, timeout=timeout)
+    evaluate_res = client.evaluate(ins, timeout=timeout, group_id=group_id)
     return client, evaluate_res
 
 
diff --git a/src/py/flwr/server/server_test.py b/src/py/flwr/server/server_test.py
index 9b5c03aeeaf9..274e5289fee1 100644
--- a/src/py/flwr/server/server_test.py
+++ b/src/py/flwr/server/server_test.py
@@ -45,18 +45,20 @@ class SuccessClient(ClientProxy):
     """Test class."""
 
     def get_properties(
-        self, ins: GetPropertiesIns, timeout: Optional[float]
+        self, ins: GetPropertiesIns, timeout: Optional[float], group_id: Optional[int]
     ) -> GetPropertiesRes:
         """Raise an error because this method is not expected to be called."""
         raise NotImplementedError()
 
     def get_parameters(
-        self, ins: GetParametersIns, timeout: Optional[float]
+        self, ins: GetParametersIns, timeout: Optional[float], group_id: Optional[int]
     ) -> GetParametersRes:
         """Raise a error because this method is not expected to be called."""
         raise NotImplementedError()
 
-    def fit(self, ins: FitIns, timeout: Optional[float]) -> FitRes:
+    def fit(
+        self, ins: FitIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> FitRes:
         """Simulate fit by returning a success FitRes with simple set of weights."""
         arr = np.array([[1, 2], [3, 4], [5, 6]])
         arr_serialized = ndarray_to_bytes(arr)
@@ -67,7 +69,9 @@ def fit(self, ins: FitIns, timeout: Optional[float]) -> FitRes:
             metrics={},
         )
 
-    def evaluate(self, ins: EvaluateIns, timeout: Optional[float]) -> EvaluateRes:
+    def evaluate(
+        self, ins: EvaluateIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> EvaluateRes:
         """Simulate evaluate by returning a success EvaluateRes with loss 1.0."""
         return EvaluateRes(
             status=Status(code=Code.OK, message="Success"),
@@ -76,7 +80,9 @@ def evaluate(self, ins: EvaluateIns, timeout: Optional[float]) -> EvaluateRes:
             metrics={},
         )
 
-    def reconnect(self, ins: ReconnectIns, timeout: Optional[float]) -> DisconnectRes:
+    def reconnect(
+        self, ins: ReconnectIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> DisconnectRes:
         """Simulate reconnect by returning a DisconnectRes with UNKNOWN reason."""
         return DisconnectRes(reason="UNKNOWN")
 
@@ -85,26 +91,32 @@ class FailingClient(ClientProxy):
     """Test class."""
 
     def get_properties(
-        self, ins: GetPropertiesIns, timeout: Optional[float]
+        self, ins: GetPropertiesIns, timeout: Optional[float], group_id: Optional[int]
     ) -> GetPropertiesRes:
         """Raise a NotImplementedError to simulate failure in the client."""
         raise NotImplementedError()
 
     def get_parameters(
-        self, ins: GetParametersIns, timeout: Optional[float]
+        self, ins: GetParametersIns, timeout: Optional[float], group_id: Optional[int]
     ) -> GetParametersRes:
         """Raise a NotImplementedError to simulate failure in the client."""
         raise NotImplementedError()
 
-    def fit(self, ins: FitIns, timeout: Optional[float]) -> FitRes:
+    def fit(
+        self, ins: FitIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> FitRes:
         """Raise a NotImplementedError to simulate failure in the client."""
         raise NotImplementedError()
 
-    def evaluate(self, ins: EvaluateIns, timeout: Optional[float]) -> EvaluateRes:
+    def evaluate(
+        self, ins: EvaluateIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> EvaluateRes:
         """Raise a NotImplementedError to simulate failure in the client."""
         raise NotImplementedError()
 
-    def reconnect(self, ins: ReconnectIns, timeout: Optional[float]) -> DisconnectRes:
+    def reconnect(
+        self, ins: ReconnectIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> DisconnectRes:
         """Raise a NotImplementedError to simulate failure in the client."""
         raise NotImplementedError()
 
@@ -122,7 +134,7 @@ def test_fit_clients() -> None:
     client_instructions = [(c, ins) for c in clients]
 
     # Execute
-    results, failures = fit_clients(client_instructions, None, None)
+    results, failures = fit_clients(client_instructions, None, None, 0)
 
     # Assert
     assert len(results) == 1
@@ -150,6 +162,7 @@ def test_eval_clients() -> None:
         client_instructions=client_instructions,
         max_workers=None,
         timeout=None,
+        group_id=0,
     )
 
     # Assert
diff --git a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy.py
index 026e8dfe51ef..ac62ad014950 100644
--- a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy.py
+++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy.py
@@ -46,6 +46,7 @@ def get_properties(
         self,
         ins: common.GetPropertiesIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.GetPropertiesRes:
         """Request client's set of internal properties."""
         get_properties_msg = serde.get_properties_ins_to_proto(ins)
@@ -65,6 +66,7 @@ def get_parameters(
         self,
         ins: common.GetParametersIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.GetParametersRes:
         """Return the current local model parameters."""
         get_parameters_msg = serde.get_parameters_ins_to_proto(ins)
@@ -84,6 +86,7 @@ def fit(
         self,
         ins: common.FitIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.FitRes:
         """Refine the provided parameters using the locally held dataset."""
         fit_ins_msg = serde.fit_ins_to_proto(ins)
@@ -102,6 +105,7 @@ def evaluate(
         self,
         ins: common.EvaluateIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.EvaluateRes:
         """Evaluate the provided parameters using the locally held dataset."""
         evaluate_msg = serde.evaluate_ins_to_proto(ins)
@@ -119,6 +123,7 @@ def reconnect(
         self,
         ins: common.ReconnectIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.DisconnectRes:
         """Disconnect and (optionally) reconnect later."""
         reconnect_ins_msg = serde.reconnect_ins_to_proto(ins)
diff --git a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy_test.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy_test.py
index 360570eb663d..e7077dfd39ae 100644
--- a/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy_test.py
+++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy_test.py
@@ -71,7 +71,7 @@ def test_get_parameters(self) -> None:
 
         # Execute
         value: flwr.common.GetParametersRes = client.get_parameters(
-            ins=get_parameters_ins, timeout=None
+            ins=get_parameters_ins, timeout=None, group_id=0
         )
 
         # Assert
@@ -88,7 +88,7 @@ def test_fit(self) -> None:
         ins: flwr.common.FitIns = flwr.common.FitIns(parameters, {})
 
         # Execute
-        fit_res = client.fit(ins=ins, timeout=None)
+        fit_res = client.fit(ins=ins, timeout=None, group_id=0)
 
         # Assert
         assert fit_res.parameters.tensor_type == "np"
@@ -106,7 +106,7 @@ def test_evaluate(self) -> None:
         evaluate_ins: flwr.common.EvaluateIns = flwr.common.EvaluateIns(parameters, {})
 
         # Execute
-        evaluate_res = client.evaluate(evaluate_ins, timeout=None)
+        evaluate_res = client.evaluate(evaluate_ins, timeout=None, group_id=1)
 
         # Assert
         assert (0, 0.0) == (
@@ -127,7 +127,9 @@ def test_get_properties(self) -> None:
         )
 
         # Execute
-        value: flwr.common.GetPropertiesRes = client.get_properties(ins, timeout=None)
+        value: flwr.common.GetPropertiesRes = client.get_properties(
+            ins, timeout=None, group_id=0
+        )
 
         # Assert
         assert value.properties["tensor_type"] == "numpy.ndarray"
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
index 6d04fa875201..ba31a69af8ee 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
@@ -98,6 +98,7 @@ def _wrap_recordset_in_message(
         recordset: RecordSet,
         message_type: str,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> Message:
         """Wrap a RecordSet inside a Message."""
         return Message(
@@ -105,7 +106,7 @@ def _wrap_recordset_in_message(
             metadata=Metadata(
                 run_id=0,
                 message_id="",
-                group_id="",
+                group_id=str(group_id) if group_id is not None else "",
                 src_node_id=0,
                 dst_node_id=int(self.cid),
                 reply_to_message="",
@@ -116,7 +117,10 @@ def _wrap_recordset_in_message(
         )
 
     def get_properties(
-        self, ins: common.GetPropertiesIns, timeout: Optional[float]
+        self,
+        ins: common.GetPropertiesIns,
+        timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.GetPropertiesRes:
         """Return client's properties."""
         recordset = getpropertiesins_to_recordset(ins)
@@ -124,6 +128,7 @@ def get_properties(
             recordset,
             message_type=MESSAGE_TYPE_GET_PROPERTIES,
             timeout=timeout,
+            group_id=group_id,
         )
 
         message_out = self._submit_job(message, timeout)
@@ -131,7 +136,10 @@ def get_properties(
         return recordset_to_getpropertiesres(message_out.content)
 
     def get_parameters(
-        self, ins: common.GetParametersIns, timeout: Optional[float]
+        self,
+        ins: common.GetParametersIns,
+        timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.GetParametersRes:
         """Return the current local model parameters."""
         recordset = getparametersins_to_recordset(ins)
@@ -139,19 +147,25 @@ def get_parameters(
             recordset,
             message_type=MESSAGE_TYPE_GET_PARAMETERS,
             timeout=timeout,
+            group_id=group_id,
         )
 
         message_out = self._submit_job(message, timeout)
 
         return recordset_to_getparametersres(message_out.content, keep_input=False)
 
-    def fit(self, ins: common.FitIns, timeout: Optional[float]) -> common.FitRes:
+    def fit(
+        self, ins: common.FitIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> common.FitRes:
         """Train model parameters on the locally held dataset."""
         recordset = fitins_to_recordset(
             ins, keep_input=True
         )  # This must stay TRUE since ins are in-memory
         message = self._wrap_recordset_in_message(
-            recordset, message_type=MESSAGE_TYPE_FIT, timeout=timeout
+            recordset,
+            message_type=MESSAGE_TYPE_FIT,
+            timeout=timeout,
+            group_id=group_id,
         )
 
         message_out = self._submit_job(message, timeout)
@@ -159,14 +173,17 @@ def fit(self, ins: common.FitIns, timeout: Optional[float]) -> common.FitRes:
         return recordset_to_fitres(message_out.content, keep_input=False)
 
     def evaluate(
-        self, ins: common.EvaluateIns, timeout: Optional[float]
+        self, ins: common.EvaluateIns, timeout: Optional[float], group_id: Optional[int]
     ) -> common.EvaluateRes:
         """Evaluate model parameters on the locally held dataset."""
         recordset = evaluateins_to_recordset(
             ins, keep_input=True
         )  # This must stay TRUE since ins are in-memory
         message = self._wrap_recordset_in_message(
-            recordset, message_type=MESSAGE_TYPE_EVALUATE, timeout=timeout
+            recordset,
+            message_type=MESSAGE_TYPE_EVALUATE,
+            timeout=timeout,
+            group_id=group_id,
         )
 
         message_out = self._submit_job(message, timeout)
@@ -174,7 +191,10 @@ def evaluate(
         return recordset_to_evaluateres(message_out.content)
 
     def reconnect(
-        self, ins: common.ReconnectIns, timeout: Optional[float]
+        self,
+        ins: common.ReconnectIns,
+        timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.DisconnectRes:
         """Disconnect and (optionally) reconnect later."""
         return common.DisconnectRes(reason="")  # Nothing to do here (yet)
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
index 684fad60bf74..e59033ad39b5 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
@@ -114,6 +114,7 @@ def test_cid_consistency_one_at_a_time() -> None:
             recordset,
             MESSAGE_TYPE_GET_PROPERTIES,
             timeout=None,
+            group_id=0,
         )
         message_out = prox._submit_job(  # pylint: disable=protected-access
             message=message, timeout=None
@@ -150,6 +151,7 @@ def test_cid_consistency_all_submit_first_run_consistency() -> None:
             recordset,
             message_type=MESSAGE_TYPE_GET_PROPERTIES,
             timeout=None,
+            group_id=0,
         )
         prox.actor_pool.submit_client_job(
             lambda a, a_fn, mssg, cid, state: a.run.remote(a_fn, mssg, cid, state),
@@ -196,7 +198,7 @@ def _load_app() -> ClientApp:
             metadata=Metadata(
                 run_id=0,
                 message_id="",
-                group_id="",
+                group_id=str(0),
                 src_node_id=0,
                 dst_node_id=12345,
                 reply_to_message="",

From bf35ca1c16bf2caaa6ad7e8d4c85111843ced5fb Mon Sep 17 00:00:00 2001
From: mohammadnaseri <mohammad@flower.dev>
Date: Wed, 28 Feb 2024 16:14:52 +0000
Subject: [PATCH 074/102] Introduce central DP with client-side adaptive
 clipping (#2892)

---
 src/py/flwr/client/mod/__init__.py            |   5 +-
 src/py/flwr/client/mod/centraldp_mods.py      |  65 ++++-
 src/py/flwr/common/differential_privacy.py    |  77 ++++++
 .../common/differential_privacy_constants.py  |   1 +
 .../flwr/common/differential_privacy_test.py  |  73 +++++-
 src/py/flwr/server/strategy/__init__.py       |  26 +-
 .../server/strategy/dp_adaptive_clipping.py   | 245 ++++++++++++++++++
 .../flwr/server/strategy/dp_fixed_clipping.py |  12 +-
 8 files changed, 480 insertions(+), 24 deletions(-)
 create mode 100644 src/py/flwr/server/strategy/dp_adaptive_clipping.py

diff --git a/src/py/flwr/client/mod/__init__.py b/src/py/flwr/client/mod/__init__.py
index 8ed6f52ef788..e06cabe995d7 100644
--- a/src/py/flwr/client/mod/__init__.py
+++ b/src/py/flwr/client/mod/__init__.py
@@ -15,12 +15,13 @@
 """Mods."""
 
 
-from .centraldp_mods import fixedclipping_mod
+from .centraldp_mods import adaptiveclipping_mod, fixedclipping_mod
 from .secure_aggregation.secaggplus_mod import secaggplus_mod
 from .utils import make_ffn
 
 __all__ = [
+    "adaptiveclipping_mod",
+    "fixedclipping_mod",
     "make_ffn",
     "secaggplus_mod",
-    "fixedclipping_mod",
 ]
diff --git a/src/py/flwr/client/mod/centraldp_mods.py b/src/py/flwr/client/mod/centraldp_mods.py
index 76bfe1b06e55..8a18e87c69f5 100644
--- a/src/py/flwr/client/mod/centraldp_mods.py
+++ b/src/py/flwr/client/mod/centraldp_mods.py
@@ -20,8 +20,11 @@
 from flwr.common import recordset_compat as compat
 from flwr.common.constant import MESSAGE_TYPE_FIT
 from flwr.common.context import Context
-from flwr.common.differential_privacy import compute_clip_model_update
-from flwr.common.differential_privacy_constants import KEY_CLIPPING_NORM
+from flwr.common.differential_privacy import (
+    compute_adaptive_clip_model_update,
+    compute_clip_model_update,
+)
+from flwr.common.differential_privacy_constants import KEY_CLIPPING_NORM, KEY_NORM_BIT
 from flwr.common.message import Message
 
 
@@ -74,3 +77,61 @@ def fixedclipping_mod(
     fit_res.parameters = ndarrays_to_parameters(client_to_server_params)
     out_msg.content = compat.fitres_to_recordset(fit_res, keep_input=True)
     return out_msg
+
+
+def adaptiveclipping_mod(
+    msg: Message, ctxt: Context, call_next: ClientAppCallable
+) -> Message:
+    """Client-side adaptive clipping modifier.
+
+    This mod needs to be used with the DifferentialPrivacyClientSideAdaptiveClipping
+    server-side strategy wrapper.
+
+    The wrapper sends the clipping_norm value to the client.
+
+    This mod clips the client model updates before sending them to the server.
+
+    It also sends KEY_NORM_BIT to the server for computing the new clipping value.
+
+    It operates on messages with type MESSAGE_TYPE_FIT.
+
+    Notes
+    -----
+    Consider the order of mods when using multiple.
+
+    Typically, adaptiveclipping_mod should be the last to operate on params.
+    """
+    if msg.metadata.message_type != MESSAGE_TYPE_FIT:
+        return call_next(msg, ctxt)
+
+    fit_ins = compat.recordset_to_fitins(msg.content, keep_input=True)
+
+    if KEY_CLIPPING_NORM not in fit_ins.config:
+        raise KeyError(
+            f"The {KEY_CLIPPING_NORM} value is not supplied by the "
+            f"DifferentialPrivacyClientSideFixedClipping wrapper at"
+            f" the server side."
+        )
+    if not isinstance(fit_ins.config[KEY_CLIPPING_NORM], float):
+        raise ValueError(f"{KEY_CLIPPING_NORM} should be a float value.")
+    clipping_norm = float(fit_ins.config[KEY_CLIPPING_NORM])
+    server_to_client_params = parameters_to_ndarrays(fit_ins.parameters)
+
+    # Call inner app
+    out_msg = call_next(msg, ctxt)
+    fit_res = compat.recordset_to_fitres(out_msg.content, keep_input=True)
+
+    client_to_server_params = parameters_to_ndarrays(fit_res.parameters)
+
+    # Clip the client update
+    norm_bit = compute_adaptive_clip_model_update(
+        client_to_server_params,
+        server_to_client_params,
+        clipping_norm,
+    )
+
+    fit_res.parameters = ndarrays_to_parameters(client_to_server_params)
+
+    fit_res.metrics[KEY_NORM_BIT] = norm_bit
+    out_msg.content = compat.fitres_to_recordset(fit_res, keep_input=True)
+    return out_msg
diff --git a/src/py/flwr/common/differential_privacy.py b/src/py/flwr/common/differential_privacy.py
index c6ec45d30f8e..3b81fba44733 100644
--- a/src/py/flwr/common/differential_privacy.py
+++ b/src/py/flwr/common/differential_privacy.py
@@ -15,6 +15,9 @@
 """Utility functions for differential privacy."""
 
 
+from logging import WARNING
+from typing import Optional, Tuple
+
 import numpy as np
 
 from flwr.common import (
@@ -23,6 +26,7 @@
     ndarrays_to_parameters,
     parameters_to_ndarrays,
 )
+from flwr.common.logger import log
 
 
 def get_norm(input_arrays: NDArrays) -> float:
@@ -72,6 +76,36 @@ def compute_clip_model_update(
         param1[i] = param2[i] + model_update[i]
 
 
+def adaptive_clip_inputs_inplace(input_arrays: NDArrays, clipping_norm: float) -> bool:
+    """Clip model update based on the clipping norm in-place.
+
+    It returns true if scaling_factor < 1 which is used for norm_bit
+    FlatClip method of the paper: https://arxiv.org/abs/1710.06963
+    """
+    input_norm = get_norm(input_arrays)
+    scaling_factor = min(1, clipping_norm / input_norm)
+    for array in input_arrays:
+        array *= scaling_factor
+    return scaling_factor < 1
+
+
+def compute_adaptive_clip_model_update(
+    param1: NDArrays, param2: NDArrays, clipping_norm: float
+) -> bool:
+    """Compute model update, clip it, then add the clipped value to param1.
+
+    model update = param1 - param2
+    Return the norm_bit
+    """
+    model_update = [np.subtract(x, y) for (x, y) in zip(param1, param2)]
+    norm_bit = adaptive_clip_inputs_inplace(model_update, clipping_norm)
+
+    for i, _ in enumerate(param2):
+        param1[i] = param2[i] + model_update[i]
+
+    return norm_bit
+
+
 def add_gaussian_noise_to_params(
     model_params: Parameters,
     noise_multiplier: float,
@@ -85,3 +119,46 @@ def add_gaussian_noise_to_params(
         compute_stdv(noise_multiplier, clipping_norm, num_sampled_clients),
     )
     return ndarrays_to_parameters(model_params_ndarrays)
+
+
+def compute_adaptive_noise_params(
+    noise_multiplier: float,
+    num_sampled_clients: float,
+    clipped_count_stddev: Optional[float],
+) -> Tuple[float, float]:
+    """Compute noising parameters for the adaptive clipping.
+
+    Paper: https://arxiv.org/abs/1905.03871
+    """
+    if noise_multiplier > 0:
+        if clipped_count_stddev is None:
+            clipped_count_stddev = num_sampled_clients / 20
+        if noise_multiplier >= 2 * clipped_count_stddev:
+            raise ValueError(
+                f"If not specified, `clipped_count_stddev` is set to "
+                f"`num_sampled_clients`/20 by default. This value "
+                f"({num_sampled_clients / 20}) is too low to achieve the "
+                f"desired effective `noise_multiplier` ({noise_multiplier}). "
+                f"Consider increasing `clipped_count_stddev` or decreasing "
+                f"`noise_multiplier`."
+            )
+        noise_multiplier_value = (
+            noise_multiplier ** (-2) - (2 * clipped_count_stddev) ** (-2)
+        ) ** -0.5
+
+        adding_noise = noise_multiplier_value / noise_multiplier
+        if adding_noise >= 2:
+            log(
+                WARNING,
+                "A significant amount of noise (%s) has to be "
+                "added. Consider increasing `clipped_count_stddev` or "
+                "`num_sampled_clients`.",
+                adding_noise,
+            )
+
+    else:
+        if clipped_count_stddev is None:
+            clipped_count_stddev = 0.0
+        noise_multiplier_value = 0.0
+
+    return clipped_count_stddev, noise_multiplier_value
diff --git a/src/py/flwr/common/differential_privacy_constants.py b/src/py/flwr/common/differential_privacy_constants.py
index 61ba3ba9a958..415759dfdf0b 100644
--- a/src/py/flwr/common/differential_privacy_constants.py
+++ b/src/py/flwr/common/differential_privacy_constants.py
@@ -16,6 +16,7 @@
 
 
 KEY_CLIPPING_NORM = "clipping_norm"
+KEY_NORM_BIT = "norm_bit"
 CLIENTS_DISCREPANCY_WARNING = (
     "The number of clients returning parameters (%s)"
     " differs from the number of sampled clients (%s)."
diff --git a/src/py/flwr/common/differential_privacy_test.py b/src/py/flwr/common/differential_privacy_test.py
index 32b7bc9e4b36..ea2e193df2f3 100644
--- a/src/py/flwr/common/differential_privacy_test.py
+++ b/src/py/flwr/common/differential_privacy_test.py
@@ -20,6 +20,7 @@
 from .differential_privacy import (
     add_gaussian_noise_inplace,
     clip_inputs_inplace,
+    compute_adaptive_noise_params,
     compute_clip_model_update,
     compute_stdv,
     get_norm,
@@ -133,6 +134,76 @@ def test_compute_clip_model_update() -> None:
     # Execute
     compute_clip_model_update(param1, param2, clipping_norm)
 
-    # Verify
+    # Assert
     for i, param in enumerate(param1):
         np.testing.assert_array_almost_equal(param, expected_result[i])
+
+
+def test_compute_adaptive_noise_params() -> None:
+    """Test compute_adaptive_noise_params function."""
+    # Test valid input with positive noise_multiplier
+    noise_multiplier = 1.0
+    num_sampled_clients = 100.0
+    clipped_count_stddev = None
+    result = compute_adaptive_noise_params(
+        noise_multiplier, num_sampled_clients, clipped_count_stddev
+    )
+
+    # Assert
+    assert isinstance(result, tuple)
+    assert len(result) == 2
+    assert result[0] > 0.0
+    assert result[1] > 0.0
+
+    # Test valid input with zero noise_multiplier
+    noise_multiplier = 0.0
+    num_sampled_clients = 50.0
+    clipped_count_stddev = None
+    result = compute_adaptive_noise_params(
+        noise_multiplier, num_sampled_clients, clipped_count_stddev
+    )
+
+    # Assert
+    assert isinstance(result, tuple)
+    assert len(result) == 2
+    assert result[0] == 0.0
+    assert result[1] == 0.0
+
+    # Test valid input with specified clipped_count_stddev
+    noise_multiplier = 3.0
+    num_sampled_clients = 200.0
+    clipped_count_stddev = 5.0
+    result = compute_adaptive_noise_params(
+        noise_multiplier, num_sampled_clients, clipped_count_stddev
+    )
+
+    # Assert
+    assert isinstance(result, tuple)
+    assert len(result) == 2
+    assert result[0] == clipped_count_stddev
+    assert result[1] > 0.0
+
+    # Test invalid input with noise_multiplier >= 2 * clipped_count_stddev
+    noise_multiplier = 10.0
+    num_sampled_clients = 100.0
+    clipped_count_stddev = None
+    try:
+        compute_adaptive_noise_params(
+            noise_multiplier, num_sampled_clients, clipped_count_stddev
+        )
+    except ValueError:
+        pass
+    else:
+        raise AssertionError("Expected ValueError not raised.")
+
+    # Test intermediate calculation
+    noise_multiplier = 3.0
+    num_sampled_clients = 200.0
+    clipped_count_stddev = 5.0
+    result = compute_adaptive_noise_params(
+        noise_multiplier, num_sampled_clients, clipped_count_stddev
+    )
+    temp_value = (noise_multiplier ** (-2) - (2 * clipped_count_stddev) ** (-2)) ** -0.5
+
+    # Assert
+    assert np.isclose(result[1], temp_value, rtol=1e-6)
diff --git a/src/py/flwr/server/strategy/__init__.py b/src/py/flwr/server/strategy/__init__.py
index 6b058c3df05b..ffb55c12a60d 100644
--- a/src/py/flwr/server/strategy/__init__.py
+++ b/src/py/flwr/server/strategy/__init__.py
@@ -16,6 +16,7 @@
 
 
 from .bulyan import Bulyan as Bulyan
+from .dp_adaptive_clipping import DifferentialPrivacyClientSideAdaptiveClipping
 from .dp_fixed_clipping import (
     DifferentialPrivacyClientSideFixedClipping,
     DifferentialPrivacyServerSideFixedClipping,
@@ -41,26 +42,27 @@
 from .strategy import Strategy as Strategy
 
 __all__ = [
-    "FaultTolerantFedAvg",
+    "Bulyan",
+    "DPFedAvgAdaptive",
+    "DPFedAvgFixed",
+    "DifferentialPrivacyClientSideAdaptiveClipping",
+    "DifferentialPrivacyClientSideFixedClipping",
+    "DifferentialPrivacyServerSideFixedClipping",
     "FedAdagrad",
     "FedAdam",
     "FedAvg",
-    "FedXgbNnAvg",
-    "FedXgbBagging",
-    "FedXgbCyclic",
     "FedAvgAndroid",
     "FedAvgM",
+    "FedMedian",
     "FedOpt",
     "FedProx",
-    "FedYogi",
-    "QFedAvg",
-    "FedMedian",
     "FedTrimmedAvg",
+    "FedXgbBagging",
+    "FedXgbCyclic",
+    "FedXgbNnAvg",
+    "FedYogi",
+    "FaultTolerantFedAvg",
     "Krum",
-    "Bulyan",
-    "DPFedAvgAdaptive",
-    "DPFedAvgFixed",
+    "QFedAvg",
     "Strategy",
-    "DifferentialPrivacyServerSideFixedClipping",
-    "DifferentialPrivacyClientSideFixedClipping",
 ]
diff --git a/src/py/flwr/server/strategy/dp_adaptive_clipping.py b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
new file mode 100644
index 000000000000..a5de29e74645
--- /dev/null
+++ b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
@@ -0,0 +1,245 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Central differential privacy with adaptive clipping.
+
+Paper (Andrew et al.): https://arxiv.org/abs/1905.03871
+"""
+
+
+import math
+from logging import WARNING
+from typing import Dict, List, Optional, Tuple, Union
+
+import numpy as np
+
+from flwr.common import EvaluateIns, EvaluateRes, FitIns, FitRes, Parameters, Scalar
+from flwr.common.differential_privacy import (
+    add_gaussian_noise_to_params,
+    compute_adaptive_noise_params,
+)
+from flwr.common.differential_privacy_constants import (
+    CLIENTS_DISCREPANCY_WARNING,
+    KEY_CLIPPING_NORM,
+    KEY_NORM_BIT,
+)
+from flwr.common.logger import log
+from flwr.server.client_manager import ClientManager
+from flwr.server.client_proxy import ClientProxy
+from flwr.server.strategy.strategy import Strategy
+
+
+class DifferentialPrivacyClientSideAdaptiveClipping(Strategy):
+    """Strategy wrapper for central DP with client-side adaptive clipping.
+
+    Use `adaptiveclipping_mod` modifier at the client side.
+
+    In comparison to `DifferentialPrivacyServerSideAdaptiveClipping`,
+    which performs clipping on the server-side, `DifferentialPrivacyClientSideAdaptiveClipping`
+    expects clipping to happen on the client-side, usually by using the built-in
+    `adaptiveclipping_mod`.
+
+    Parameters
+    ----------
+    strategy : Strategy
+        The strategy to which DP functionalities will be added by this wrapper.
+    noise_multiplier : float
+        The noise multiplier for the Gaussian mechanism for model updates.
+    num_sampled_clients : int
+        The number of clients that are sampled on each round.
+    initial_clipping_norm : float
+        The initial value of clipping norm. Deafults to 0.1.
+        Andrew et al. recommends to set to 0.1.
+    target_clipped_quantile : float
+        The desired quantile of updates which should be clipped. Defaults to 0.5.
+    clip_norm_lr : float
+        The learning rate for the clipping norm adaptation. Defaults to 0.2.
+        Andrew et al. recommends to set to 0.2.
+    clipped_count_stddev : float
+        The stddev of the noise added to the count of updates currently below the estimate.
+        Andrew et al. recommends to set to `expected_num_records/20`
+
+    Examples
+    --------
+    Create a strategy:
+
+    >>> strategy = fl.server.strategy.FedAvg(...)
+
+    Wrap the strategy with the `DifferentialPrivacyClientSideAdaptiveClipping` wrapper:
+
+    >>> DifferentialPrivacyClientSideAdaptiveClipping(
+    >>>     strategy, cfg.noise_multiplier, cfg.num_sampled_clients
+    >>> )
+
+    On the client, add the `adaptiveclipping_mod` to the client-side mods:
+
+    >>> app = fl.client.ClientApp(
+    >>>     client_fn=client_fn, mods=[adaptiveclipping_mod]
+    >>> )
+    """
+
+    # pylint: disable=too-many-arguments,too-many-instance-attributes
+    def __init__(
+        self,
+        strategy: Strategy,
+        noise_multiplier: float,
+        num_sampled_clients: int,
+        initial_clipping_norm: float = 0.1,
+        target_clipped_quantile: float = 0.5,
+        clip_norm_lr: float = 0.2,
+        clipped_count_stddev: Optional[float] = None,
+    ) -> None:
+        super().__init__()
+
+        if strategy is None:
+            raise ValueError("The passed strategy is None.")
+
+        if noise_multiplier < 0:
+            raise ValueError("The noise multiplier should be a non-negative value.")
+
+        if num_sampled_clients <= 0:
+            raise ValueError(
+                "The number of sampled clients should be a positive value."
+            )
+
+        if initial_clipping_norm <= 0:
+            raise ValueError("The initial clipping norm should be a positive value.")
+
+        if not 0 <= target_clipped_quantile <= 1:
+            raise ValueError(
+                "The target clipped quantile must be between 0 and 1 (inclusive)."
+            )
+
+        if clip_norm_lr <= 0:
+            raise ValueError("The learning rate must be positive.")
+
+        if clipped_count_stddev is not None and clipped_count_stddev < 0:
+            raise ValueError("The `clipped_count_stddev` must be non-negative.")
+
+        self.strategy = strategy
+        self.num_sampled_clients = num_sampled_clients
+        self.clipping_norm = initial_clipping_norm
+        self.target_clipped_quantile = target_clipped_quantile
+        self.clip_norm_lr = clip_norm_lr
+        (
+            self.clipped_count_stddev,
+            self.noise_multiplier,
+        ) = compute_adaptive_noise_params(
+            noise_multiplier,
+            num_sampled_clients,
+            clipped_count_stddev,
+        )
+
+    def __repr__(self) -> str:
+        """Compute a string representation of the strategy."""
+        rep = "Differential Privacy Strategy Wrapper (Client-Side Adaptive Clipping)"
+        return rep
+
+    def initialize_parameters(
+        self, client_manager: ClientManager
+    ) -> Optional[Parameters]:
+        """Initialize global model parameters using given strategy."""
+        return self.strategy.initialize_parameters(client_manager)
+
+    def configure_fit(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, FitIns]]:
+        """Configure the next round of training."""
+        additional_config = {KEY_CLIPPING_NORM: self.clipping_norm}
+        inner_strategy_config_result = self.strategy.configure_fit(
+            server_round, parameters, client_manager
+        )
+        for _, fit_ins in inner_strategy_config_result:
+            fit_ins.config.update(additional_config)
+
+        return inner_strategy_config_result
+
+    def configure_evaluate(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, EvaluateIns]]:
+        """Configure the next round of evaluation."""
+        return self.strategy.configure_evaluate(
+            server_round, parameters, client_manager
+        )
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Aggregate training results and update clip norms."""
+        if failures:
+            return None, {}
+
+        if len(results) != self.num_sampled_clients:
+            log(
+                WARNING,
+                CLIENTS_DISCREPANCY_WARNING,
+                len(results),
+                self.num_sampled_clients,
+            )
+
+        aggregated_params, metrics = self.strategy.aggregate_fit(
+            server_round, results, failures
+        )
+        self._update_clip_norm(results)
+
+        # Add Gaussian noise to the aggregated parameters
+        if aggregated_params:
+            aggregated_params = add_gaussian_noise_to_params(
+                aggregated_params,
+                self.noise_multiplier,
+                self.clipping_norm,
+                self.num_sampled_clients,
+            )
+
+        return aggregated_params, metrics
+
+    def _update_clip_norm(self, results: List[Tuple[ClientProxy, FitRes]]) -> None:
+        # Calculate the number of clients which set the norm indicator bit
+        norm_bit_set_count = 0
+        for client_proxy, fit_res in results:
+            if KEY_NORM_BIT not in fit_res.metrics:
+                raise KeyError(
+                    f"{KEY_NORM_BIT} not returned by client with id {client_proxy.cid}."
+                )
+            if fit_res.metrics[KEY_NORM_BIT]:
+                norm_bit_set_count += 1
+        # Add noise to the count
+        noised_norm_bit_set_count = float(
+            np.random.normal(norm_bit_set_count, self.clipped_count_stddev)
+        )
+
+        noised_norm_bit_set_fraction = noised_norm_bit_set_count / len(results)
+        # Geometric update
+        self.clipping_norm *= math.exp(
+            -self.clip_norm_lr
+            * (noised_norm_bit_set_fraction - self.target_clipped_quantile)
+        )
+
+    def aggregate_evaluate(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, EvaluateRes]],
+        failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+    ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+        """Aggregate evaluation losses using the given strategy."""
+        return self.strategy.aggregate_evaluate(server_round, results, failures)
+
+    def evaluate(
+        self, server_round: int, parameters: Parameters
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """Evaluate model parameters using an evaluation function from the strategy."""
+        return self.strategy.evaluate(server_round, parameters)
diff --git a/src/py/flwr/server/strategy/dp_fixed_clipping.py b/src/py/flwr/server/strategy/dp_fixed_clipping.py
index fd1809125d3e..69930ce49c0b 100644
--- a/src/py/flwr/server/strategy/dp_fixed_clipping.py
+++ b/src/py/flwr/server/strategy/dp_fixed_clipping.py
@@ -47,8 +47,7 @@
 
 
 class DifferentialPrivacyServerSideFixedClipping(Strategy):
-    """Strategy wrapper for central differential privacy with server-side fixed
-    clipping.
+    """Strategy wrapper for central DP with server-side fixed clipping.
 
     Parameters
     ----------
@@ -192,15 +191,14 @@ def evaluate(
 
 
 class DifferentialPrivacyClientSideFixedClipping(Strategy):
-    """Strategy wrapper for central differential privacy with client-side fixed
-    clipping.
+    """Strategy wrapper for central DP with client-side fixed clipping.
 
     Use `fixedclipping_mod` modifier at the client side.
 
     In comparison to `DifferentialPrivacyServerSideFixedClipping`,
     which performs clipping on the server-side, `DifferentialPrivacyClientSideFixedClipping`
     expects clipping to happen on the client-side, usually by using the built-in
-    `fixedclipping_mod `.
+    `fixedclipping_mod`.
 
     Parameters
     ----------
@@ -220,7 +218,7 @@ class DifferentialPrivacyClientSideFixedClipping(Strategy):
 
     >>> strategy = fl.server.strategy.FedAvg(...)
 
-    Wrap the strategy with the `DifferentialPrivacyServerSideFixedClipping` wrapper:
+    Wrap the strategy with the `DifferentialPrivacyClientSideFixedClipping` wrapper:
 
     >>> DifferentialPrivacyClientSideFixedClipping(
     >>>     strategy, cfg.noise_multiplier, cfg.clipping_norm, cfg.num_sampled_clients
@@ -229,7 +227,7 @@ class DifferentialPrivacyClientSideFixedClipping(Strategy):
     On the client, add the `fixedclipping_mod` to the client-side mods:
 
     >>> app = fl.client.ClientApp(
-    >>>     client_fn=FlowerClient().to_client(), mods=[fixedclipping_mod]
+    >>>     client_fn=client_fn, mods=[fixedclipping_mod]
     >>> )
     """
 

From 61571cdf031fdf570eaeb2f147ad1f862a4cd773 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Wed, 28 Feb 2024 18:15:34 +0100
Subject: [PATCH 075/102] Complete `VCE Fleet` API loop (#2998)

---
 src/py/flwr/server/app.py                     |   6 +-
 .../server/superlink/fleet/vce/__init__.py    |   2 +-
 .../superlink/fleet/vce/backend/raybackend.py |   7 +-
 .../fleet/vce/backend/raybackend_test.py      |   9 +
 .../server/superlink/fleet/vce/vce_api.py     | 252 ++++++++++++++-
 .../superlink/fleet/vce/vce_api_test.py       | 292 ++++++++++++++++++
 6 files changed, 546 insertions(+), 22 deletions(-)
 create mode 100644 src/py/flwr/server/superlink/fleet/vce/vce_api_test.py

diff --git a/src/py/flwr/server/app.py b/src/py/flwr/server/app.py
index a4913d51315b..cf6b716bd189 100644
--- a/src/py/flwr/server/app.py
+++ b/src/py/flwr/server/app.py
@@ -14,8 +14,8 @@
 # ==============================================================================
 """Flower server app."""
 
-
 import argparse
+import asyncio
 import importlib.util
 import sys
 import threading
@@ -362,6 +362,7 @@ def run_superlink() -> None:
         )
         grpc_servers.append(fleet_server)
     elif args.fleet_api_type == TRANSPORT_TYPE_VCE:
+        f_stop = asyncio.Event()  # Does nothing
         _run_fleet_api_vce(
             num_supernodes=args.num_supernodes,
             client_app_module_name=args.client_app,
@@ -369,6 +370,7 @@ def run_superlink() -> None:
             backend_config_json_stream=args.backend_config,
             working_dir=args.dir,
             state_factory=state_factory,
+            f_stop=f_stop,
         )
     else:
         raise ValueError(f"Unknown fleet_api_type: {args.fleet_api_type}")
@@ -468,6 +470,7 @@ def _run_fleet_api_vce(
     backend_config_json_stream: str,
     working_dir: str,
     state_factory: StateFactory,
+    f_stop: asyncio.Event,
 ) -> None:
     log(INFO, "Flower VCE: Starting Fleet API (VirtualClientEngine)")
 
@@ -478,6 +481,7 @@ def _run_fleet_api_vce(
         backend_config_json_stream=backend_config_json_stream,
         state_factory=state_factory,
         working_dir=working_dir,
+        f_stop=f_stop,
     )
 
 
diff --git a/src/py/flwr/server/superlink/fleet/vce/__init__.py b/src/py/flwr/server/superlink/fleet/vce/__init__.py
index 72cd76f73761..57d39688b527 100644
--- a/src/py/flwr/server/superlink/fleet/vce/__init__.py
+++ b/src/py/flwr/server/superlink/fleet/vce/__init__.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Fleet VirtualClientEngine side."""
+"""Fleet Simulation Engine side."""
 
 from .vce_api import start_vce
 
diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
index 409deb077f1d..8ef0d54622ae 100644
--- a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
@@ -141,13 +141,13 @@ async def process_message(
 
         Return output message and updated context.
         """
-        node_id = message.metadata.dst_node_id
+        partition_id = message.metadata.partition_id
 
         try:
             # Submite a task to the pool
             future = await self.pool.submit(
                 lambda a, a_fn, mssg, cid, state: a.run.remote(a_fn, mssg, cid, state),
-                (app, message, str(node_id), context),
+                (app, message, str(partition_id), context),
             )
 
             await future
@@ -163,10 +163,9 @@ async def process_message(
         except LoadClientAppError as load_ex:
             log(
                 ERROR,
-                "An exception was raised when processing a message. Terminating %s",
+                "An exception was raised when processing a message by %s",
                 self.__class__.__name__,
             )
-            await self.terminate()
             raise load_ex
 
     async def terminate(self) -> None:
diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py
index fd246b5fc2af..e14c466e7b82 100644
--- a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py
@@ -20,6 +20,8 @@
 from typing import Callable, Dict, Optional, Tuple, Union
 from unittest import IsolatedAsyncioTestCase
 
+import ray
+
 from flwr.client import Client, NumPyClient
 from flwr.client.client_app import ClientApp, LoadClientAppError, load_client_app
 from flwr.common import (
@@ -119,6 +121,11 @@ def _create_message_and_context() -> Tuple[Message, Context, float]:
 class AsyncTestRayBackend(IsolatedAsyncioTestCase):
     """A basic class that allows runnig multliple asyncio tests."""
 
+    async def on_cleanup(self) -> None:
+        """Ensure Ray has shutdown."""
+        if ray.is_initialized():
+            ray.shutdown()
+
     def test_backend_creation_and_termination(self) -> None:
         """Test creation of RayBackend and its termination."""
         backend = RayBackend(backend_config={}, work_dir="")
@@ -171,6 +178,7 @@ def test_backend_creation_submit_and_termination_non_existing_client_app(
             self.test_backend_creation_submit_and_termination(
                 client_app_loader=_load_from_module("a_non_existing_module:app")
             )
+        self.addAsyncCleanup(self.on_cleanup)
 
     def test_backend_creation_submit_and_termination_existing_client_app(
         self,
@@ -198,3 +206,4 @@ def test_backend_creation_submit_and_termination_existing_client_app_unsetworkdi
                 client_app_loader=_load_from_module("raybackend_test:client_app"),
                 workdir="/?&%$^#%@$!",
             )
+        self.addAsyncCleanup(self.on_cleanup)
diff --git a/src/py/flwr/server/superlink/fleet/vce/vce_api.py b/src/py/flwr/server/superlink/fleet/vce/vce_api.py
index 5d194632541e..ad858cbb9979 100644
--- a/src/py/flwr/server/superlink/fleet/vce/vce_api.py
+++ b/src/py/flwr/server/superlink/fleet/vce/vce_api.py
@@ -12,19 +12,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Fleet VirtualClientEngine API."""
+"""Fleet Simulation Engine API."""
+
 
 import asyncio
 import json
-from logging import ERROR, INFO
-from typing import Dict, Optional
+import traceback
+from logging import DEBUG, ERROR, INFO, WARN
+from typing import Callable, Dict, List, Optional
 
-from flwr.client.client_app import ClientApp, load_client_app
+from flwr.client.client_app import ClientApp, LoadClientAppError, load_client_app
 from flwr.client.node_state import NodeState
 from flwr.common.logger import log
+from flwr.common.serde import message_from_taskins, message_to_taskres
+from flwr.proto.task_pb2 import TaskIns  # pylint: disable=E0611
 from flwr.server.superlink.state import StateFactory
 
-from .backend import error_messages_backends, supported_backends
+from .backend import Backend, error_messages_backends, supported_backends
 
 NodeToPartitionMapping = Dict[int, int]
 
@@ -42,21 +46,223 @@ def _register_nodes(
     return nodes_mapping
 
 
-# pylint: disable=too-many-arguments,unused-argument
+# pylint: disable=too-many-arguments,too-many-locals
+async def worker(
+    app_fn: Callable[[], ClientApp],
+    queue: "asyncio.Queue[TaskIns]",
+    node_states: Dict[int, NodeState],
+    state_factory: StateFactory,
+    nodes_mapping: NodeToPartitionMapping,
+    backend: Backend,
+) -> None:
+    """Get TaskIns from queue and pass it to an actor in the pool to execute it."""
+    state = state_factory.state()
+    while True:
+        try:
+            task_ins: TaskIns = await queue.get()
+            node_id = task_ins.task.consumer.node_id
+
+            # Register and retrieve runstate
+            node_states[node_id].register_context(run_id=task_ins.run_id)
+            context = node_states[node_id].retrieve_context(run_id=task_ins.run_id)
+
+            # Convert TaskIns to Message
+            message = message_from_taskins(task_ins)
+            # Set partition_id
+            message.metadata.partition_id = nodes_mapping[node_id]
+
+            # Let backend process message
+            out_mssg, updated_context = await backend.process_message(
+                app_fn, message, context
+            )
+
+            # Update Context
+            node_states[node_id].update_context(
+                task_ins.run_id, context=updated_context
+            )
+
+            # Convert to TaskRes
+            task_res = message_to_taskres(out_mssg)
+            # Store TaskRes in state
+            state.store_task_res(task_res)
+
+        except asyncio.CancelledError as e:
+            log(DEBUG, "Async worker: %s", e)
+            break
+
+        except LoadClientAppError as app_ex:
+            log(ERROR, "Async worker: %s", app_ex)
+            log(ERROR, traceback.format_exc())
+            raise
+
+        except Exception as ex:  # pylint: disable=broad-exception-caught
+            log(ERROR, ex)
+            log(ERROR, traceback.format_exc())
+            break
+
+
+async def add_taskins_to_queue(
+    queue: "asyncio.Queue[TaskIns]",
+    state_factory: StateFactory,
+    nodes_mapping: NodeToPartitionMapping,
+    backend: Backend,
+    consumers: List["asyncio.Task[None]"],
+    f_stop: asyncio.Event,
+) -> None:
+    """Retrieve TaskIns and add it to the queue."""
+    state = state_factory.state()
+    num_initial_consumers = len(consumers)
+    while not f_stop.is_set():
+        for node_id in nodes_mapping.keys():
+            task_ins = state.get_task_ins(node_id=node_id, limit=1)
+            if task_ins:
+                await queue.put(task_ins[0])
+
+        # Count consumers that are running
+        num_active = sum(not (cc.done()) for cc in consumers)
+
+        # Alert if number of consumers decreased by half
+        if num_active < num_initial_consumers // 2:
+            log(
+                WARN,
+                "Number of active workers has more than halved: (%i/%i active)",
+                num_active,
+                num_initial_consumers,
+            )
+
+        # Break if consumers died
+        if num_active == 0:
+            raise RuntimeError("All workers have died. Ending Simulation.")
+
+        # Log some stats
+        log(
+            DEBUG,
+            "Simulation Engine stats: "
+            "Active workers: (%i/%i) | %s (%i workers) | Tasks in queue: %i)",
+            num_active,
+            num_initial_consumers,
+            backend.__class__.__name__,
+            backend.num_workers,
+            queue.qsize(),
+        )
+        await asyncio.sleep(1.0)
+    log(DEBUG, "Async producer: Stopped pulling from StateFactory.")
+
+
+async def run(
+    app_fn: Callable[[], ClientApp],
+    backend_fn: Callable[[], Backend],
+    nodes_mapping: NodeToPartitionMapping,
+    state_factory: StateFactory,
+    node_states: Dict[int, NodeState],
+    f_stop: asyncio.Event,
+) -> None:
+    """Run the VCE async."""
+    queue: "asyncio.Queue[TaskIns]" = asyncio.Queue(128)
+
+    try:
+
+        # Instantiate backend
+        backend = backend_fn()
+
+        # Build backend
+        await backend.build()
+
+        # Add workers (they submit Messages to Backend)
+        worker_tasks = [
+            asyncio.create_task(
+                worker(
+                    app_fn, queue, node_states, state_factory, nodes_mapping, backend
+                )
+            )
+            for _ in range(backend.num_workers)
+        ]
+        # Create producer (adds TaskIns into Queue)
+        producer = asyncio.create_task(
+            add_taskins_to_queue(
+                queue, state_factory, nodes_mapping, backend, worker_tasks, f_stop
+            )
+        )
+
+        # Wait for producer to finish
+        # The producer runs forever until f_stop is set or until
+        # all worker (consumer) coroutines are completed. Workers
+        # also run forever and only end if an exception is raised.
+        await asyncio.gather(producer)
+
+    except Exception as ex:
+
+        log(ERROR, "An exception occured!! %s", ex)
+        log(ERROR, traceback.format_exc())
+        log(WARN, "Stopping Simulation Engine.")
+
+        # Manually trigger stopping event
+        f_stop.set()
+
+        # Raise exception
+        raise RuntimeError("Simulation Engine crashed.") from ex
+
+    finally:
+        # Produced task terminated, now cancel worker tasks
+        for w_t in worker_tasks:
+            _ = w_t.cancel()
+
+        while not all(w_t.done() for w_t in worker_tasks):
+            log(DEBUG, "Terminating async workers...")
+            await asyncio.sleep(0.5)
+
+        await asyncio.gather(*[w_t for w_t in worker_tasks if not w_t.done()])
+
+        # Terminate backend
+        await backend.terminate()
+
+
+# pylint: disable=too-many-arguments,unused-argument,too-many-locals
 def start_vce(
-    num_supernodes: int,
     client_app_module_name: str,
     backend_name: str,
     backend_config_json_stream: str,
-    state_factory: StateFactory,
     working_dir: str,
-    f_stop: Optional[asyncio.Event] = None,
+    f_stop: asyncio.Event,
+    num_supernodes: Optional[int] = None,
+    state_factory: Optional[StateFactory] = None,
+    existing_nodes_mapping: Optional[NodeToPartitionMapping] = None,
 ) -> None:
-    """Start Fleet API with the VirtualClientEngine (VCE)."""
-    # Register SuperNodes
-    nodes_mapping = _register_nodes(
-        num_nodes=num_supernodes, state_factory=state_factory
-    )
+    """Start Fleet API with the Simulation Engine."""
+    if num_supernodes is not None and existing_nodes_mapping is not None:
+        raise ValueError(
+            "Both `num_supernodes` and `existing_nodes_mapping` are provided, "
+            "but only one is allowed."
+        )
+    if num_supernodes is None:
+        if state_factory is None or existing_nodes_mapping is None:
+            raise ValueError(
+                "If not passing an existing `state_factory` and associated "
+                "`existing_nodes_mapping` you must supply `num_supernodes` to indicate "
+                "how many nodes to insert into a new StateFactory that will be created."
+            )
+    if existing_nodes_mapping:
+        if state_factory is None:
+            raise ValueError(
+                "`existing_nodes_mapping` was passed, but no `state_factory` was "
+                "passed."
+            )
+        log(INFO, "Using exiting NodeToPartitionMapping and StateFactory.")
+        # Use mapping constructed externally. This also means nodes
+        # have previously being registered.
+        nodes_mapping = existing_nodes_mapping
+
+    if not state_factory:
+        log(INFO, "A StateFactory was not supplied to the SimulationEngine.")
+        # Create an empty in-memory state factory
+        state_factory = StateFactory(":flwr-in-memory-state:")
+        log(INFO, "Created new %s.", state_factory.__class__.__name__)
+
+    if num_supernodes:
+        # Register SuperNodes
+        nodes_mapping = _register_nodes(
+            num_nodes=num_supernodes, state_factory=state_factory
+        )
 
     # Construct mapping of NodeStates
     node_states: Dict[int, NodeState] = {}
@@ -69,7 +275,6 @@ def start_vce(
 
     try:
         backend_type = supported_backends[backend_name]
-        _ = backend_type(backend_config, work_dir=working_dir)
     except KeyError as ex:
         log(
             ERROR,
@@ -83,10 +288,25 @@ def start_vce(
 
         raise ex
 
+    def backend_fn() -> Backend:
+        """Instantiate a Backend."""
+        return backend_type(backend_config, work_dir=working_dir)
+
     log(INFO, "client_app_module_name = %s", client_app_module_name)
 
     def _load() -> ClientApp:
         app: ClientApp = load_client_app(client_app_module_name)
         return app
 
-    # start backend
+    app_fn = _load
+
+    asyncio.run(
+        run(
+            app_fn,
+            backend_fn,
+            nodes_mapping,
+            state_factory,
+            node_states,
+            f_stop,
+        )
+    )
diff --git a/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py b/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py
new file mode 100644
index 000000000000..ea2de2e636ba
--- /dev/null
+++ b/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py
@@ -0,0 +1,292 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test Fleet Simulation Engine API."""
+
+
+import asyncio
+import threading
+from itertools import cycle
+from json import JSONDecodeError
+from math import pi
+from pathlib import Path
+from time import sleep
+from typing import Dict, Optional, Set, Tuple
+from unittest import IsolatedAsyncioTestCase
+from uuid import UUID
+
+from flwr.common import GetPropertiesIns, Message, Metadata
+from flwr.common.constant import MESSAGE_TYPE_GET_PROPERTIES
+from flwr.common.recordset_compat import getpropertiesins_to_recordset
+from flwr.common.serde import message_from_taskres, message_to_taskins
+from flwr.server.superlink.fleet.vce.vce_api import (
+    NodeToPartitionMapping,
+    _register_nodes,
+    start_vce,
+)
+from flwr.server.superlink.state import InMemoryState, StateFactory
+
+
+def terminate_simulation(f_stop: asyncio.Event, sleep_duration: int) -> None:
+    """Set event to terminate Simulation Engine after `sleep_duration` seconds."""
+    sleep(sleep_duration)
+    f_stop.set()
+
+
+def init_state_factory_nodes_mapping(
+    num_nodes: int,
+    num_messages: int,
+    erroneous_message: Optional[bool] = False,
+) -> Tuple[StateFactory, NodeToPartitionMapping, Dict[UUID, float]]:
+    """Instatiate StateFactory, register nodes and pre-insert messages in the state."""
+    # Register a state and a run_id in it
+    run_id = 1234
+    state_factory = StateFactory(":flwr-in-memory-state:")
+
+    # Register a few nodes
+    nodes_mapping = _register_nodes(num_nodes=num_nodes, state_factory=state_factory)
+
+    expected_results = register_messages_into_state(
+        state_factory=state_factory,
+        nodes_mapping=nodes_mapping,
+        run_id=run_id,
+        num_messages=num_messages,
+        erroneous_message=erroneous_message,
+    )
+    return state_factory, nodes_mapping, expected_results
+
+
+# pylint: disable=too-many-locals
+def register_messages_into_state(
+    state_factory: StateFactory,
+    nodes_mapping: NodeToPartitionMapping,
+    run_id: int,
+    num_messages: int,
+    erroneous_message: Optional[bool] = False,
+) -> Dict[UUID, float]:
+    """Register `num_messages` into the state factory."""
+    state: InMemoryState = state_factory.state()  # type: ignore
+    state.run_ids.add(run_id)
+    # Artificially add TaskIns to state so they can be processed
+    # by the Simulation Engine logic
+    nodes_cycle = cycle(nodes_mapping.keys())  # we have more messages than supernodes
+    task_ids: Set[UUID] = set()  # so we can retrieve them later
+    expected_results = {}
+    for i in range(num_messages):
+        dst_node_id = next(nodes_cycle)
+        # Construct a Message
+        mult_factor = 2024 + i
+        getproperties_ins = GetPropertiesIns(config={"factor": mult_factor})
+        recordset = getpropertiesins_to_recordset(getproperties_ins)
+        message = Message(
+            content=recordset,
+            metadata=Metadata(
+                run_id=run_id,
+                message_id="",
+                group_id="",
+                src_node_id=0,
+                dst_node_id=dst_node_id,  # indicate destination node
+                reply_to_message="",
+                ttl="",
+                message_type=(
+                    "a bad message"
+                    if erroneous_message
+                    else MESSAGE_TYPE_GET_PROPERTIES
+                ),
+            ),
+        )
+        # Convert Message to TaskIns
+        taskins = message_to_taskins(message)
+        # Instert in state
+        task_id = state.store_task_ins(taskins)
+        if task_id:
+            # Add to UUID set
+            task_ids.add(task_id)
+            # Store expected output for check later on
+            expected_results[task_id] = mult_factor * pi
+
+    return expected_results
+
+
+def _autoresolve_working_dir(rel_client_app_dir: str = "backend") -> str:
+    """Correctly resolve working directory."""
+    file_path = Path(__file__)
+    working_dir = Path.cwd()
+    rel_workdir = file_path.relative_to(working_dir)
+
+    # Susbtract lats element and append "backend/test" (wher the client module is.)
+    return str(rel_workdir.parent / rel_client_app_dir)
+
+
+# pylint: disable=too-many-arguments
+def start_and_shutdown(
+    backend: str = "ray",
+    clientapp_module: str = "raybackend_test:client_app",
+    working_dir: str = "",
+    num_supernodes: Optional[int] = None,
+    state_factory: Optional[StateFactory] = None,
+    nodes_mapping: Optional[NodeToPartitionMapping] = None,
+    duration: int = 0,
+    backend_config: str = "{}",
+) -> None:
+    """Start Simulation Engine and terminate after specified number of seconds.
+
+    Some tests need to be terminated by triggering externally an asyncio.Event. This
+    is enabled whtn passing `duration`>0.
+    """
+    f_stop = asyncio.Event()
+
+    if duration:
+
+        # Setup thread that will set the f_stop event, triggering the termination of all
+        # asyncio logic in the Simulation Engine. It will also terminate the Backend.
+        termination_th = threading.Thread(
+            target=terminate_simulation, args=(f_stop, duration)
+        )
+        termination_th.start()
+
+    # Resolve working directory if not passed
+    if not working_dir:
+        working_dir = _autoresolve_working_dir()
+
+    start_vce(
+        num_supernodes=num_supernodes,
+        client_app_module_name=clientapp_module,
+        backend_name=backend,
+        backend_config_json_stream=backend_config,
+        state_factory=state_factory,
+        working_dir=working_dir,
+        f_stop=f_stop,
+        existing_nodes_mapping=nodes_mapping,
+    )
+
+    if duration:
+        termination_th.join()
+
+
+class AsyncTestFleetSimulationEngineRayBackend(IsolatedAsyncioTestCase):
+    """A basic class that enables testing asyncio functionalities."""
+
+    def test_erroneous_no_supernodes_client_mapping(self) -> None:
+        """Test with unset arguments."""
+        with self.assertRaises(ValueError):
+            start_and_shutdown(duration=2)
+
+    def test_erroneous_clientapp_module_name(self) -> None:
+        """Tests attempt to load a ClientApp that can't be found."""
+        num_messages = 7
+        num_nodes = 59
+
+        state_factory, nodes_mapping, _ = init_state_factory_nodes_mapping(
+            num_nodes=num_nodes, num_messages=num_messages
+        )
+        with self.assertRaises(RuntimeError):
+            start_and_shutdown(
+                clientapp_module="totally_fictitious_app:client",
+                state_factory=state_factory,
+                nodes_mapping=nodes_mapping,
+            )
+
+    def test_erroneous_messages(self) -> None:
+        """Test handling of error in async worker (consumer).
+
+        We register messages which will trigger an error when handling, triggering an
+        error.
+        """
+        num_messages = 100
+        num_nodes = 59
+
+        state_factory, nodes_mapping, _ = init_state_factory_nodes_mapping(
+            num_nodes=num_nodes, num_messages=num_messages, erroneous_message=True
+        )
+
+        with self.assertRaises(RuntimeError):
+            start_and_shutdown(
+                state_factory=state_factory,
+                nodes_mapping=nodes_mapping,
+            )
+
+    def test_erroneous_backend_config(self) -> None:
+        """Backend Config should be a JSON stream."""
+        with self.assertRaises(JSONDecodeError):
+            start_and_shutdown(num_supernodes=50, backend_config="not a proper config")
+
+    def test_with_nonexistent_backend(self) -> None:
+        """Test specifying a backend that does not exist."""
+        with self.assertRaises(KeyError):
+            start_and_shutdown(num_supernodes=50, backend="this-backend-does-not-exist")
+
+    def test_erroneous_arguments_num_supernodes_and_existing_mapping(self) -> None:
+        """Test ValueError if a node mapping is passed but also num_supernodes.
+
+        Passing `num_supernodes` does nothing since we assume that if a node mapping
+        is supplied, nodes have been registered externally already. Therefore passing
+        `num_supernodes` might give the impression that that many nodes will be
+        registered. We don't do that since a mapping already exists.
+        """
+        with self.assertRaises(ValueError):
+            start_and_shutdown(num_supernodes=50, nodes_mapping={0: 1})
+
+    def test_erroneous_arguments_existing_mapping_but_no_state_factory(self) -> None:
+        """Test ValueError if a node mapping is passed but no state.
+
+        Passing a node mapping indicates that (externally) nodes have registered with a
+        state factory. Therefore, that state factory should be passed too.
+        """
+        with self.assertRaises(ValueError):
+            start_and_shutdown(nodes_mapping={0: 1})
+
+    def test_start_and_shutdown(self) -> None:
+        """Start Simulation Engine Fleet and terminate it."""
+        start_and_shutdown(num_supernodes=50, duration=10)
+
+    # pylint: disable=too-many-locals
+    def test_start_and_shutdown_with_tasks_in_state(self) -> None:
+        """Run Simulation Engine with some TasksIns in State.
+
+        This test creates a few nodes and submits a few messages that need to be
+        executed by the Backend. In order for that to happen the asyncio
+        producer/consumer logic must function. This also severs to evaluate a valid
+        ClientApp.
+        """
+        num_messages = 229
+        num_nodes = 59
+
+        state_factory, nodes_mapping, expected_results = (
+            init_state_factory_nodes_mapping(
+                num_nodes=num_nodes, num_messages=num_messages
+            )
+        )
+
+        # Run
+        start_and_shutdown(
+            state_factory=state_factory, nodes_mapping=nodes_mapping, duration=10
+        )
+
+        # Get all TaskRes
+        state = state_factory.state()
+        task_ids = set(expected_results.keys())
+        task_res_list = state.get_task_res(task_ids=task_ids, limit=len(task_ids))
+
+        # Check results by first converting to Message
+        for task_res in task_res_list:
+
+            message = message_from_taskres(task_res)
+
+            # Verify message content is as expected
+            content = message.content
+            assert (
+                content.configs_records["getpropertiesres.properties"]["result"]
+                == expected_results[UUID(task_res.task.ancestry[0])]
+            )

From d9d753332c09046e94cd9cdcda1da7d858c605f4 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Wed, 28 Feb 2024 19:31:19 +0100
Subject: [PATCH 076/102] Move `run_driver_api_grpc` to a new module (#3031)

---
 src/py/flwr/server/app.py                     | 33 ++----------
 .../server/superlink/driver/driver_grpc.py    | 54 +++++++++++++++++++
 2 files changed, 57 insertions(+), 30 deletions(-)
 create mode 100644 src/py/flwr/server/superlink/driver/driver_grpc.py

diff --git a/src/py/flwr/server/app.py b/src/py/flwr/server/app.py
index cf6b716bd189..788ebeb8a454 100644
--- a/src/py/flwr/server/app.py
+++ b/src/py/flwr/server/app.py
@@ -36,9 +36,6 @@
 )
 from flwr.common.exit_handlers import register_exit_handlers
 from flwr.common.logger import log
-from flwr.proto.driver_pb2_grpc import (  # pylint: disable=E0611
-    add_DriverServicer_to_server,
-)
 from flwr.proto.fleet_pb2_grpc import (  # pylint: disable=E0611
     add_FleetServicer_to_server,
 )
@@ -48,7 +45,7 @@
 from .server import Server, init_defaults, run_fl
 from .server_config import ServerConfig
 from .strategy import Strategy
-from .superlink.driver.driver_servicer import DriverServicer
+from .superlink.driver.driver_grpc import run_driver_api_grpc
 from .superlink.fleet.grpc_bidi.grpc_server import (
     generic_create_grpc_server,
     start_grpc_server,
@@ -204,7 +201,7 @@ def run_driver_api() -> None:
     state_factory = StateFactory(args.database)
 
     # Start server
-    grpc_server: grpc.Server = _run_driver_api_grpc(
+    grpc_server: grpc.Server = run_driver_api_grpc(
         address=address,
         state_factory=state_factory,
         certificates=certificates,
@@ -313,7 +310,7 @@ def run_superlink() -> None:
     state_factory = StateFactory(args.database)
 
     # Start Driver API
-    driver_server: grpc.Server = _run_driver_api_grpc(
+    driver_server: grpc.Server = run_driver_api_grpc(
         address=address,
         state_factory=state_factory,
         certificates=certificates,
@@ -414,30 +411,6 @@ def _try_obtain_certificates(
     return certificates
 
 
-def _run_driver_api_grpc(
-    address: str,
-    state_factory: StateFactory,
-    certificates: Optional[Tuple[bytes, bytes, bytes]],
-) -> grpc.Server:
-    """Run Driver API (gRPC, request-response)."""
-    # Create Driver API gRPC server
-    driver_servicer: grpc.Server = DriverServicer(
-        state_factory=state_factory,
-    )
-    driver_add_servicer_to_server_fn = add_DriverServicer_to_server
-    driver_grpc_server = generic_create_grpc_server(
-        servicer_and_add_fn=(driver_servicer, driver_add_servicer_to_server_fn),
-        server_address=address,
-        max_message_length=GRPC_MAX_MESSAGE_LENGTH,
-        certificates=certificates,
-    )
-
-    log(INFO, "Flower ECE: Starting Driver API (gRPC-rere) on %s", address)
-    driver_grpc_server.start()
-
-    return driver_grpc_server
-
-
 def _run_fleet_api_grpc_rere(
     address: str,
     state_factory: StateFactory,
diff --git a/src/py/flwr/server/superlink/driver/driver_grpc.py b/src/py/flwr/server/superlink/driver/driver_grpc.py
new file mode 100644
index 000000000000..f74000bc59c4
--- /dev/null
+++ b/src/py/flwr/server/superlink/driver/driver_grpc.py
@@ -0,0 +1,54 @@
+# Copyright 2020 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Driver gRPC API."""
+
+from logging import INFO
+from typing import Optional, Tuple
+
+import grpc
+
+from flwr.common import GRPC_MAX_MESSAGE_LENGTH
+from flwr.common.logger import log
+from flwr.proto.driver_pb2_grpc import (  # pylint: disable=E0611
+    add_DriverServicer_to_server,
+)
+from flwr.server.superlink.state import StateFactory
+
+from ..fleet.grpc_bidi.grpc_server import generic_create_grpc_server
+from .driver_servicer import DriverServicer
+
+
+def run_driver_api_grpc(
+    address: str,
+    state_factory: StateFactory,
+    certificates: Optional[Tuple[bytes, bytes, bytes]],
+) -> grpc.Server:
+    """Run Driver API (gRPC, request-response)."""
+    # Create Driver API gRPC server
+    driver_servicer: grpc.Server = DriverServicer(
+        state_factory=state_factory,
+    )
+    driver_add_servicer_to_server_fn = add_DriverServicer_to_server
+    driver_grpc_server = generic_create_grpc_server(
+        servicer_and_add_fn=(driver_servicer, driver_add_servicer_to_server_fn),
+        server_address=address,
+        max_message_length=GRPC_MAX_MESSAGE_LENGTH,
+        certificates=certificates,
+    )
+
+    log(INFO, "Flower ECE: Starting Driver API (gRPC-rere) on %s", address)
+    driver_grpc_server.start()
+
+    return driver_grpc_server

From 42f13add36f5d11448b030b85a26eb00f798dbb4 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Wed, 28 Feb 2024 20:23:52 +0100
Subject: [PATCH 077/102] Add MONAI quickstart example (#2773)

Co-authored-by: jafermarq <javier@flower.ai>
---
 README.md                                  |   3 +-
 examples/quickstart-monai/.gitignore       |   1 +
 examples/quickstart-monai/README.md        |  85 +++++++++++
 examples/quickstart-monai/client.py        |  61 ++++++++
 examples/quickstart-monai/data.py          | 158 +++++++++++++++++++++
 examples/quickstart-monai/model.py         |  33 +++++
 examples/quickstart-monai/pyproject.toml   |  19 +++
 examples/quickstart-monai/requirements.txt |   7 +
 examples/quickstart-monai/run.sh           |  19 +++
 examples/quickstart-monai/server.py        |  25 ++++
 10 files changed, 410 insertions(+), 1 deletion(-)
 create mode 100644 examples/quickstart-monai/.gitignore
 create mode 100644 examples/quickstart-monai/README.md
 create mode 100644 examples/quickstart-monai/client.py
 create mode 100644 examples/quickstart-monai/data.py
 create mode 100644 examples/quickstart-monai/model.py
 create mode 100644 examples/quickstart-monai/pyproject.toml
 create mode 100644 examples/quickstart-monai/requirements.txt
 create mode 100755 examples/quickstart-monai/run.sh
 create mode 100644 examples/quickstart-monai/server.py

diff --git a/README.md b/README.md
index b5c8bfdaa4b9..dee42bd5b733 100644
--- a/README.md
+++ b/README.md
@@ -33,7 +33,7 @@ design of Flower is based on a few guiding principles:
 
 - **Framework-agnostic**: Different machine learning frameworks have different
   strengths. Flower can be used with any machine learning framework, for
-  example, [PyTorch](https://pytorch.org), [TensorFlow](https://tensorflow.org), [Hugging Face Transformers](https://huggingface.co/), [PyTorch Lightning](https://pytorchlightning.ai/), [scikit-learn](https://scikit-learn.org/), [JAX](https://jax.readthedocs.io/), [TFLite](https://tensorflow.org/lite/), [fastai](https://www.fast.ai/), [MLX](https://ml-explore.github.io/mlx/build/html/index.html), [XGBoost](https://xgboost.readthedocs.io/en/stable/), [Pandas](https://pandas.pydata.org/) for federated analytics, or even raw [NumPy](https://numpy.org/)
+  example, [PyTorch](https://pytorch.org), [TensorFlow](https://tensorflow.org), [Hugging Face Transformers](https://huggingface.co/), [PyTorch Lightning](https://pytorchlightning.ai/), [scikit-learn](https://scikit-learn.org/), [JAX](https://jax.readthedocs.io/), [TFLite](https://tensorflow.org/lite/), [MONAI](https://docs.monai.io/en/latest/index.html), [fastai](https://www.fast.ai/), [MLX](https://ml-explore.github.io/mlx/build/html/index.html), [XGBoost](https://xgboost.readthedocs.io/en/stable/), [Pandas](https://pandas.pydata.org/) for federated analytics, or even raw [NumPy](https://numpy.org/)
   for users who enjoy computing gradients by hand.
 
 - **Understandable**: Flower is written with maintainability in mind. The
@@ -130,6 +130,7 @@ Quickstart examples:
 - [Quickstart (fastai)](https://github.com/adap/flower/tree/main/examples/quickstart-fastai)
 - [Quickstart (Pandas)](https://github.com/adap/flower/tree/main/examples/quickstart-pandas)
 - [Quickstart (JAX)](https://github.com/adap/flower/tree/main/examples/quickstart-jax)
+- [Quickstart (MONAI)](https://github.com/adap/flower/tree/main/examples/quickstart-monai)
 - [Quickstart (scikit-learn)](https://github.com/adap/flower/tree/main/examples/sklearn-logreg-mnist)
 - [Quickstart (XGBoost)](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart)
 - [Quickstart (Android [TFLite])](https://github.com/adap/flower/tree/main/examples/android)
diff --git a/examples/quickstart-monai/.gitignore b/examples/quickstart-monai/.gitignore
new file mode 100644
index 000000000000..a218cab9669e
--- /dev/null
+++ b/examples/quickstart-monai/.gitignore
@@ -0,0 +1 @@
+MedNIST*
diff --git a/examples/quickstart-monai/README.md b/examples/quickstart-monai/README.md
new file mode 100644
index 000000000000..4a9afef4f86a
--- /dev/null
+++ b/examples/quickstart-monai/README.md
@@ -0,0 +1,85 @@
+# Flower Example using MONAI
+
+This introductory example to Flower uses MONAI, but deep knowledge of MONAI is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case.
+Running this example in itself is quite easy.
+
+[MONAI](https://docs.monai.io/en/latest/index.html)(Medical Open Network for AI) is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of the PyTorch Ecosystem.
+
+Its ambitions are:
+
+- developing a community of academic, industrial and clinical researchers collaborating on a common foundation;
+
+- creating state-of-the-art, end-to-end training workflows for healthcare imaging;
+
+- providing researchers with an optimized and standardized way to create and evaluate deep learning models.
+
+## Project Setup
+
+Start by cloning the example project. We prepared a single-line command that you can copy into your shell which will checkout the example for you:
+
+```shell
+git clone --depth=1 https://github.com/adap/flower.git _tmp && mv _tmp/examples/quickstart-monai . && rm -rf _tmp && cd quickstart-monai
+```
+
+This will create a new directory called `quickstart-monai` containing the following files:
+
+```shell
+-- pyproject.toml
+-- requirements.txt
+-- client.py
+-- data.py
+-- model.py
+-- server.py
+-- README.md
+```
+
+### Installing Dependencies
+
+Project dependencies (such as `monai` and `flwr`) are defined in `pyproject.toml` and `requirements.txt`. We recommend [Poetry](https://python-poetry.org/docs/) to install those dependencies and manage your virtual environment ([Poetry installation](https://python-poetry.org/docs/#installation)) or [pip](https://pip.pypa.io/en/latest/development/), but feel free to use a different way of installing dependencies and managing virtual environments if you have other preferences.
+
+#### Poetry
+
+```shell
+poetry install
+poetry shell
+```
+
+Poetry will install all your dependencies in a newly created virtual environment. To verify that everything works correctly you can run the following command:
+
+```shell
+poetry run python3 -c "import flwr"
+```
+
+If you don't see any errors you're good to go!
+
+#### pip
+
+Write the command below in your terminal to install the dependencies according to the configuration file requirements.txt.
+
+```shell
+pip install -r requirements.txt
+```
+
+## Run Federated Learning with MONAI and Flower
+
+Afterwards you are ready to start the Flower server as well as the clients. You can simply start the server in a terminal as follows:
+
+```shell
+python3 server.py
+```
+
+Now you are ready to start the Flower clients which will participate in the learning. To do so simply open two more terminal windows and run the following commands.  Clients will train a [DenseNet121](https://docs.monai.io/en/stable/networks.html#densenet121) from MONAI. If a GPU is present in your system, clients will use it.
+
+Start client 1 in the first terminal:
+
+```shell
+python3 client.py --partition-id 0
+```
+
+Start client 2 in the second terminal:
+
+```shell
+python3 client.py --partition-id 1
+```
+
+You will see that the federated training is starting. Look at the [code](https://github.com/adap/flower/tree/main/examples/quickstart-monai) for a detailed explanation.
diff --git a/examples/quickstart-monai/client.py b/examples/quickstart-monai/client.py
new file mode 100644
index 000000000000..0ed943da83cc
--- /dev/null
+++ b/examples/quickstart-monai/client.py
@@ -0,0 +1,61 @@
+import argparse
+import warnings
+from collections import OrderedDict
+
+import torch
+from data import load_data
+from model import test, train
+from monai.networks.nets.densenet import DenseNet121
+
+import flwr as fl
+
+warnings.filterwarnings("ignore", category=UserWarning)
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+# Define Flower client
+class FlowerClient(fl.client.NumPyClient):
+    def __init__(self, net, trainloader, testloader, device):
+        self.net = net
+        self.trainloader = trainloader
+        self.testloader = testloader
+        self.device = device
+
+    def get_parameters(self, config):
+        return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
+
+    def set_parameters(self, parameters):
+        params_dict = zip(self.net.state_dict().keys(), parameters)
+        state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
+        self.net.load_state_dict(state_dict, strict=True)
+
+    def fit(self, parameters, config):
+        self.set_parameters(parameters)
+        train(self.net, self.trainloader, epoch_num=1, device=self.device)
+        return self.get_parameters(config={}), len(self.trainloader), {}
+
+    def evaluate(self, parameters, config):
+        self.set_parameters(parameters)
+        loss, accuracy = test(self.net, self.testloader, self.device)
+        return loss, len(self.testloader), {"accuracy": accuracy}
+
+
+if __name__ == "__main__":
+    total_partitions = 10
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--partition-id", type=int, choices=range(total_partitions), required=True
+    )
+    args = parser.parse_args()
+
+    # Load model and data (simple CNN, CIFAR-10)
+    trainloader, _, testloader, num_class = load_data(
+        total_partitions, args.partition_id
+    )
+    net = DenseNet121(spatial_dims=2, in_channels=1, out_channels=num_class).to(DEVICE)
+
+    # Start Flower client
+    fl.client.start_numpy_client(
+        server_address="127.0.0.1:8080",
+        client=FlowerClient(net, trainloader, testloader, DEVICE),
+    )
diff --git a/examples/quickstart-monai/data.py b/examples/quickstart-monai/data.py
new file mode 100644
index 000000000000..d184476522e8
--- /dev/null
+++ b/examples/quickstart-monai/data.py
@@ -0,0 +1,158 @@
+import os
+import tarfile
+from urllib import request
+
+import numpy as np
+from monai.data import DataLoader, Dataset
+from monai.transforms import (
+    Compose,
+    EnsureChannelFirst,
+    LoadImage,
+    RandFlip,
+    RandRotate,
+    RandZoom,
+    ScaleIntensity,
+    ToTensor,
+)
+
+
+def _partition(files_list, labels_list, num_shards, index):
+    total_size = len(files_list)
+    assert total_size == len(
+        labels_list
+    ), f"List of datapoints and labels must be of the same length"
+    shard_size = total_size // num_shards
+
+    # Calculate start and end indices for the shard
+    start_idx = index * shard_size
+    if index == num_shards - 1:
+        # Last shard takes the remainder
+        end_idx = total_size
+    else:
+        end_idx = start_idx + shard_size
+
+    # Create a subset for the shard
+    files = files_list[start_idx:end_idx]
+    labels = labels_list[start_idx:end_idx]
+    return files, labels
+
+
+def load_data(num_shards, index):
+    image_file_list, image_label_list, _, num_class = _download_data()
+
+    # Get partition given index
+    files_list, labels_list = _partition(
+        image_file_list, image_label_list, num_shards, index
+    )
+
+    trainX, trainY, valX, valY, testX, testY = _split_data(
+        files_list, labels_list, len(files_list)
+    )
+    train_transforms, val_transforms = _get_transforms()
+
+    train_ds = MedNISTDataset(trainX, trainY, train_transforms)
+    train_loader = DataLoader(train_ds, batch_size=300, shuffle=True)
+
+    val_ds = MedNISTDataset(valX, valY, val_transforms)
+    val_loader = DataLoader(val_ds, batch_size=300)
+
+    test_ds = MedNISTDataset(testX, testY, val_transforms)
+    test_loader = DataLoader(test_ds, batch_size=300)
+
+    return train_loader, val_loader, test_loader, num_class
+
+
+class MedNISTDataset(Dataset):
+    def __init__(self, image_files, labels, transforms):
+        self.image_files = image_files
+        self.labels = labels
+        self.transforms = transforms
+
+    def __len__(self):
+        return len(self.image_files)
+
+    def __getitem__(self, index):
+        return self.transforms(self.image_files[index]), self.labels[index]
+
+
+def _download_data():
+    data_dir = "./MedNIST/"
+    _download_and_extract(
+        "https://dl.dropboxusercontent.com/s/5wwskxctvcxiuea/MedNIST.tar.gz",
+        os.path.join(data_dir),
+    )
+
+    class_names = sorted(
+        [x for x in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir, x))]
+    )
+    num_class = len(class_names)
+    image_files = [
+        [
+            os.path.join(data_dir, class_name, x)
+            for x in os.listdir(os.path.join(data_dir, class_name))
+        ]
+        for class_name in class_names
+    ]
+    image_file_list = []
+    image_label_list = []
+    for i, class_name in enumerate(class_names):
+        image_file_list.extend(image_files[i])
+        image_label_list.extend([i] * len(image_files[i]))
+    num_total = len(image_label_list)
+    return image_file_list, image_label_list, num_total, num_class
+
+
+def _split_data(image_file_list, image_label_list, num_total):
+    valid_frac, test_frac = 0.1, 0.1
+    trainX, trainY = [], []
+    valX, valY = [], []
+    testX, testY = [], []
+
+    for i in range(num_total):
+        rann = np.random.random()
+        if rann < valid_frac:
+            valX.append(image_file_list[i])
+            valY.append(image_label_list[i])
+        elif rann < test_frac + valid_frac:
+            testX.append(image_file_list[i])
+            testY.append(image_label_list[i])
+        else:
+            trainX.append(image_file_list[i])
+            trainY.append(image_label_list[i])
+
+    return trainX, trainY, valX, valY, testX, testY
+
+
+def _get_transforms():
+    train_transforms = Compose(
+        [
+            LoadImage(image_only=True),
+            EnsureChannelFirst(),
+            ScaleIntensity(),
+            RandRotate(range_x=15, prob=0.5, keep_size=True),
+            RandFlip(spatial_axis=0, prob=0.5),
+            RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5, keep_size=True),
+            ToTensor(),
+        ]
+    )
+
+    val_transforms = Compose(
+        [LoadImage(image_only=True), EnsureChannelFirst(), ScaleIntensity(), ToTensor()]
+    )
+
+    return train_transforms, val_transforms
+
+
+def _download_and_extract(url, dest_folder):
+    if not os.path.isdir(dest_folder):
+        # Download the tar.gz file
+        tar_gz_filename = url.split("/")[-1]
+        if not os.path.isfile(tar_gz_filename):
+            with request.urlopen(url) as response, open(
+                tar_gz_filename, "wb"
+            ) as out_file:
+                out_file.write(response.read())
+
+        # Extract the tar.gz file
+        with tarfile.open(tar_gz_filename, "r:gz") as tar_ref:
+            tar_ref.extractall()
diff --git a/examples/quickstart-monai/model.py b/examples/quickstart-monai/model.py
new file mode 100644
index 000000000000..4c74d50553e4
--- /dev/null
+++ b/examples/quickstart-monai/model.py
@@ -0,0 +1,33 @@
+import torch
+
+
+def train(model, train_loader, epoch_num, device):
+    loss_function = torch.nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(model.parameters(), 1e-5)
+    for _ in range(epoch_num):
+        model.train()
+        for inputs, labels in train_loader:
+            optimizer.zero_grad()
+            loss_function(model(inputs.to(device)), labels.to(device)).backward()
+            optimizer.step()
+
+
+def test(model, test_loader, device):
+    model.eval()
+    loss = 0.0
+    y_true = list()
+    y_pred = list()
+    loss_function = torch.nn.CrossEntropyLoss()
+    with torch.no_grad():
+        for test_images, test_labels in test_loader:
+            out = model(test_images.to(device))
+            test_labels = test_labels.to(device)
+            loss += loss_function(out, test_labels).item()
+            pred = out.argmax(dim=1)
+            for i in range(len(pred)):
+                y_true.append(test_labels[i].item())
+                y_pred.append(pred[i].item())
+    accuracy = sum([1 if t == p else 0 for t, p in zip(y_true, y_pred)]) / len(
+        test_loader.dataset
+    )
+    return loss, accuracy
diff --git a/examples/quickstart-monai/pyproject.toml b/examples/quickstart-monai/pyproject.toml
new file mode 100644
index 000000000000..66a56ee2270b
--- /dev/null
+++ b/examples/quickstart-monai/pyproject.toml
@@ -0,0 +1,19 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "quickstart-monai"
+version = "0.1.0"
+description = "MONAI Federated Learning Quickstart with Flower"
+authors = ["The Flower Authors <hello@flower.dev>"]
+
+[tool.poetry.dependencies]
+python = ">=3.8,<3.11"
+flwr = ">=1.0,<2.0"
+torch = "1.13.1"
+tqdm = "4.65.0"
+scikit-learn = "1.3.1"
+monai = { version = "1.3.0", extras=["gdown", "nibabel", "tqdm", "itk"] }
+numpy = "1.24.4"
+pillow = "10.2.0"
diff --git a/examples/quickstart-monai/requirements.txt b/examples/quickstart-monai/requirements.txt
new file mode 100644
index 000000000000..e3f1e463c629
--- /dev/null
+++ b/examples/quickstart-monai/requirements.txt
@@ -0,0 +1,7 @@
+flwr>=1.0, <2.0
+torch==1.13.1
+tqdm==4.65.0
+scikit-learn==1.3.1
+monai[gdown,nibabel,tqdm,itk]==1.3.0
+numpy==1.24.4
+pillow==10.2.0
diff --git a/examples/quickstart-monai/run.sh b/examples/quickstart-monai/run.sh
new file mode 100755
index 000000000000..1da60bccb86d
--- /dev/null
+++ b/examples/quickstart-monai/run.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+set -e
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
+
+python -c "from data import _download_data; _download_data()"
+
+echo "Starting server"
+python server.py &
+sleep 3  # Sleep for 3s to give the server enough time to start
+
+for i in `seq 0 1`; do
+    echo "Starting client $i"
+    python client.py --partition-id $i &
+done
+
+# Enable CTRL+C to stop all background processes
+trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM
+# Wait for all background processes to complete
+wait
diff --git a/examples/quickstart-monai/server.py b/examples/quickstart-monai/server.py
new file mode 100644
index 000000000000..fe691a88aba0
--- /dev/null
+++ b/examples/quickstart-monai/server.py
@@ -0,0 +1,25 @@
+from typing import List, Tuple
+
+import flwr as fl
+from flwr.common import Metrics
+
+
+# Define metric aggregation function
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+    # Multiply accuracy of each client by number of examples used
+    accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics]
+    examples = [num_examples for num_examples, _ in metrics]
+
+    # Aggregate and return custom metric (weighted average)
+    return {"accuracy": sum(accuracies) / sum(examples)}
+
+
+# Define strategy
+strategy = fl.server.strategy.FedAvg(evaluate_metrics_aggregation_fn=weighted_average)
+
+# Start Flower server
+fl.server.start_server(
+    server_address="0.0.0.0:8080",
+    config=fl.server.ServerConfig(num_rounds=3),
+    strategy=strategy,
+)

From e960a211621423052b872040c4ef1888fa531646 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Thu, 29 Feb 2024 08:00:29 +0100
Subject: [PATCH 078/102] Add `Error` protobuf definition (#3033)

---
 src/proto/flwr/proto/error.proto     | 23 +++++++++++++++++++++++
 src/proto/flwr/proto/task.proto      |  2 ++
 src/py/flwr/proto/error_pb2.py       | 26 ++++++++++++++++++++++++++
 src/py/flwr/proto/error_pb2.pyi      | 25 +++++++++++++++++++++++++
 src/py/flwr/proto/error_pb2_grpc.py  |  4 ++++
 src/py/flwr/proto/error_pb2_grpc.pyi |  4 ++++
 src/py/flwr/proto/task_pb2.py        | 15 ++++++++-------
 src/py/flwr/proto/task_pb2.pyi       |  9 +++++++--
 src/py/flwr_tool/protoc_test.py      |  2 +-
 9 files changed, 100 insertions(+), 10 deletions(-)
 create mode 100644 src/proto/flwr/proto/error.proto
 create mode 100644 src/py/flwr/proto/error_pb2.py
 create mode 100644 src/py/flwr/proto/error_pb2.pyi
 create mode 100644 src/py/flwr/proto/error_pb2_grpc.py
 create mode 100644 src/py/flwr/proto/error_pb2_grpc.pyi

diff --git a/src/proto/flwr/proto/error.proto b/src/proto/flwr/proto/error.proto
new file mode 100644
index 000000000000..a35af7f8af67
--- /dev/null
+++ b/src/proto/flwr/proto/error.proto
@@ -0,0 +1,23 @@
+// Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// ==============================================================================
+
+syntax = "proto3";
+
+package flwr.proto;
+
+message Error {
+  sint64 code = 1;
+  string reason = 2;
+}
diff --git a/src/proto/flwr/proto/task.proto b/src/proto/flwr/proto/task.proto
index 7ae0c31fded5..423df76f1335 100644
--- a/src/proto/flwr/proto/task.proto
+++ b/src/proto/flwr/proto/task.proto
@@ -20,6 +20,7 @@ package flwr.proto;
 import "flwr/proto/node.proto";
 import "flwr/proto/recordset.proto";
 import "flwr/proto/transport.proto";
+import "flwr/proto/error.proto";
 
 message Task {
   Node producer = 1;
@@ -30,6 +31,7 @@ message Task {
   repeated string ancestry = 6;
   string task_type = 7;
   RecordSet recordset = 8;
+  Error error = 9;
 }
 
 message TaskIns {
diff --git a/src/py/flwr/proto/error_pb2.py b/src/py/flwr/proto/error_pb2.py
new file mode 100644
index 000000000000..41721ae08804
--- /dev/null
+++ b/src/py/flwr/proto/error_pb2.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: flwr/proto/error.proto
+# Protobuf Python Version: 4.25.0
+"""Generated protocol buffer code."""
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf.internal import builder as _builder
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x16\x66lwr/proto/error.proto\x12\nflwr.proto\"%\n\x05\x45rror\x12\x0c\n\x04\x63ode\x18\x01 \x01(\x12\x12\x0e\n\x06reason\x18\x02 \x01(\tb\x06proto3')
+
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flwr.proto.error_pb2', _globals)
+if _descriptor._USE_C_DESCRIPTORS == False:
+  DESCRIPTOR._options = None
+  _globals['_ERROR']._serialized_start=38
+  _globals['_ERROR']._serialized_end=75
+# @@protoc_insertion_point(module_scope)
diff --git a/src/py/flwr/proto/error_pb2.pyi b/src/py/flwr/proto/error_pb2.pyi
new file mode 100644
index 000000000000..1811e5aa0ca8
--- /dev/null
+++ b/src/py/flwr/proto/error_pb2.pyi
@@ -0,0 +1,25 @@
+"""
+@generated by mypy-protobuf.  Do not edit manually!
+isort:skip_file
+"""
+import builtins
+import google.protobuf.descriptor
+import google.protobuf.message
+import typing
+import typing_extensions
+
+DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
+
+class Error(google.protobuf.message.Message):
+    DESCRIPTOR: google.protobuf.descriptor.Descriptor
+    CODE_FIELD_NUMBER: builtins.int
+    REASON_FIELD_NUMBER: builtins.int
+    code: builtins.int
+    reason: typing.Text
+    def __init__(self,
+        *,
+        code: builtins.int = ...,
+        reason: typing.Text = ...,
+        ) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["code",b"code","reason",b"reason"]) -> None: ...
+global___Error = Error
diff --git a/src/py/flwr/proto/error_pb2_grpc.py b/src/py/flwr/proto/error_pb2_grpc.py
new file mode 100644
index 000000000000..2daafffebfc8
--- /dev/null
+++ b/src/py/flwr/proto/error_pb2_grpc.py
@@ -0,0 +1,4 @@
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+"""Client and server classes corresponding to protobuf-defined services."""
+import grpc
+
diff --git a/src/py/flwr/proto/error_pb2_grpc.pyi b/src/py/flwr/proto/error_pb2_grpc.pyi
new file mode 100644
index 000000000000..f3a5a087ef5d
--- /dev/null
+++ b/src/py/flwr/proto/error_pb2_grpc.pyi
@@ -0,0 +1,4 @@
+"""
+@generated by mypy-protobuf.  Do not edit manually!
+isort:skip_file
+"""
diff --git a/src/py/flwr/proto/task_pb2.py b/src/py/flwr/proto/task_pb2.py
index 9294b27eec0e..4d5f863e88dd 100644
--- a/src/py/flwr/proto/task_pb2.py
+++ b/src/py/flwr/proto/task_pb2.py
@@ -15,19 +15,20 @@
 from flwr.proto import node_pb2 as flwr_dot_proto_dot_node__pb2
 from flwr.proto import recordset_pb2 as flwr_dot_proto_dot_recordset__pb2
 from flwr.proto import transport_pb2 as flwr_dot_proto_dot_transport__pb2
+from flwr.proto import error_pb2 as flwr_dot_proto_dot_error__pb2
 
 
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15\x66lwr/proto/task.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x1a\x66lwr/proto/recordset.proto\x1a\x1a\x66lwr/proto/transport.proto\"\xd4\x01\n\x04Task\x12\"\n\x08producer\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\"\n\x08\x63onsumer\x18\x02 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x12\n\ncreated_at\x18\x03 \x01(\t\x12\x14\n\x0c\x64\x65livered_at\x18\x04 \x01(\t\x12\x0b\n\x03ttl\x18\x05 \x01(\t\x12\x10\n\x08\x61ncestry\x18\x06 \x03(\t\x12\x11\n\ttask_type\x18\x07 \x01(\t\x12(\n\trecordset\x18\x08 \x01(\x0b\x32\x15.flwr.proto.RecordSet\"\\\n\x07TaskIns\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x0e\n\x06run_id\x18\x03 \x01(\x12\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Task\"\\\n\x07TaskRes\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x0e\n\x06run_id\x18\x03 \x01(\x12\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Taskb\x06proto3')
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15\x66lwr/proto/task.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x1a\x66lwr/proto/recordset.proto\x1a\x1a\x66lwr/proto/transport.proto\x1a\x16\x66lwr/proto/error.proto\"\xf6\x01\n\x04Task\x12\"\n\x08producer\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\"\n\x08\x63onsumer\x18\x02 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x12\n\ncreated_at\x18\x03 \x01(\t\x12\x14\n\x0c\x64\x65livered_at\x18\x04 \x01(\t\x12\x0b\n\x03ttl\x18\x05 \x01(\t\x12\x10\n\x08\x61ncestry\x18\x06 \x03(\t\x12\x11\n\ttask_type\x18\x07 \x01(\t\x12(\n\trecordset\x18\x08 \x01(\x0b\x32\x15.flwr.proto.RecordSet\x12 \n\x05\x65rror\x18\t \x01(\x0b\x32\x11.flwr.proto.Error\"\\\n\x07TaskIns\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x0e\n\x06run_id\x18\x03 \x01(\x12\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Task\"\\\n\x07TaskRes\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x0e\n\x06run_id\x18\x03 \x01(\x12\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Taskb\x06proto3')
 
 _globals = globals()
 _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
 _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flwr.proto.task_pb2', _globals)
 if _descriptor._USE_C_DESCRIPTORS == False:
   DESCRIPTOR._options = None
-  _globals['_TASK']._serialized_start=117
-  _globals['_TASK']._serialized_end=329
-  _globals['_TASKINS']._serialized_start=331
-  _globals['_TASKINS']._serialized_end=423
-  _globals['_TASKRES']._serialized_start=425
-  _globals['_TASKRES']._serialized_end=517
+  _globals['_TASK']._serialized_start=141
+  _globals['_TASK']._serialized_end=387
+  _globals['_TASKINS']._serialized_start=389
+  _globals['_TASKINS']._serialized_end=481
+  _globals['_TASKRES']._serialized_start=483
+  _globals['_TASKRES']._serialized_end=575
 # @@protoc_insertion_point(module_scope)
diff --git a/src/py/flwr/proto/task_pb2.pyi b/src/py/flwr/proto/task_pb2.pyi
index 4c8412fd691a..b9c10139cfb3 100644
--- a/src/py/flwr/proto/task_pb2.pyi
+++ b/src/py/flwr/proto/task_pb2.pyi
@@ -3,6 +3,7 @@
 isort:skip_file
 """
 import builtins
+import flwr.proto.error_pb2
 import flwr.proto.node_pb2
 import flwr.proto.recordset_pb2
 import google.protobuf.descriptor
@@ -23,6 +24,7 @@ class Task(google.protobuf.message.Message):
     ANCESTRY_FIELD_NUMBER: builtins.int
     TASK_TYPE_FIELD_NUMBER: builtins.int
     RECORDSET_FIELD_NUMBER: builtins.int
+    ERROR_FIELD_NUMBER: builtins.int
     @property
     def producer(self) -> flwr.proto.node_pb2.Node: ...
     @property
@@ -35,6 +37,8 @@ class Task(google.protobuf.message.Message):
     task_type: typing.Text
     @property
     def recordset(self) -> flwr.proto.recordset_pb2.RecordSet: ...
+    @property
+    def error(self) -> flwr.proto.error_pb2.Error: ...
     def __init__(self,
         *,
         producer: typing.Optional[flwr.proto.node_pb2.Node] = ...,
@@ -45,9 +49,10 @@ class Task(google.protobuf.message.Message):
         ancestry: typing.Optional[typing.Iterable[typing.Text]] = ...,
         task_type: typing.Text = ...,
         recordset: typing.Optional[flwr.proto.recordset_pb2.RecordSet] = ...,
+        error: typing.Optional[flwr.proto.error_pb2.Error] = ...,
         ) -> None: ...
-    def HasField(self, field_name: typing_extensions.Literal["consumer",b"consumer","producer",b"producer","recordset",b"recordset"]) -> builtins.bool: ...
-    def ClearField(self, field_name: typing_extensions.Literal["ancestry",b"ancestry","consumer",b"consumer","created_at",b"created_at","delivered_at",b"delivered_at","producer",b"producer","recordset",b"recordset","task_type",b"task_type","ttl",b"ttl"]) -> None: ...
+    def HasField(self, field_name: typing_extensions.Literal["consumer",b"consumer","error",b"error","producer",b"producer","recordset",b"recordset"]) -> builtins.bool: ...
+    def ClearField(self, field_name: typing_extensions.Literal["ancestry",b"ancestry","consumer",b"consumer","created_at",b"created_at","delivered_at",b"delivered_at","error",b"error","producer",b"producer","recordset",b"recordset","task_type",b"task_type","ttl",b"ttl"]) -> None: ...
 global___Task = Task
 
 class TaskIns(google.protobuf.message.Message):
diff --git a/src/py/flwr_tool/protoc_test.py b/src/py/flwr_tool/protoc_test.py
index 607d808c8497..2d48582eb441 100644
--- a/src/py/flwr_tool/protoc_test.py
+++ b/src/py/flwr_tool/protoc_test.py
@@ -28,4 +28,4 @@ def test_directories() -> None:
 
 def test_proto_file_count() -> None:
     """Test if the correct number of proto files were captured by the glob."""
-    assert len(PROTO_FILES) == 6
+    assert len(PROTO_FILES) == 7

From d4ce2afa0635c7b56c9c1a717be8d758b938f3f4 Mon Sep 17 00:00:00 2001
From: mohammadnaseri <mohammad@flower.dev>
Date: Thu, 29 Feb 2024 10:35:26 +0000
Subject: [PATCH 079/102] Introduce central DP with server-side adaptive
 clipping (#2909)

Co-authored-by: Heng Pan <pan@flower.ai>
---
 src/py/flwr/server/strategy/__init__.py       |  14 +-
 .../server/strategy/dp_adaptive_clipping.py   | 206 +++++++++++++++++-
 2 files changed, 216 insertions(+), 4 deletions(-)

diff --git a/src/py/flwr/server/strategy/__init__.py b/src/py/flwr/server/strategy/__init__.py
index ffb55c12a60d..b7de9a946fff 100644
--- a/src/py/flwr/server/strategy/__init__.py
+++ b/src/py/flwr/server/strategy/__init__.py
@@ -16,10 +16,17 @@
 
 
 from .bulyan import Bulyan as Bulyan
-from .dp_adaptive_clipping import DifferentialPrivacyClientSideAdaptiveClipping
+from .dp_adaptive_clipping import (
+    DifferentialPrivacyClientSideAdaptiveClipping as DifferentialPrivacyClientSideAdaptiveClipping,
+)
+from .dp_adaptive_clipping import (
+    DifferentialPrivacyServerSideAdaptiveClipping as DifferentialPrivacyServerSideAdaptiveClipping,
+)
+from .dp_fixed_clipping import (
+    DifferentialPrivacyClientSideFixedClipping as DifferentialPrivacyClientSideFixedClipping,
+)
 from .dp_fixed_clipping import (
-    DifferentialPrivacyClientSideFixedClipping,
-    DifferentialPrivacyServerSideFixedClipping,
+    DifferentialPrivacyServerSideFixedClipping as DifferentialPrivacyServerSideFixedClipping,
 )
 from .dpfedavg_adaptive import DPFedAvgAdaptive as DPFedAvgAdaptive
 from .dpfedavg_fixed import DPFedAvgFixed as DPFedAvgFixed
@@ -46,6 +53,7 @@
     "DPFedAvgAdaptive",
     "DPFedAvgFixed",
     "DifferentialPrivacyClientSideAdaptiveClipping",
+    "DifferentialPrivacyServerSideAdaptiveClipping",
     "DifferentialPrivacyClientSideFixedClipping",
     "DifferentialPrivacyServerSideFixedClipping",
     "FedAdagrad",
diff --git a/src/py/flwr/server/strategy/dp_adaptive_clipping.py b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
index a5de29e74645..deff3e11054b 100644
--- a/src/py/flwr/server/strategy/dp_adaptive_clipping.py
+++ b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
@@ -24,8 +24,19 @@
 
 import numpy as np
 
-from flwr.common import EvaluateIns, EvaluateRes, FitIns, FitRes, Parameters, Scalar
+from flwr.common import (
+    EvaluateIns,
+    EvaluateRes,
+    FitIns,
+    FitRes,
+    NDArrays,
+    Parameters,
+    Scalar,
+    ndarrays_to_parameters,
+    parameters_to_ndarrays,
+)
 from flwr.common.differential_privacy import (
+    adaptive_clip_inputs_inplace,
     add_gaussian_noise_to_params,
     compute_adaptive_noise_params,
 )
@@ -40,6 +51,199 @@
 from flwr.server.strategy.strategy import Strategy
 
 
+class DifferentialPrivacyServerSideAdaptiveClipping(Strategy):
+    """Strategy wrapper for central DP with server-side adaptive clipping.
+
+    Parameters
+    ----------
+    strategy: Strategy
+        The strategy to which DP functionalities will be added by this wrapper.
+    noise_multiplier : float
+        The noise multiplier for the Gaussian mechanism for model updates.
+    num_sampled_clients : int
+        The number of clients that are sampled on each round.
+    initial_clipping_norm : float
+        The initial value of clipping norm. Deafults to 0.1.
+        Andrew et al. recommends to set to 0.1.
+    target_clipped_quantile : float
+        The desired quantile of updates which should be clipped. Defaults to 0.5.
+    clip_norm_lr : float
+        The learning rate for the clipping norm adaptation. Defaults to 0.2.
+        Andrew et al. recommends to set to 0.2.
+    clipped_count_stddev : float
+        The standard deviation of the noise added to the count of updates below the estimate.
+        Andrew et al. recommends to set to `expected_num_records/20`
+
+    Examples
+    --------
+    Create a strategy:
+
+    >>> strategy = fl.server.strategy.FedAvg( ... )
+
+    Wrap the strategy with the DifferentialPrivacyServerSideAdaptiveClipping wrapper
+
+    >>> dp_strategy = DifferentialPrivacyServerSideAdaptiveClipping(
+    >>>     strategy, cfg.noise_multiplier, cfg.num_sampled_clients, ...
+    >>> )
+    """
+
+    # pylint: disable=too-many-arguments,too-many-instance-attributes
+    def __init__(
+        self,
+        strategy: Strategy,
+        noise_multiplier: float,
+        num_sampled_clients: int,
+        initial_clipping_norm: float = 0.1,
+        target_clipped_quantile: float = 0.5,
+        clip_norm_lr: float = 0.2,
+        clipped_count_stddev: Optional[float] = None,
+    ) -> None:
+        super().__init__()
+
+        if strategy is None:
+            raise ValueError("The passed strategy is None.")
+
+        if noise_multiplier < 0:
+            raise ValueError("The noise multiplier should be a non-negative value.")
+
+        if num_sampled_clients <= 0:
+            raise ValueError(
+                "The number of sampled clients should be a positive value."
+            )
+
+        if initial_clipping_norm <= 0:
+            raise ValueError("The initial clipping norm should be a positive value.")
+
+        if not 0 <= target_clipped_quantile <= 1:
+            raise ValueError(
+                "The target clipped quantile must be between 0 and 1 (inclusive)."
+            )
+
+        if clip_norm_lr <= 0:
+            raise ValueError("The learning rate must be positive.")
+
+        if clipped_count_stddev is not None:
+            if clipped_count_stddev < 0:
+                raise ValueError("The `clipped_count_stddev` must be non-negative.")
+
+        self.strategy = strategy
+        self.num_sampled_clients = num_sampled_clients
+        self.clipping_norm = initial_clipping_norm
+        self.target_clipped_quantile = target_clipped_quantile
+        self.clip_norm_lr = clip_norm_lr
+        (
+            self.clipped_count_stddev,
+            self.noise_multiplier,
+        ) = compute_adaptive_noise_params(
+            noise_multiplier,
+            num_sampled_clients,
+            clipped_count_stddev,
+        )
+
+        self.current_round_params: NDArrays = []
+
+    def __repr__(self) -> str:
+        """Compute a string representation of the strategy."""
+        rep = "Differential Privacy Strategy Wrapper (Server-Side Adaptive Clipping)"
+        return rep
+
+    def initialize_parameters(
+        self, client_manager: ClientManager
+    ) -> Optional[Parameters]:
+        """Initialize global model parameters using given strategy."""
+        return self.strategy.initialize_parameters(client_manager)
+
+    def configure_fit(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, FitIns]]:
+        """Configure the next round of training."""
+        self.current_round_params = parameters_to_ndarrays(parameters)
+        return self.strategy.configure_fit(server_round, parameters, client_manager)
+
+    def configure_evaluate(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, EvaluateIns]]:
+        """Configure the next round of evaluation."""
+        return self.strategy.configure_evaluate(
+            server_round, parameters, client_manager
+        )
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Aggregate training results and update clip norms."""
+        if failures:
+            return None, {}
+
+        if len(results) != self.num_sampled_clients:
+            log(
+                WARNING,
+                CLIENTS_DISCREPANCY_WARNING,
+                len(results),
+                self.num_sampled_clients,
+            )
+
+        norm_bit_set_count = 0
+        for _, res in results:
+            param = parameters_to_ndarrays(res.parameters)
+            # Compute and clip update
+            model_update = [
+                np.subtract(x, y) for (x, y) in zip(param, self.current_round_params)
+            ]
+
+            norm_bit = adaptive_clip_inputs_inplace(model_update, self.clipping_norm)
+            norm_bit_set_count += norm_bit
+
+            for i, _ in enumerate(self.current_round_params):
+                param[i] = self.current_round_params[i] + model_update[i]
+            # Convert back to parameters
+            res.parameters = ndarrays_to_parameters(param)
+
+        # Noising the count
+        noised_norm_bit_set_count = float(
+            np.random.normal(norm_bit_set_count, self.clipped_count_stddev)
+        )
+        noised_norm_bit_set_fraction = noised_norm_bit_set_count / len(results)
+        # Geometric update
+        self.clipping_norm *= math.exp(
+            -self.clip_norm_lr
+            * (noised_norm_bit_set_fraction - self.target_clipped_quantile)
+        )
+
+        aggregated_params, metrics = self.strategy.aggregate_fit(
+            server_round, results, failures
+        )
+
+        # Add Gaussian noise to the aggregated parameters
+        if aggregated_params:
+            aggregated_params = add_gaussian_noise_to_params(
+                aggregated_params,
+                self.noise_multiplier,
+                self.clipping_norm,
+                self.num_sampled_clients,
+            )
+
+        return aggregated_params, metrics
+
+    def aggregate_evaluate(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, EvaluateRes]],
+        failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+    ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+        """Aggregate evaluation losses using the given strategy."""
+        return self.strategy.aggregate_evaluate(server_round, results, failures)
+
+    def evaluate(
+        self, server_round: int, parameters: Parameters
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """Evaluate model parameters using an evaluation function from the strategy."""
+        return self.strategy.evaluate(server_round, parameters)
+
+
 class DifferentialPrivacyClientSideAdaptiveClipping(Strategy):
     """Strategy wrapper for central DP with client-side adaptive clipping.
 

From 7144284ddb21808528576dd401835ca1a6649771 Mon Sep 17 00:00:00 2001
From: Heng Pan <pan@flower.ai>
Date: Thu, 29 Feb 2024 10:50:45 +0000
Subject: [PATCH 080/102] Use enum-like keys for SA (#3032)

---
 examples/secaggplus-mt/workflows.py           |  86 +++++-------
 .../mod/secure_aggregation/secaggplus_mod.py  | 130 ++++++++----------
 .../secure_aggregation/secaggplus_mod_test.py | 111 +++++++--------
 .../secaggplus_constants.py                   |  76 ++++++----
 4 files changed, 186 insertions(+), 217 deletions(-)

diff --git a/examples/secaggplus-mt/workflows.py b/examples/secaggplus-mt/workflows.py
index 4079a2568f2f..8f14dc452e53 100644
--- a/examples/secaggplus-mt/workflows.py
+++ b/examples/secaggplus-mt/workflows.py
@@ -29,28 +29,8 @@
 )
 from flwr.common.secure_aggregation.quantization import dequantize, quantize
 from flwr.common.secure_aggregation.secaggplus_constants import (
-    KEY_ACTIVE_SECURE_ID_LIST,
-    KEY_CIPHERTEXT_LIST,
-    KEY_CLIPPING_RANGE,
-    KEY_DEAD_SECURE_ID_LIST,
-    KEY_DESTINATION_LIST,
-    KEY_MASKED_PARAMETERS,
-    KEY_MOD_RANGE,
-    KEY_PUBLIC_KEY_1,
-    KEY_PUBLIC_KEY_2,
-    KEY_SAMPLE_NUMBER,
-    KEY_SECURE_ID,
-    KEY_SECURE_ID_LIST,
-    KEY_SHARE_LIST,
-    KEY_SHARE_NUMBER,
-    KEY_SOURCE_LIST,
-    KEY_STAGE,
-    KEY_TARGET_RANGE,
-    KEY_THRESHOLD,
-    STAGE_COLLECT_MASKED_INPUT,
-    STAGE_SETUP,
-    STAGE_SHARE_KEYS,
-    STAGE_UNMASK,
+    Key,
+    Stage,
     RECORD_KEY_CONFIGS,
 )
 from flwr.common.secure_aggregation.secaggplus_utils import pseudo_rand_gen
@@ -92,11 +72,11 @@ def _get_from_task(task: Task) -> Dict[str, ConfigsRecordValues]:
 
 
 _secure_aggregation_configuration = {
-    KEY_SHARE_NUMBER: 3,
-    KEY_THRESHOLD: 2,
-    KEY_CLIPPING_RANGE: 3.0,
-    KEY_TARGET_RANGE: 1 << 20,
-    KEY_MOD_RANGE: 1 << 30,
+    Key.SHARE_NUMBER: 3,
+    Key.THRESHOLD: 2,
+    Key.CLIPPING_RANGE: 3.0,
+    Key.TARGET_RANGE: 1 << 20,
+    Key.MOD_RANGE: 1 << 30,
 }
 
 
@@ -110,12 +90,12 @@ def workflow_with_sec_agg(
     """
     # Protocol config
     num_samples = len(sampled_node_ids)
-    num_shares = sec_agg_config[KEY_SHARE_NUMBER]
-    threshold = sec_agg_config[KEY_THRESHOLD]
-    mod_range = sec_agg_config[KEY_MOD_RANGE]
+    num_shares = sec_agg_config[Key.SHARE_NUMBER]
+    threshold = sec_agg_config[Key.THRESHOLD]
+    mod_range = sec_agg_config[Key.MOD_RANGE]
     # Quantization config
-    clipping_range = sec_agg_config[KEY_CLIPPING_RANGE]
-    target_range = sec_agg_config[KEY_TARGET_RANGE]
+    clipping_range = sec_agg_config[Key.CLIPPING_RANGE]
+    target_range = sec_agg_config[Key.TARGET_RANGE]
 
     if LOG_EXPLAIN:
         _quantized = quantize(
@@ -141,13 +121,13 @@ def workflow_with_sec_agg(
             "########################## Secure Aggregation Start ##########################"
         )
     cfg = {
-        KEY_STAGE: STAGE_SETUP,
-        KEY_SAMPLE_NUMBER: num_samples,
-        KEY_SHARE_NUMBER: num_shares,
-        KEY_THRESHOLD: threshold,
-        KEY_CLIPPING_RANGE: clipping_range,
-        KEY_TARGET_RANGE: target_range,
-        KEY_MOD_RANGE: mod_range,
+        Key.STAGE: Stage.SETUP,
+        Key.SAMPLE_NUMBER: num_samples,
+        Key.SHARE_NUMBER: num_shares,
+        Key.THRESHOLD: threshold,
+        Key.CLIPPING_RANGE: clipping_range,
+        Key.TARGET_RANGE: target_range,
+        Key.MOD_RANGE: mod_range,
     }
     # The number of shares should better be odd in the SecAgg+ protocol.
     if num_samples != num_shares and num_shares & 0x1 == 0:
@@ -179,7 +159,7 @@ def workflow_with_sec_agg(
         node_id: _wrap_in_task(
             named_values={
                 **cfg,
-                KEY_SECURE_ID: nid2sid[node_id],
+                Key.SECURE_ID: nid2sid[node_id],
             }
         )
         for node_id in surviving_node_ids
@@ -194,7 +174,7 @@ def workflow_with_sec_agg(
     sid2public_keys = {}
     for node_id, task in node_messages.items():
         key_dict = _get_from_task(task)
-        pk1, pk2 = key_dict[KEY_PUBLIC_KEY_1], key_dict[KEY_PUBLIC_KEY_2]
+        pk1, pk2 = key_dict[Key.PUBLIC_KEY_1], key_dict[Key.PUBLIC_KEY_2]
         sid2public_keys[nid2sid[node_id]] = [pk1, pk2]
 
     """
@@ -206,7 +186,7 @@ def workflow_with_sec_agg(
     yield {
         node_id: _wrap_in_task(
             named_values={
-                KEY_STAGE: STAGE_SHARE_KEYS,
+                Key.STAGE: Stage.SHARE_KEYS,
                 **{
                     str(sid): value
                     for sid, value in sid2public_keys.items()
@@ -232,9 +212,9 @@ def workflow_with_sec_agg(
     }  # dest secure ID -> list of src secure IDs
     for node_id, task in node_messages.items():
         res_dict = _get_from_task(task)
-        srcs += [nid2sid[node_id]] * len(res_dict[KEY_DESTINATION_LIST])
-        dsts += res_dict[KEY_DESTINATION_LIST]
-        ciphertexts += res_dict[KEY_CIPHERTEXT_LIST]
+        srcs += [nid2sid[node_id]] * len(res_dict[Key.DESTINATION_LIST])
+        dsts += res_dict[Key.DESTINATION_LIST]
+        ciphertexts += res_dict[Key.CIPHERTEXT_LIST]
 
     for src, dst, ciphertext in zip(srcs, dsts, ciphertexts):
         if dst in fwd_ciphertexts:
@@ -251,9 +231,9 @@ def workflow_with_sec_agg(
     yield {
         node_id: _wrap_in_task(
             named_values={
-                KEY_STAGE: STAGE_COLLECT_MASKED_INPUT,
-                KEY_CIPHERTEXT_LIST: fwd_ciphertexts[nid2sid[node_id]],
-                KEY_SOURCE_LIST: fwd_srcs[nid2sid[node_id]],
+                Key.STAGE: Stage.COLLECT_MASKED_INPUT,
+                Key.CIPHERTEXT_LIST: fwd_ciphertexts[nid2sid[node_id]],
+                Key.SOURCE_LIST: fwd_srcs[nid2sid[node_id]],
             },
             fit_ins=FitIns(
                 parameters=parameters, config={"drop": nid2sid[node_id] == 0}
@@ -281,7 +261,7 @@ def workflow_with_sec_agg(
             print(f"Client {sid} dropped out.")
     for node_id, task in node_messages.items():
         named_values = _get_from_task(task)
-        client_masked_vec = named_values[KEY_MASKED_PARAMETERS]
+        client_masked_vec = named_values[Key.MASKED_PARAMETERS]
         client_masked_vec = [bytes_to_ndarray(b) for b in client_masked_vec]
         if LOG_EXPLAIN:
             print(f"Received {client_masked_vec[1]} from Client {nid2sid[node_id]}.")
@@ -297,9 +277,9 @@ def workflow_with_sec_agg(
     yield {
         node_id: _wrap_in_task(
             named_values={
-                KEY_STAGE: STAGE_UNMASK,
-                KEY_DEAD_SECURE_ID_LIST: list(dead_sids & nid2neighbours[node_id]),
-                KEY_ACTIVE_SECURE_ID_LIST: list(active_sids & nid2neighbours[node_id]),
+                Key.STAGE: Stage.UNMASK,
+                Key.DEAD_SECURE_ID_LIST: list(dead_sids & nid2neighbours[node_id]),
+                Key.ACTIVE_SECURE_ID_LIST: list(active_sids & nid2neighbours[node_id]),
             }
         )
         for node_id in surviving_node_ids
@@ -319,7 +299,7 @@ def workflow_with_sec_agg(
     for _, task in node_messages.items():
         named_values = _get_from_task(task)
         for owner_sid, share in zip(
-            named_values[KEY_SECURE_ID_LIST], named_values[KEY_SHARE_LIST]
+            named_values[Key.SECURE_ID_LIST], named_values[Key.SHARE_LIST]
         ):
             collected_shares_dict[owner_sid].append(share)
     # Remove mask for every client who is available before ask vectors stage,
diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
index fcb30c1eb0da..63bf6bd6ebe2 100644
--- a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
@@ -52,31 +52,10 @@
 )
 from flwr.common.secure_aggregation.quantization import quantize
 from flwr.common.secure_aggregation.secaggplus_constants import (
-    KEY_ACTIVE_SECURE_ID_LIST,
-    KEY_CIPHERTEXT_LIST,
-    KEY_CLIPPING_RANGE,
-    KEY_DEAD_SECURE_ID_LIST,
-    KEY_DESTINATION_LIST,
-    KEY_MASKED_PARAMETERS,
-    KEY_MOD_RANGE,
-    KEY_PUBLIC_KEY_1,
-    KEY_PUBLIC_KEY_2,
-    KEY_SAMPLE_NUMBER,
-    KEY_SECURE_ID,
-    KEY_SECURE_ID_LIST,
-    KEY_SHARE_LIST,
-    KEY_SHARE_NUMBER,
-    KEY_SOURCE_LIST,
-    KEY_STAGE,
-    KEY_TARGET_RANGE,
-    KEY_THRESHOLD,
     RECORD_KEY_CONFIGS,
     RECORD_KEY_STATE,
-    STAGE_COLLECT_MASKED_INPUT,
-    STAGE_SETUP,
-    STAGE_SHARE_KEYS,
-    STAGE_UNMASK,
-    STAGES,
+    Key,
+    Stage,
 )
 from flwr.common.secure_aggregation.secaggplus_utils import (
     pseudo_rand_gen,
@@ -91,7 +70,7 @@
 class SecAggPlusState:
     """State of the SecAgg+ protocol."""
 
-    current_stage: str = STAGE_UNMASK
+    current_stage: str = Stage.UNMASK
 
     sid: int = 0
     sample_num: int = 0
@@ -187,20 +166,20 @@ def secaggplus_mod(
     check_stage(state.current_stage, configs)
 
     # Update the current stage
-    state.current_stage = cast(str, configs.pop(KEY_STAGE))
+    state.current_stage = cast(str, configs.pop(Key.STAGE))
 
     # Check the validity of the configs based on the current stage
     check_configs(state.current_stage, configs)
 
     # Execute
-    if state.current_stage == STAGE_SETUP:
+    if state.current_stage == Stage.SETUP:
         res = _setup(state, configs)
-    elif state.current_stage == STAGE_SHARE_KEYS:
+    elif state.current_stage == Stage.SHARE_KEYS:
         res = _share_keys(state, configs)
-    elif state.current_stage == STAGE_COLLECT_MASKED_INPUT:
+    elif state.current_stage == Stage.COLLECT_MASKED_INPUT:
         fit = _get_fit_fn(msg, ctxt, call_next)
         res = _collect_masked_input(state, configs, fit)
-    elif state.current_stage == STAGE_UNMASK:
+    elif state.current_stage == Stage.UNMASK:
         res = _unmask(state, configs)
     else:
         raise ValueError(f"Unknown secagg stage: {state.current_stage}")
@@ -215,28 +194,29 @@ def secaggplus_mod(
 
 def check_stage(current_stage: str, configs: ConfigsRecord) -> None:
     """Check the validity of the next stage."""
-    # Check the existence of KEY_STAGE
-    if KEY_STAGE not in configs:
+    # Check the existence of Config.STAGE
+    if Key.STAGE not in configs:
         raise KeyError(
-            f"The required key '{KEY_STAGE}' is missing from the input `named_values`."
+            f"The required key '{Key.STAGE}' is missing from the input `named_values`."
         )
 
-    # Check the value type of the KEY_STAGE
-    next_stage = configs[KEY_STAGE]
+    # Check the value type of the Config.STAGE
+    next_stage = configs[Key.STAGE]
     if not isinstance(next_stage, str):
         raise TypeError(
-            f"The value for the key '{KEY_STAGE}' must be of type {str}, "
+            f"The value for the key '{Key.STAGE}' must be of type {str}, "
             f"but got {type(next_stage)} instead."
         )
 
     # Check the validity of the next stage
-    if next_stage == STAGE_SETUP:
-        if current_stage != STAGE_UNMASK:
+    if next_stage == Stage.SETUP:
+        if current_stage != Stage.UNMASK:
             log(WARNING, "Restart from the setup stage")
     # If stage is not "setup",
     # the stage from `named_values` should be the expected next stage
     else:
-        expected_next_stage = STAGES[(STAGES.index(current_stage) + 1) % len(STAGES)]
+        stages = Stage.all()
+        expected_next_stage = stages[(stages.index(current_stage) + 1) % len(stages)]
         if next_stage != expected_next_stage:
             raise ValueError(
                 "Abort secure aggregation: "
@@ -248,20 +228,20 @@ def check_stage(current_stage: str, configs: ConfigsRecord) -> None:
 def check_configs(stage: str, configs: ConfigsRecord) -> None:
     """Check the validity of the configs."""
     # Check `named_values` for the setup stage
-    if stage == STAGE_SETUP:
+    if stage == Stage.SETUP:
         key_type_pairs = [
-            (KEY_SAMPLE_NUMBER, int),
-            (KEY_SECURE_ID, int),
-            (KEY_SHARE_NUMBER, int),
-            (KEY_THRESHOLD, int),
-            (KEY_CLIPPING_RANGE, float),
-            (KEY_TARGET_RANGE, int),
-            (KEY_MOD_RANGE, int),
+            (Key.SAMPLE_NUMBER, int),
+            (Key.SECURE_ID, int),
+            (Key.SHARE_NUMBER, int),
+            (Key.THRESHOLD, int),
+            (Key.CLIPPING_RANGE, float),
+            (Key.TARGET_RANGE, int),
+            (Key.MOD_RANGE, int),
         ]
         for key, expected_type in key_type_pairs:
             if key not in configs:
                 raise KeyError(
-                    f"Stage {STAGE_SETUP}: the required key '{key}' is "
+                    f"Stage {Stage.SETUP}: the required key '{key}' is "
                     "missing from the input `named_values`."
                 )
             # Bool is a subclass of int in Python,
@@ -269,11 +249,11 @@ def check_configs(stage: str, configs: ConfigsRecord) -> None:
             # pylint: disable-next=unidiomatic-typecheck
             if type(configs[key]) is not expected_type:
                 raise TypeError(
-                    f"Stage {STAGE_SETUP}: The value for the key '{key}' "
+                    f"Stage {Stage.SETUP}: The value for the key '{key}' "
                     f"must be of type {expected_type}, "
                     f"but got {type(configs[key])} instead."
                 )
-    elif stage == STAGE_SHARE_KEYS:
+    elif stage == Stage.SHARE_KEYS:
         for key, value in configs.items():
             if (
                 not isinstance(value, list)
@@ -282,18 +262,18 @@ def check_configs(stage: str, configs: ConfigsRecord) -> None:
                 or not isinstance(value[1], bytes)
             ):
                 raise TypeError(
-                    f"Stage {STAGE_SHARE_KEYS}: "
+                    f"Stage {Stage.SHARE_KEYS}: "
                     f"the value for the key '{key}' must be a list of two bytes."
                 )
-    elif stage == STAGE_COLLECT_MASKED_INPUT:
+    elif stage == Stage.COLLECT_MASKED_INPUT:
         key_type_pairs = [
-            (KEY_CIPHERTEXT_LIST, bytes),
-            (KEY_SOURCE_LIST, int),
+            (Key.CIPHERTEXT_LIST, bytes),
+            (Key.SOURCE_LIST, int),
         ]
         for key, expected_type in key_type_pairs:
             if key not in configs:
                 raise KeyError(
-                    f"Stage {STAGE_COLLECT_MASKED_INPUT}: "
+                    f"Stage {Stage.COLLECT_MASKED_INPUT}: "
                     f"the required key '{key}' is "
                     "missing from the input `named_values`."
                 )
@@ -304,19 +284,19 @@ def check_configs(stage: str, configs: ConfigsRecord) -> None:
                 if type(elm) is not expected_type
             ):
                 raise TypeError(
-                    f"Stage {STAGE_COLLECT_MASKED_INPUT}: "
+                    f"Stage {Stage.COLLECT_MASKED_INPUT}: "
                     f"the value for the key '{key}' "
                     f"must be of type List[{expected_type.__name__}]"
                 )
-    elif stage == STAGE_UNMASK:
+    elif stage == Stage.UNMASK:
         key_type_pairs = [
-            (KEY_ACTIVE_SECURE_ID_LIST, int),
-            (KEY_DEAD_SECURE_ID_LIST, int),
+            (Key.ACTIVE_SECURE_ID_LIST, int),
+            (Key.DEAD_SECURE_ID_LIST, int),
         ]
         for key, expected_type in key_type_pairs:
             if key not in configs:
                 raise KeyError(
-                    f"Stage {STAGE_UNMASK}: "
+                    f"Stage {Stage.UNMASK}: "
                     f"the required key '{key}' is "
                     "missing from the input `named_values`."
                 )
@@ -327,7 +307,7 @@ def check_configs(stage: str, configs: ConfigsRecord) -> None:
                 if type(elm) is not expected_type
             ):
                 raise TypeError(
-                    f"Stage {STAGE_UNMASK}: "
+                    f"Stage {Stage.UNMASK}: "
                     f"the value for the key '{key}' "
                     f"must be of type List[{expected_type.__name__}]"
                 )
@@ -340,15 +320,15 @@ def _setup(
 ) -> Dict[str, ConfigsRecordValues]:
     # Assigning parameter values to object fields
     sec_agg_param_dict = configs
-    state.sample_num = cast(int, sec_agg_param_dict[KEY_SAMPLE_NUMBER])
-    state.sid = cast(int, sec_agg_param_dict[KEY_SECURE_ID])
+    state.sample_num = cast(int, sec_agg_param_dict[Key.SAMPLE_NUMBER])
+    state.sid = cast(int, sec_agg_param_dict[Key.SECURE_ID])
     log(INFO, "Client %d: starting stage 0...", state.sid)
 
-    state.share_num = cast(int, sec_agg_param_dict[KEY_SHARE_NUMBER])
-    state.threshold = cast(int, sec_agg_param_dict[KEY_THRESHOLD])
-    state.clipping_range = cast(float, sec_agg_param_dict[KEY_CLIPPING_RANGE])
-    state.target_range = cast(int, sec_agg_param_dict[KEY_TARGET_RANGE])
-    state.mod_range = cast(int, sec_agg_param_dict[KEY_MOD_RANGE])
+    state.share_num = cast(int, sec_agg_param_dict[Key.SHARE_NUMBER])
+    state.threshold = cast(int, sec_agg_param_dict[Key.THRESHOLD])
+    state.clipping_range = cast(float, sec_agg_param_dict[Key.CLIPPING_RANGE])
+    state.target_range = cast(int, sec_agg_param_dict[Key.TARGET_RANGE])
+    state.mod_range = cast(int, sec_agg_param_dict[Key.MOD_RANGE])
 
     # Dictionaries containing client secure IDs as keys
     # and their respective secret shares as values.
@@ -367,7 +347,7 @@ def _setup(
     state.sk1, state.pk1 = private_key_to_bytes(sk1), public_key_to_bytes(pk1)
     state.sk2, state.pk2 = private_key_to_bytes(sk2), public_key_to_bytes(pk2)
     log(INFO, "Client %d: stage 0 completes. uploading public keys...", state.sid)
-    return {KEY_PUBLIC_KEY_1: state.pk1, KEY_PUBLIC_KEY_2: state.pk2}
+    return {Key.PUBLIC_KEY_1: state.pk1, Key.PUBLIC_KEY_2: state.pk2}
 
 
 # pylint: disable-next=too-many-locals
@@ -429,7 +409,7 @@ def _share_keys(
             ciphertexts.append(ciphertext)
 
     log(INFO, "Client %d: stage 1 completes. uploading key shares...", state.sid)
-    return {KEY_DESTINATION_LIST: dsts, KEY_CIPHERTEXT_LIST: ciphertexts}
+    return {Key.DESTINATION_LIST: dsts, Key.CIPHERTEXT_LIST: ciphertexts}
 
 
 # pylint: disable-next=too-many-locals
@@ -440,8 +420,8 @@ def _collect_masked_input(
 ) -> Dict[str, ConfigsRecordValues]:
     log(INFO, "Client %d: starting stage 2...", state.sid)
     available_clients: List[int] = []
-    ciphertexts = cast(List[bytes], configs[KEY_CIPHERTEXT_LIST])
-    srcs = cast(List[int], configs[KEY_SOURCE_LIST])
+    ciphertexts = cast(List[bytes], configs[Key.CIPHERTEXT_LIST])
+    srcs = cast(List[int], configs[Key.SOURCE_LIST])
     if len(ciphertexts) + 1 < state.threshold:
         raise ValueError("Not enough available neighbour clients.")
 
@@ -505,7 +485,7 @@ def _collect_masked_input(
     quantized_parameters = parameters_mod(quantized_parameters, state.mod_range)
     log(INFO, "Client %d: stage 2 completes. uploading masked parameters...", state.sid)
     return {
-        KEY_MASKED_PARAMETERS: [ndarray_to_bytes(arr) for arr in quantized_parameters]
+        Key.MASKED_PARAMETERS: [ndarray_to_bytes(arr) for arr in quantized_parameters]
     }
 
 
@@ -514,8 +494,8 @@ def _unmask(
 ) -> Dict[str, ConfigsRecordValues]:
     log(INFO, "Client %d: starting stage 3...", state.sid)
 
-    active_sids = cast(List[int], configs[KEY_ACTIVE_SECURE_ID_LIST])
-    dead_sids = cast(List[int], configs[KEY_DEAD_SECURE_ID_LIST])
+    active_sids = cast(List[int], configs[Key.ACTIVE_SECURE_ID_LIST])
+    dead_sids = cast(List[int], configs[Key.DEAD_SECURE_ID_LIST])
     # Send private mask seed share for every avaliable client (including itclient)
     # Send first private key share for building pairwise mask for every dropped client
     if len(active_sids) < state.threshold:
@@ -528,4 +508,4 @@ def _unmask(
     shares += [state.sk1_share_dict[sid] for sid in dead_sids]
 
     log(INFO, "Client %d: stage 3 completes. uploading key shares...", state.sid)
-    return {KEY_SECURE_ID_LIST: sids, KEY_SHARE_LIST: shares}
+    return {Key.SECURE_ID_LIST: sids, Key.SHARE_LIST: shares}
diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
index 0a181f2ea8f5..a46a2fa1b16e 100644
--- a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
@@ -22,25 +22,10 @@
 from flwr.common import ConfigsRecord, Context, Message, Metadata, RecordSet
 from flwr.common.constant import MESSAGE_TYPE_FIT
 from flwr.common.secure_aggregation.secaggplus_constants import (
-    KEY_ACTIVE_SECURE_ID_LIST,
-    KEY_CIPHERTEXT_LIST,
-    KEY_CLIPPING_RANGE,
-    KEY_DEAD_SECURE_ID_LIST,
-    KEY_MOD_RANGE,
-    KEY_SAMPLE_NUMBER,
-    KEY_SECURE_ID,
-    KEY_SHARE_NUMBER,
-    KEY_SOURCE_LIST,
-    KEY_STAGE,
-    KEY_TARGET_RANGE,
-    KEY_THRESHOLD,
     RECORD_KEY_CONFIGS,
     RECORD_KEY_STATE,
-    STAGE_COLLECT_MASKED_INPUT,
-    STAGE_SETUP,
-    STAGE_SHARE_KEYS,
-    STAGE_UNMASK,
-    STAGES,
+    Key,
+    Stage,
 )
 from flwr.common.typing import ConfigsRecordValues
 
@@ -105,27 +90,29 @@ def test_stage_transition(self) -> None:
         handler = get_test_handler(ctxt)
         set_stage = _make_set_state_fn(ctxt)
 
-        assert STAGES == (
-            STAGE_SETUP,
-            STAGE_SHARE_KEYS,
-            STAGE_COLLECT_MASKED_INPUT,
-            STAGE_UNMASK,
+        assert Stage.all() == (
+            Stage.SETUP,
+            Stage.SHARE_KEYS,
+            Stage.COLLECT_MASKED_INPUT,
+            Stage.UNMASK,
         )
 
         valid_transitions = {
             # From one stage to the next stage
-            (STAGE_UNMASK, STAGE_SETUP),
-            (STAGE_SETUP, STAGE_SHARE_KEYS),
-            (STAGE_SHARE_KEYS, STAGE_COLLECT_MASKED_INPUT),
-            (STAGE_COLLECT_MASKED_INPUT, STAGE_UNMASK),
+            (Stage.UNMASK, Stage.SETUP),
+            (Stage.SETUP, Stage.SHARE_KEYS),
+            (Stage.SHARE_KEYS, Stage.COLLECT_MASKED_INPUT),
+            (Stage.COLLECT_MASKED_INPUT, Stage.UNMASK),
             # From any stage to the initial stage
             # Such transitions will log a warning.
-            (STAGE_SETUP, STAGE_SETUP),
-            (STAGE_SHARE_KEYS, STAGE_SETUP),
-            (STAGE_COLLECT_MASKED_INPUT, STAGE_SETUP),
+            (Stage.SETUP, Stage.SETUP),
+            (Stage.SHARE_KEYS, Stage.SETUP),
+            (Stage.COLLECT_MASKED_INPUT, Stage.SETUP),
         }
 
-        invalid_transitions = set(product(STAGES, STAGES)).difference(valid_transitions)
+        invalid_transitions = set(product(Stage.all(), Stage.all())).difference(
+            valid_transitions
+        )
 
         # Test valid transitions
         # If the next stage is valid, the function should update the current stage
@@ -134,7 +121,7 @@ def test_stage_transition(self) -> None:
             set_stage(current_stage)
 
             with self.assertRaises(KeyError):
-                handler({KEY_STAGE: next_stage})
+                handler({Key.STAGE: next_stage})
 
         # Test invalid transitions
         # If the next stage is invalid, the function should raise ValueError
@@ -142,7 +129,7 @@ def test_stage_transition(self) -> None:
             set_stage(current_stage)
 
             with self.assertRaises(ValueError):
-                handler({KEY_STAGE: next_stage})
+                handler({Key.STAGE: next_stage})
 
     def test_stage_setup_check(self) -> None:
         """Test content checking for the setup stage."""
@@ -151,13 +138,13 @@ def test_stage_setup_check(self) -> None:
         set_stage = _make_set_state_fn(ctxt)
 
         valid_key_type_pairs = [
-            (KEY_SAMPLE_NUMBER, int),
-            (KEY_SECURE_ID, int),
-            (KEY_SHARE_NUMBER, int),
-            (KEY_THRESHOLD, int),
-            (KEY_CLIPPING_RANGE, float),
-            (KEY_TARGET_RANGE, int),
-            (KEY_MOD_RANGE, int),
+            (Key.SAMPLE_NUMBER, int),
+            (Key.SECURE_ID, int),
+            (Key.SHARE_NUMBER, int),
+            (Key.THRESHOLD, int),
+            (Key.CLIPPING_RANGE, float),
+            (Key.TARGET_RANGE, int),
+            (Key.MOD_RANGE, int),
         ]
 
         type_to_test_value: Dict[type, ConfigsRecordValues] = {
@@ -175,13 +162,13 @@ def test_stage_setup_check(self) -> None:
 
         # Test valid `named_values`
         try:
-            check_configs(STAGE_SETUP, ConfigsRecord(valid_configs))
+            check_configs(Stage.SETUP, ConfigsRecord(valid_configs))
         # pylint: disable-next=broad-except
         except Exception as exc:
             self.fail(f"check_named_values() raised {type(exc)} unexpectedly!")
 
         # Set the stage
-        valid_configs[KEY_STAGE] = STAGE_SETUP
+        valid_configs[Key.STAGE] = Stage.SETUP
 
         # Test invalid `named_values`
         for key, value_type in valid_key_type_pairs:
@@ -193,14 +180,14 @@ def test_stage_setup_check(self) -> None:
                     continue
                 invalid_configs[key] = other_value
 
-                set_stage(STAGE_UNMASK)
+                set_stage(Stage.UNMASK)
                 with self.assertRaises(TypeError):
                     handler(invalid_configs.copy())
 
             # Test missing key
             invalid_configs.pop(key)
 
-            set_stage(STAGE_UNMASK)
+            set_stage(Stage.UNMASK)
             with self.assertRaises(KeyError):
                 handler(invalid_configs.copy())
 
@@ -218,13 +205,13 @@ def test_stage_share_keys_check(self) -> None:
 
         # Test valid `named_values`
         try:
-            check_configs(STAGE_SHARE_KEYS, ConfigsRecord(valid_configs))
+            check_configs(Stage.SHARE_KEYS, ConfigsRecord(valid_configs))
         # pylint: disable-next=broad-except
         except Exception as exc:
             self.fail(f"check_named_values() raised {type(exc)} unexpectedly!")
 
         # Set the stage
-        valid_configs[KEY_STAGE] = STAGE_SHARE_KEYS
+        valid_configs[Key.STAGE] = Stage.SHARE_KEYS
 
         # Test invalid `named_values`
         invalid_values: List[ConfigsRecordValues] = [
@@ -237,7 +224,7 @@ def test_stage_share_keys_check(self) -> None:
             invalid_configs = valid_configs.copy()
             invalid_configs["1"] = value
 
-            set_stage(STAGE_SETUP)
+            set_stage(Stage.SETUP)
             with self.assertRaises(TypeError):
                 handler(invalid_configs.copy())
 
@@ -248,40 +235,40 @@ def test_stage_collect_masked_input_check(self) -> None:
         set_stage = _make_set_state_fn(ctxt)
 
         valid_configs: Dict[str, ConfigsRecordValues] = {
-            KEY_CIPHERTEXT_LIST: [b"ctxt!", b"ctxt@", b"ctxt#", b"ctxt?"],
-            KEY_SOURCE_LIST: [32, 51324, 32324123, -3],
+            Key.CIPHERTEXT_LIST: [b"ctxt!", b"ctxt@", b"ctxt#", b"ctxt?"],
+            Key.SOURCE_LIST: [32, 51324, 32324123, -3],
         }
 
         # Test valid `named_values`
         try:
-            check_configs(STAGE_COLLECT_MASKED_INPUT, ConfigsRecord(valid_configs))
+            check_configs(Stage.COLLECT_MASKED_INPUT, ConfigsRecord(valid_configs))
         # pylint: disable-next=broad-except
         except Exception as exc:
             self.fail(f"check_named_values() raised {type(exc)} unexpectedly!")
 
         # Set the stage
-        valid_configs[KEY_STAGE] = STAGE_COLLECT_MASKED_INPUT
+        valid_configs[Key.STAGE] = Stage.COLLECT_MASKED_INPUT
 
         # Test invalid `named_values`
         # Test missing keys
         for key in list(valid_configs.keys()):
-            if key == KEY_STAGE:
+            if key == Key.STAGE:
                 continue
             invalid_configs = valid_configs.copy()
             invalid_configs.pop(key)
 
-            set_stage(STAGE_SHARE_KEYS)
+            set_stage(Stage.SHARE_KEYS)
             with self.assertRaises(KeyError):
                 handler(invalid_configs)
 
         # Test wrong value type for the key
         for key in valid_configs:
-            if key == KEY_STAGE:
+            if key == Key.STAGE:
                 continue
             invalid_configs = valid_configs.copy()
             invalid_configs[key] = [3.1415926]
 
-            set_stage(STAGE_SHARE_KEYS)
+            set_stage(Stage.SHARE_KEYS)
             with self.assertRaises(TypeError):
                 handler(invalid_configs)
 
@@ -292,39 +279,39 @@ def test_stage_unmask_check(self) -> None:
         set_stage = _make_set_state_fn(ctxt)
 
         valid_configs: Dict[str, ConfigsRecordValues] = {
-            KEY_ACTIVE_SECURE_ID_LIST: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
-            KEY_DEAD_SECURE_ID_LIST: [32, 51324, 32324123, -3],
+            Key.ACTIVE_SECURE_ID_LIST: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            Key.DEAD_SECURE_ID_LIST: [32, 51324, 32324123, -3],
         }
 
         # Test valid `named_values`
         try:
-            check_configs(STAGE_UNMASK, ConfigsRecord(valid_configs))
+            check_configs(Stage.UNMASK, ConfigsRecord(valid_configs))
         # pylint: disable-next=broad-except
         except Exception as exc:
             self.fail(f"check_named_values() raised {type(exc)} unexpectedly!")
 
         # Set the stage
-        valid_configs[KEY_STAGE] = STAGE_UNMASK
+        valid_configs[Key.STAGE] = Stage.UNMASK
 
         # Test invalid `named_values`
         # Test missing keys
         for key in list(valid_configs.keys()):
-            if key == KEY_STAGE:
+            if key == Key.STAGE:
                 continue
             invalid_configs = valid_configs.copy()
             invalid_configs.pop(key)
 
-            set_stage(STAGE_COLLECT_MASKED_INPUT)
+            set_stage(Stage.COLLECT_MASKED_INPUT)
             with self.assertRaises(KeyError):
                 handler(invalid_configs)
 
         # Test wrong value type for the key
         for key in valid_configs:
-            if key == KEY_STAGE:
+            if key == Key.STAGE:
                 continue
             invalid_configs = valid_configs.copy()
             invalid_configs[key] = [True, False, True, False]
 
-            set_stage(STAGE_COLLECT_MASKED_INPUT)
+            set_stage(Stage.COLLECT_MASKED_INPUT)
             with self.assertRaises(TypeError):
                 handler(invalid_configs)
diff --git a/src/py/flwr/common/secure_aggregation/secaggplus_constants.py b/src/py/flwr/common/secure_aggregation/secaggplus_constants.py
index 9a2bf26e98e8..17370543e1ef 100644
--- a/src/py/flwr/common/secure_aggregation/secaggplus_constants.py
+++ b/src/py/flwr/common/secure_aggregation/secaggplus_constants.py
@@ -14,33 +14,55 @@
 # ==============================================================================
 """Constants for the SecAgg/SecAgg+ protocol."""
 
+
+from __future__ import annotations
+
 RECORD_KEY_STATE = "secaggplus_state"
 RECORD_KEY_CONFIGS = "secaggplus_configs"
 
-# Names of stages
-STAGE_SETUP = "setup"
-STAGE_SHARE_KEYS = "share_keys"
-STAGE_COLLECT_MASKED_INPUT = "collect_masked_input"
-STAGE_UNMASK = "unmask"
-STAGES = (STAGE_SETUP, STAGE_SHARE_KEYS, STAGE_COLLECT_MASKED_INPUT, STAGE_UNMASK)
-
-# All valid keys in received/replied `named_values` dictionaries
-KEY_STAGE = "stage"
-KEY_SAMPLE_NUMBER = "sample_num"
-KEY_SECURE_ID = "secure_id"
-KEY_SHARE_NUMBER = "share_num"
-KEY_THRESHOLD = "threshold"
-KEY_CLIPPING_RANGE = "clipping_range"
-KEY_TARGET_RANGE = "target_range"
-KEY_MOD_RANGE = "mod_range"
-KEY_PUBLIC_KEY_1 = "pk1"
-KEY_PUBLIC_KEY_2 = "pk2"
-KEY_DESTINATION_LIST = "dsts"
-KEY_CIPHERTEXT_LIST = "ctxts"
-KEY_SOURCE_LIST = "srcs"
-KEY_PARAMETERS = "params"
-KEY_MASKED_PARAMETERS = "masked_params"
-KEY_ACTIVE_SECURE_ID_LIST = "active_sids"
-KEY_DEAD_SECURE_ID_LIST = "dead_sids"
-KEY_SECURE_ID_LIST = "sids"
-KEY_SHARE_LIST = "shares"
+
+class Stage:
+    """Stages for the SecAgg+ protocol."""
+
+    SETUP = "setup"
+    SHARE_KEYS = "share_keys"
+    COLLECT_MASKED_INPUT = "collect_masked_input"
+    UNMASK = "unmask"
+    _stages = (SETUP, SHARE_KEYS, COLLECT_MASKED_INPUT, UNMASK)
+
+    @classmethod
+    def all(cls) -> tuple[str, str, str, str]:
+        """Return all stages."""
+        return cls._stages
+
+    def __new__(cls) -> Stage:
+        """Prevent instantiation."""
+        raise TypeError(f"{cls.__name__} cannot be instantiated.")
+
+
+class Key:
+    """Keys for the configs in the ConfigsRecord."""
+
+    STAGE = "stage"
+    SAMPLE_NUMBER = "sample_num"
+    SECURE_ID = "secure_id"
+    SHARE_NUMBER = "share_num"
+    THRESHOLD = "threshold"
+    CLIPPING_RANGE = "clipping_range"
+    TARGET_RANGE = "target_range"
+    MOD_RANGE = "mod_range"
+    PUBLIC_KEY_1 = "pk1"
+    PUBLIC_KEY_2 = "pk2"
+    DESTINATION_LIST = "dsts"
+    CIPHERTEXT_LIST = "ctxts"
+    SOURCE_LIST = "srcs"
+    PARAMETERS = "params"
+    MASKED_PARAMETERS = "masked_params"
+    ACTIVE_SECURE_ID_LIST = "active_sids"
+    DEAD_SECURE_ID_LIST = "dead_sids"
+    SECURE_ID_LIST = "sids"
+    SHARE_LIST = "shares"
+
+    def __new__(cls) -> Key:
+        """Prevent instantiation."""
+        raise TypeError(f"{cls.__name__} cannot be instantiated.")

From 3233c5b34cdd23b9887ee28ab0502f3f4648a55b Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Thu, 29 Feb 2024 20:24:47 +0100
Subject: [PATCH 081/102] Change INFO to DEBUG for a few logs (#3035)

---
 src/py/flwr/server/superlink/driver/driver_servicer.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/py/flwr/server/superlink/driver/driver_servicer.py b/src/py/flwr/server/superlink/driver/driver_servicer.py
index ca2cd69b2f20..5be53a8e0c6d 100644
--- a/src/py/flwr/server/superlink/driver/driver_servicer.py
+++ b/src/py/flwr/server/superlink/driver/driver_servicer.py
@@ -15,7 +15,7 @@
 """Driver API servicer."""
 
 
-from logging import INFO
+from logging import DEBUG, INFO
 from typing import List, Optional, Set
 from uuid import UUID
 
@@ -70,7 +70,7 @@ def PushTaskIns(
         self, request: PushTaskInsRequest, context: grpc.ServicerContext
     ) -> PushTaskInsResponse:
         """Push a set of TaskIns."""
-        log(INFO, "DriverServicer.PushTaskIns")
+        log(DEBUG, "DriverServicer.PushTaskIns")
 
         # Validate request
         _raise_if(len(request.task_ins_list) == 0, "`task_ins_list` must not be empty")
@@ -95,7 +95,7 @@ def PullTaskRes(
         self, request: PullTaskResRequest, context: grpc.ServicerContext
     ) -> PullTaskResResponse:
         """Pull a set of TaskRes."""
-        log(INFO, "DriverServicer.PullTaskRes")
+        log(DEBUG, "DriverServicer.PullTaskRes")
 
         # Convert each task_id str to UUID
         task_ids: Set[UUID] = {UUID(task_id) for task_id in request.task_ids}
@@ -105,7 +105,7 @@ def PullTaskRes(
 
         # Register callback
         def on_rpc_done() -> None:
-            log(INFO, "DriverServicer.PullTaskRes callback: delete TaskIns/TaskRes")
+            log(DEBUG, "DriverServicer.PullTaskRes callback: delete TaskIns/TaskRes")
 
             if context.is_active():
                 return

From f25ddc13727ee85f372583f6d85c046803a76329 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Thu, 29 Feb 2024 22:09:45 +0100
Subject: [PATCH 082/102] Introduce `flower-simulation` command and update
 simulation examples (#3009)

---
 examples/simulation-pytorch/README.md    |  35 +++--
 examples/simulation-pytorch/sim.py       |  48 +++---
 examples/simulation-tensorflow/README.md |  39 +++--
 examples/simulation-tensorflow/sim.py    |  54 ++++---
 pyproject.toml                           |   1 +
 src/py/flwr/simulation/__init__.py       |   3 +
 src/py/flwr/simulation/run_simulation.py | 177 +++++++++++++++++++++++
 7 files changed, 293 insertions(+), 64 deletions(-)
 create mode 100644 src/py/flwr/simulation/run_simulation.py

diff --git a/examples/simulation-pytorch/README.md b/examples/simulation-pytorch/README.md
index 5ba5ec70dc3e..d74b5be06a00 100644
--- a/examples/simulation-pytorch/README.md
+++ b/examples/simulation-pytorch/README.md
@@ -54,17 +54,13 @@ Write the command below in your terminal to install the dependencies according t
 pip install -r requirements.txt
 ```
 
-### Run Federated Learning Example
+### Run with `start_simulation()`
 
-```bash
-# You can run the example without activating your environemnt
-poetry run python sim.py
+Ensure you have activated your environment then:
 
-# Or by first activating it
-poetry shell
+```bash
 # and then run the example
 python sim.py
-# you can exit your environment by typing "exit"
 ```
 
 You can adjust the CPU/GPU resources you assign to each of your virtual clients. By default, your clients will only use 1xCPU core. For example:
@@ -73,10 +69,29 @@ You can adjust the CPU/GPU resources you assign to each of your virtual clients.
 # Will assign 2xCPUs to each client
 python sim.py --num_cpus=2
 
-# Will assign 2xCPUs and 20% of the GPU's VRAM to each client
-# This means that you can have 5 concurrent clients on each GPU
+# Will assign 2xCPUs and 25% of the GPU's VRAM to each client
+# This means that you can have 4 concurrent clients on each GPU
 # (assuming you have enough CPUs)
-python sim.py --num_cpus=2 --num_gpus=0.2
+python sim.py --num_cpus=2 --num_gpus=0.25
+```
+
+### Run with Flower Next (preview)
+
+Ensure you have activated your environment, then execute the command below. All `ClientApp` instances will run on CPU but the `ServerApp` will run on the GPU if one is available. Note that this is the case because the `Simulation Engine` only exposes certain resources to the `ClientApp` (based on the `client_resources` in `--backend-config`).
+
+```bash
+# Run with the default backend-config.
+# `--server-app` points to the `server` object in the sim.py file in this example.
+# `--client-app` points to the `client` object in the sim.py file in this example.
+flower-simulation --client-app=sim:client --server-app=sim:server --num-supernodes=100
+```
+
+You can change the default resources assigned to each `ClientApp` by means of the `--backend-config` argument:
+
+```bash
+# Tells the VCE to resever 2x CPUs and 25% of available VRAM for each ClientApp
+flower-simulation --client-app=sim:client --server-app=sim:server --num-supernodes=100 \
+    --backend-config='{"client_resources": {"num_cpus":2, "num_gpus":0.25}}'
 ```
 
 Take a look at the [Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) for more details on how you can customise your simulation.
diff --git a/examples/simulation-pytorch/sim.py b/examples/simulation-pytorch/sim.py
index 0a6ed8ebb9b8..ca9e6f0e8366 100644
--- a/examples/simulation-pytorch/sim.py
+++ b/examples/simulation-pytorch/sim.py
@@ -29,9 +29,9 @@
     default=0.0,
     help="Ratio of GPU memory to assign to a virtual client",
 )
-parser.add_argument("--num_rounds", type=int, default=10, help="Number of FL rounds.")
 
 NUM_CLIENTS = 100
+NUM_ROUNDS = 10
 
 
 # Flower client, adapted from Pytorch quickstart example
@@ -167,28 +167,36 @@ def evaluate(
     return evaluate
 
 
+# Download MNIST dataset and partition it
+mnist_fds = FederatedDataset(dataset="mnist", partitioners={"train": NUM_CLIENTS})
+centralized_testset = mnist_fds.load_full("test")
+
+# Configure the strategy
+strategy = fl.server.strategy.FedAvg(
+    fraction_fit=0.1,  # Sample 10% of available clients for training
+    fraction_evaluate=0.05,  # Sample 5% of available clients for evaluation
+    min_available_clients=10,
+    on_fit_config_fn=fit_config,
+    evaluate_metrics_aggregation_fn=weighted_average,  # Aggregate federated metrics
+    evaluate_fn=get_evaluate_fn(centralized_testset),  # Global evaluation function
+)
+
+# ClientApp for Flower-Next
+client = fl.client.ClientApp(
+    client_fn=get_client_fn(mnist_fds),
+)
+
+# ServerApp for Flower-Next
+server = fl.server.ServerApp(
+    config=fl.server.ServerConfig(num_rounds=NUM_ROUNDS),
+    strategy=strategy,
+)
+
+
 def main():
     # Parse input arguments
     args = parser.parse_args()
 
-    # Download MNIST dataset and partition it
-    mnist_fds = FederatedDataset(dataset="mnist", partitioners={"train": NUM_CLIENTS})
-    centralized_testset = mnist_fds.load_full("test")
-
-    # Configure the strategy
-    strategy = fl.server.strategy.FedAvg(
-        fraction_fit=0.1,  # Sample 10% of available clients for training
-        fraction_evaluate=0.05,  # Sample 5% of available clients for evaluation
-        min_fit_clients=10,  # Never sample less than 10 clients for training
-        min_evaluate_clients=5,  # Never sample less than 5 clients for evaluation
-        min_available_clients=int(
-            NUM_CLIENTS * 0.75
-        ),  # Wait until at least 75 clients are available
-        on_fit_config_fn=fit_config,
-        evaluate_metrics_aggregation_fn=weighted_average,  # Aggregate federated metrics
-        evaluate_fn=get_evaluate_fn(centralized_testset),  # Global evaluation function
-    )
-
     # Resources to be assigned to each virtual client
     client_resources = {
         "num_cpus": args.num_cpus,
@@ -200,7 +208,7 @@ def main():
         client_fn=get_client_fn(mnist_fds),
         num_clients=NUM_CLIENTS,
         client_resources=client_resources,
-        config=fl.server.ServerConfig(num_rounds=args.num_rounds),
+        config=fl.server.ServerConfig(num_rounds=NUM_ROUNDS),
         strategy=strategy,
         actor_kwargs={
             "on_actor_init_fn": disable_progress_bar  # disable tqdm on each actor/process spawning virtual clients
diff --git a/examples/simulation-tensorflow/README.md b/examples/simulation-tensorflow/README.md
index 75be823db2eb..e3dca8e28906 100644
--- a/examples/simulation-tensorflow/README.md
+++ b/examples/simulation-tensorflow/README.md
@@ -53,29 +53,46 @@ Write the command below in your terminal to install the dependencies according t
 pip install -r requirements.txt
 ```
 
-### Run Federated Learning Example
+### Run with `start_simulation()`
 
-```bash
-# You can run the example without activating your environemnt
-poetry run python sim.py
+Ensure you have activated your environment then:
 
-# Or by first activating it
-poetry shell
+```bash
 # and then run the example
 python sim.py
-# you can exit your environment by typing "exit"
 ```
 
-You can adjust the CPU/GPU resources you assign to each of your virtual clients. By default, your clients will only use 1xCPU core. For example:
+You can adjust the CPU/GPU resources you assign to each of your virtual clients. By default, your clients will only use 2xCPU core. For example:
 
 ```bash
 # Will assign 2xCPUs to each client
 python sim.py --num_cpus=2
 
-# Will assign 2xCPUs and 20% of the GPU's VRAM to each client
-# This means that you can have 5 concurrent clients on each GPU
+# Will assign 2xCPUs and 25% of the GPU's VRAM to each client
+# This means that you can have 4 concurrent clients on each GPU
 # (assuming you have enough CPUs)
-python sim.py --num_cpus=2 --num_gpus=0.2
+python sim.py --num_cpus=2 --num_gpus=0.25
+```
+
+Because TensorFlow by default maps all the available VRAM, we need to [enable GPU memory growth](https://www.tensorflow.org/guide/gpu#limiting_gpu_memory_growth), see how it is done in the example (`sim.py`) for both the "main" process (where the server/strategy runs) and for the clients (using the `actor_kwargs`)
+
+### Run with Flower Next (preview)
+
+Ensure you have activated your environment, then execute the command below. All `ClientApp` instances will run on CPU but the `ServerApp` will run on the GPU if one is available. Note that this is the case because the `Simulation Engine` only exposes certain resources to the `ClientApp` (based on the `client_resources` in `--backend-config`). For TensorFlow simulations, it is desirable to make use of TF's [memory growth](https://www.tensorflow.org/api_docs/python/tf/config/experimental/set_memory_growth) feature. You can enable that easily with the `--enable-tf-gpu-growth` flag.
+
+```bash
+# Run with the default backend-config.
+# `--server-app` points to the `server` object in the sim.py file in this example.
+# `--client-app` points to the `client` object in the sim.py file in this example.
+flower-simulation --client-app=sim:client --server-app=sim:server --num-supernodes=100 --enable-tf-gpu-growth
+```
+
+You can change the default resources assigned to each `ClientApp` using the `--backend-config` argument.
+
+```bash
+# Tells the VCE to resever 2x CPUs and 25% of available VRAM for each ClientApp
+flower-simulation --client-app=sim:client --server-app=sim:server --num-supernodes=100 \
+    --backend-config='{"client_resources": {"num_cpus":2, "num_gpus":0.25}}' --enable-tf-gpu-growth
 ```
 
 Take a look at the [Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) for more details on how you can customise your simulation.
diff --git a/examples/simulation-tensorflow/sim.py b/examples/simulation-tensorflow/sim.py
index 043c624a40a9..2a19e131fe79 100644
--- a/examples/simulation-tensorflow/sim.py
+++ b/examples/simulation-tensorflow/sim.py
@@ -1,5 +1,4 @@
 import os
-import math
 import argparse
 from typing import Dict, List, Tuple
 
@@ -29,9 +28,9 @@
     default=0.0,
     help="Ratio of GPU memory to assign to a virtual client",
 )
-parser.add_argument("--num_rounds", type=int, default=10, help="Number of FL rounds.")
 
 NUM_CLIENTS = 100
+NUM_ROUNDS = 10
 VERBOSE = 0
 
 
@@ -129,30 +128,39 @@ def evaluate(
     return evaluate
 
 
+# Download MNIST dataset and partition it
+mnist_fds = FederatedDataset(dataset="mnist", partitioners={"train": NUM_CLIENTS})
+# Get the whole test set for centralised evaluation
+centralized_testset = mnist_fds.load_full("test").to_tf_dataset(
+    columns="image", label_cols="label", batch_size=64
+)
+
+# Create FedAvg strategy
+strategy = fl.server.strategy.FedAvg(
+    fraction_fit=0.1,  # Sample 10% of available clients for training
+    fraction_evaluate=0.05,  # Sample 5% of available clients for evaluation
+    min_fit_clients=10,  # Never sample less than 10 clients for training
+    evaluate_metrics_aggregation_fn=weighted_average,  # aggregates federated metrics
+    evaluate_fn=get_evaluate_fn(centralized_testset),  # global evaluation function
+)
+
+
+# ClientApp for Flower-Next
+client = fl.client.ClientApp(
+    client_fn=get_client_fn(mnist_fds),
+)
+
+# ServerApp for Flower-Next
+server = fl.server.ServerApp(
+    config=fl.server.ServerConfig(num_rounds=NUM_ROUNDS),
+    strategy=strategy,
+)
+
+
 def main() -> None:
     # Parse input arguments
     args = parser.parse_args()
 
-    # Download MNIST dataset and partition it
-    mnist_fds = FederatedDataset(dataset="mnist", partitioners={"train": NUM_CLIENTS})
-    # Get the whole test set for centralised evaluation
-    centralized_testset = mnist_fds.load_full("test").to_tf_dataset(
-        columns="image", label_cols="label", batch_size=64
-    )
-
-    # Create FedAvg strategy
-    strategy = fl.server.strategy.FedAvg(
-        fraction_fit=0.1,  # Sample 10% of available clients for training
-        fraction_evaluate=0.05,  # Sample 5% of available clients for evaluation
-        min_fit_clients=10,  # Never sample less than 10 clients for training
-        min_evaluate_clients=5,  # Never sample less than 5 clients for evaluation
-        min_available_clients=int(
-            NUM_CLIENTS * 0.75
-        ),  # Wait until at least 75 clients are available
-        evaluate_metrics_aggregation_fn=weighted_average,  # aggregates federated metrics
-        evaluate_fn=get_evaluate_fn(centralized_testset),  # global evaluation function
-    )
-
     # With a dictionary, you tell Flower's VirtualClientEngine that each
     # client needs exclusive access to these many resources in order to run
     client_resources = {
@@ -164,7 +172,7 @@ def main() -> None:
     fl.simulation.start_simulation(
         client_fn=get_client_fn(mnist_fds),
         num_clients=NUM_CLIENTS,
-        config=fl.server.ServerConfig(num_rounds=args.num_rounds),
+        config=fl.server.ServerConfig(NUM_ROUNDS),
         strategy=strategy,
         client_resources=client_resources,
         actor_kwargs={
diff --git a/pyproject.toml b/pyproject.toml
index b52be36b7ff5..72d001e5914d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -58,6 +58,7 @@ flower-fleet-api = "flwr.server:run_fleet_api"
 flower-superlink = "flwr.server:run_superlink"
 flower-client-app = "flwr.client:run_client_app"
 flower-server-app = "flwr.server:run_server_app"
+flower-simulation = "flwr.simulation:run_simulation"
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/src/py/flwr/simulation/__init__.py b/src/py/flwr/simulation/__init__.py
index 724ea9273916..b283de70c588 100644
--- a/src/py/flwr/simulation/__init__.py
+++ b/src/py/flwr/simulation/__init__.py
@@ -17,6 +17,8 @@
 
 import importlib
 
+from flwr.simulation.run_simulation import run_simulation
+
 is_ray_installed = importlib.util.find_spec("ray") is not None
 
 if is_ray_installed:
@@ -36,4 +38,5 @@ def start_simulation(*args, **kwargs):  # type: ignore
 
 __all__ = [
     "start_simulation",
+    "run_simulation",
 ]
diff --git a/src/py/flwr/simulation/run_simulation.py b/src/py/flwr/simulation/run_simulation.py
new file mode 100644
index 000000000000..ebe76944e779
--- /dev/null
+++ b/src/py/flwr/simulation/run_simulation.py
@@ -0,0 +1,177 @@
+# Copyright 2024 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Flower Simulation."""
+
+import argparse
+import asyncio
+import json
+import threading
+import traceback
+from logging import ERROR, INFO, WARNING
+
+import grpc
+
+from flwr.common import EventType, event, log
+from flwr.common.exit_handlers import register_exit_handlers
+from flwr.server.driver.driver import Driver
+from flwr.server.run_serverapp import run
+from flwr.server.superlink.driver.driver_grpc import run_driver_api_grpc
+from flwr.server.superlink.fleet import vce
+from flwr.server.superlink.state import StateFactory
+from flwr.simulation.ray_transport.utils import enable_tf_gpu_growth
+
+
+def run_simulation() -> None:
+    """Run Simulation Engine."""
+    args = _parse_args_run_simulation().parse_args()
+
+    # Load JSON config
+    backend_config_dict = json.loads(args.backend_config)
+
+    # Enable GPU memory growth (relevant only for TF)
+    if args.enable_tf_gpu_growth:
+        log(INFO, "Enabling GPU growth for Tensorflow on the main thread.")
+        enable_tf_gpu_growth()
+        # Check that Backend config has also enabled using GPU growth
+        use_tf = backend_config_dict.get("tensorflow", False)
+        if not use_tf:
+            log(WARNING, "Enabling GPU growth for your backend.")
+            backend_config_dict["tensorflow"] = True
+
+    # Convert back to JSON stream
+    backend_config = json.dumps(backend_config_dict)
+
+    # Initialize StateFactory
+    state_factory = StateFactory(":flwr-in-memory-state:")
+
+    # Start Driver API
+    driver_server: grpc.Server = run_driver_api_grpc(
+        address=args.driver_api_address,
+        state_factory=state_factory,
+        certificates=None,
+    )
+
+    # SuperLink with Simulation Engine
+    f_stop = asyncio.Event()
+    superlink_th = threading.Thread(
+        target=vce.start_vce,
+        kwargs={
+            "num_supernodes": args.num_supernodes,
+            "client_app_module_name": args.client_app,
+            "backend_name": args.backend,
+            "backend_config_json_stream": backend_config,
+            "working_dir": args.dir,
+            "state_factory": state_factory,
+            "f_stop": f_stop,
+        },
+        daemon=False,
+    )
+
+    superlink_th.start()
+    event(EventType.RUN_SUPERLINK_ENTER)
+
+    try:
+        # Initialize Driver
+        driver = Driver(
+            driver_service_address=args.driver_api_address,
+            root_certificates=None,
+        )
+
+        # Launch server app
+        run(args.server_app, driver, args.dir)
+
+    except Exception as ex:
+
+        log(ERROR, "An exception occurred: %s", ex)
+        log(ERROR, traceback.format_exc())
+        raise RuntimeError(
+            "An error was encountered by the Simulation Engine. Ending simulation."
+        ) from ex
+
+    finally:
+
+        del driver
+
+        # Trigger stop event
+        f_stop.set()
+
+        register_exit_handlers(
+            grpc_servers=[driver_server],
+            bckg_threads=[superlink_th],
+            event_type=EventType.RUN_SUPERLINK_LEAVE,
+        )
+        superlink_th.join()
+
+
+def _parse_args_run_simulation() -> argparse.ArgumentParser:
+    """Parse flower-simulation command line arguments."""
+    parser = argparse.ArgumentParser(
+        description="Start a Flower simulation",
+    )
+    parser.add_argument(
+        "--client-app",
+        required=True,
+        help="For example: `client:app` or `project.package.module:wrapper.app`",
+    )
+    parser.add_argument(
+        "--server-app",
+        required=True,
+        help="For example: `server:app` or `project.package.module:wrapper.app`",
+    )
+    parser.add_argument(
+        "--driver-api-address",
+        default="0.0.0.0:9091",
+        type=str,
+        help="For example: `server:app` or `project.package.module:wrapper.app`",
+    )
+    parser.add_argument(
+        "--num-supernodes",
+        type=int,
+        required=True,
+        help="Number of simulated SuperNodes.",
+    )
+    parser.add_argument(
+        "--backend",
+        default="ray",
+        type=str,
+        help="Simulation backend that executes the ClientApp.",
+    )
+    parser.add_argument(
+        "--enable-tf-gpu-growth",
+        action="store_true",
+        help="Enables GPU growth on the main thread. This is desirable if you make "
+        "use of a TensorFlow model on your `ServerApp` while having your `ClientApp` "
+        "running on the same GPU. Without enabling this, you might encounter an "
+        "out-of-memory error becasue TensorFlow by default allocates all GPU memory."
+        "Read mor about how `tf.config.experimental.set_memory_growth()` works in "
+        "the TensorFlow documentation: https://www.tensorflow.org/api/stable.",
+    )
+    parser.add_argument(
+        "--backend-config",
+        type=str,
+        default='{"client_resources": {"num_cpus":2, "num_gpus":0.0}, "tensorflow": 0}',
+        help='A JSON formatted stream, e.g \'{"<keyA>":<value>, "<keyB>":<value>}\' to '
+        "configure a backend. Values supported in <value> are those included by "
+        "`flwr.common.typing.ConfigsRecordValues`. ",
+    )
+    parser.add_argument(
+        "--dir",
+        default="",
+        help="Add specified directory to the PYTHONPATH and load"
+        "ClientApp and ServerApp from there."
+        " Default: current working directory.",
+    )
+
+    return parser

From da7125af0ab066c66fcc4d94c24fb2ab096737d4 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Fri, 1 Mar 2024 10:04:25 +0100
Subject: [PATCH 083/102] Define `Error` structure in `common.Message`. (#3034)

Co-authored-by: Daniel J. Beutel <daniel@flower.ai>
Co-authored-by: Heng Pan <pan@flower.ai>
---
 src/py/flwr/common/message.py            | 129 ++++++++++++++++++++---
 src/py/flwr/common/serde.py              |  54 ++++++++--
 src/py/flwr/common/serde_test.py         | 127 +++++++++++++++++-----
 src/py/flwr/server/driver/driver_test.py |  17 ++-
 4 files changed, 275 insertions(+), 52 deletions(-)

diff --git a/src/py/flwr/common/message.py b/src/py/flwr/common/message.py
index 1e1132e42e27..ecb1615a3441 100644
--- a/src/py/flwr/common/message.py
+++ b/src/py/flwr/common/message.py
@@ -154,6 +154,36 @@ def partition_id(self, value: int) -> None:
         self._partition_id = value
 
 
+@dataclass
+class Error:
+    """A dataclass that stores information about an error that ocurred.
+
+    Parameters
+    ----------
+    code : int
+        An identifier for the error.
+    reason : Optional[str]
+        A reason for why the error arised (e.g. an exception stack-trace)
+    """
+
+    _code: int
+    _reason: str | None = None
+
+    def __init__(self, code: int, reason: str | None = None) -> None:
+        self._code = code
+        self._reason = reason
+
+    @property
+    def code(self) -> int:
+        """Error code."""
+        return self._code
+
+    @property
+    def reason(self) -> str | None:
+        """Reason reported about the error."""
+        return self._reason
+
+
 @dataclass
 class Message:
     """State of your application from the viewpoint of the entity using it.
@@ -162,17 +192,31 @@ class Message:
     ----------
     metadata : Metadata
         A dataclass including information about the message to be executed.
-    content : RecordSet
+    content : Optional[RecordSet]
         Holds records either sent by another entity (e.g. sent by the server-side
         logic to a client, or vice-versa) or that will be sent to it.
+    error : Optional[Error]
+        A dataclass that captures information about an error that took place
+        when processing another message.
     """
 
     _metadata: Metadata
-    _content: RecordSet
+    _content: RecordSet | None = None
+    _error: Error | None = None
 
-    def __init__(self, metadata: Metadata, content: RecordSet) -> None:
+    def __init__(
+        self,
+        metadata: Metadata,
+        content: RecordSet | None = None,
+        error: Error | None = None,
+    ) -> None:
         self._metadata = metadata
+
+        if not (content is None) ^ (error is None):
+            raise ValueError("Either `content` or `error` must be set, but not both.")
+
         self._content = content
+        self._error = error
 
     @property
     def metadata(self) -> Metadata:
@@ -182,12 +226,77 @@ def metadata(self) -> Metadata:
     @property
     def content(self) -> RecordSet:
         """The content of this message."""
+        if self._content is None:
+            raise ValueError(
+                "Message content is None. Use <message>.has_content() "
+                "to check if a message has content."
+            )
         return self._content
 
     @content.setter
     def content(self, value: RecordSet) -> None:
         """Set content."""
-        self._content = value
+        if self._error is None:
+            self._content = value
+        else:
+            raise ValueError("A message with an error set cannot have content.")
+
+    @property
+    def error(self) -> Error:
+        """Error captured by this message."""
+        if self._error is None:
+            raise ValueError(
+                "Message error is None. Use <message>.has_error() "
+                "to check first if a message carries an error."
+            )
+        return self._error
+
+    @error.setter
+    def error(self, value: Error) -> None:
+        """Set error."""
+        if self.has_content():
+            raise ValueError("A message with content set cannot carry an error.")
+        self._error = value
+
+    def has_content(self) -> bool:
+        """Return True if message has content, else False."""
+        return self._content is not None
+
+    def has_error(self) -> bool:
+        """Return True if message has an error, else False."""
+        return self._error is not None
+
+    def _create_reply_metadata(self, ttl: str) -> Metadata:
+        """Construct metadata for a reply message."""
+        return Metadata(
+            run_id=self.metadata.run_id,
+            message_id="",
+            src_node_id=self.metadata.dst_node_id,
+            dst_node_id=self.metadata.src_node_id,
+            reply_to_message=self.metadata.message_id,
+            group_id=self.metadata.group_id,
+            ttl=ttl,
+            message_type=self.metadata.message_type,
+            partition_id=self.metadata.partition_id,
+        )
+
+    def create_error_reply(
+        self,
+        error: Error,
+        ttl: str,
+    ) -> Message:
+        """Construct a reply message indicating an error happened.
+
+        Parameters
+        ----------
+        error : Error
+            The error that was encountered.
+        ttl : str
+            Time-to-live for this message.
+        """
+        # Create reply with error
+        message = Message(metadata=self._create_reply_metadata(ttl), error=error)
+        return message
 
     def create_reply(self, content: RecordSet, ttl: str) -> Message:
         """Create a reply to this message with specified content and TTL.
@@ -209,16 +318,6 @@ def create_reply(self, content: RecordSet, ttl: str) -> Message:
             A new `Message` instance representing the reply.
         """
         return Message(
-            metadata=Metadata(
-                run_id=self.metadata.run_id,
-                message_id="",
-                src_node_id=self.metadata.dst_node_id,
-                dst_node_id=self.metadata.src_node_id,
-                reply_to_message=self.metadata.message_id,
-                group_id=self.metadata.group_id,
-                ttl=ttl,
-                message_type=self.metadata.message_type,
-                partition_id=self.metadata.partition_id,
-            ),
+            metadata=self._create_reply_metadata(ttl),
             content=content,
         )
diff --git a/src/py/flwr/common/serde.py b/src/py/flwr/common/serde.py
index 531a4bde6e9d..6c7a077d2f9f 100644
--- a/src/py/flwr/common/serde.py
+++ b/src/py/flwr/common/serde.py
@@ -20,6 +20,7 @@
 from google.protobuf.message import Message as GrpcMessage
 
 # pylint: disable=E0611
+from flwr.proto.error_pb2 import Error as ProtoError
 from flwr.proto.node_pb2 import Node
 from flwr.proto.recordset_pb2 import Array as ProtoArray
 from flwr.proto.recordset_pb2 import BoolList, BytesList
@@ -44,7 +45,7 @@
 
 # pylint: enable=E0611
 from . import Array, ConfigsRecord, MetricsRecord, ParametersRecord, RecordSet, typing
-from .message import Message, Metadata
+from .message import Error, Message, Metadata
 from .record.typeddict import TypedDict
 
 #  === Parameters message ===
@@ -512,6 +513,21 @@ def configs_record_from_proto(record_proto: ProtoConfigsRecord) -> ConfigsRecord
     )
 
 
+# === Error message ===
+
+
+def error_to_proto(error: Error) -> ProtoError:
+    """Serialize Error to ProtoBuf."""
+    reason = error.reason if error.reason else ""
+    return ProtoError(code=error.code, reason=reason)
+
+
+def error_from_proto(error_proto: ProtoError) -> Error:
+    """Deserialize Error from ProtoBuf."""
+    reason = error_proto.reason if len(error_proto.reason) > 0 else None
+    return Error(code=error_proto.code, reason=reason)
+
+
 # === RecordSet message ===
 
 
@@ -562,7 +578,10 @@ def message_to_taskins(message: Message) -> TaskIns:
             ttl=md.ttl,
             ancestry=[md.reply_to_message] if md.reply_to_message != "" else [],
             task_type=md.message_type,
-            recordset=recordset_to_proto(message.content),
+            recordset=(
+                recordset_to_proto(message.content) if message.has_content() else None
+            ),
+            error=error_to_proto(message.error) if message.has_error() else None,
         ),
     )
 
@@ -581,10 +600,19 @@ def message_from_taskins(taskins: TaskIns) -> Message:
         message_type=taskins.task.task_type,
     )
 
-    # Return the Message
+    # Construct Message
     return Message(
         metadata=metadata,
-        content=recordset_from_proto(taskins.task.recordset),
+        content=(
+            recordset_from_proto(taskins.task.recordset)
+            if taskins.task.HasField("recordset")
+            else None
+        ),
+        error=(
+            error_from_proto(taskins.task.error)
+            if taskins.task.HasField("error")
+            else None
+        ),
     )
 
 
@@ -601,7 +629,10 @@ def message_to_taskres(message: Message) -> TaskRes:
             ttl=md.ttl,
             ancestry=[md.reply_to_message] if md.reply_to_message != "" else [],
             task_type=md.message_type,
-            recordset=recordset_to_proto(message.content),
+            recordset=(
+                recordset_to_proto(message.content) if message.has_content() else None
+            ),
+            error=error_to_proto(message.error) if message.has_error() else None,
         ),
     )
 
@@ -620,8 +651,17 @@ def message_from_taskres(taskres: TaskRes) -> Message:
         message_type=taskres.task.task_type,
     )
 
-    # Return the Message
+    # Construct the Message
     return Message(
         metadata=metadata,
-        content=recordset_from_proto(taskres.task.recordset),
+        content=(
+            recordset_from_proto(taskres.task.recordset)
+            if taskres.task.HasField("recordset")
+            else None
+        ),
+        error=(
+            error_from_proto(taskres.task.error)
+            if taskres.task.HasField("error")
+            else None
+        ),
     )
diff --git a/src/py/flwr/common/serde_test.py b/src/py/flwr/common/serde_test.py
index 1f25fd1852c1..509ce2700948 100644
--- a/src/py/flwr/common/serde_test.py
+++ b/src/py/flwr/common/serde_test.py
@@ -14,10 +14,12 @@
 # ==============================================================================
 """(De-)serialization tests."""
 
-
 import random
 import string
-from typing import Any, Optional, OrderedDict, Type, TypeVar, Union, cast
+from contextlib import ExitStack
+from typing import Any, Callable, Optional, OrderedDict, Type, TypeVar, Union, cast
+
+import pytest
 
 # pylint: disable=E0611
 from flwr.proto import transport_pb2 as pb2
@@ -29,7 +31,7 @@
 
 # pylint: enable=E0611
 from . import Array, ConfigsRecord, MetricsRecord, ParametersRecord, RecordSet, typing
-from .message import Message, Metadata
+from .message import Error, Message, Metadata
 from .serde import (
     array_from_proto,
     array_to_proto,
@@ -298,44 +300,115 @@ def test_recordset_serialization_deserialization() -> None:
     assert original == deserialized
 
 
-def test_message_to_and_from_taskins() -> None:
+@pytest.mark.parametrize(
+    "content_fn, error_fn, context",
+    [
+        (
+            lambda maker: maker.recordset(1, 1, 1),
+            None,
+            None,
+        ),  # check when only content is set
+        (None, lambda code: Error(code=code), None),  # check when only error is set
+        (
+            lambda maker: maker.recordset(1, 1, 1),
+            lambda code: Error(code=code),
+            pytest.raises(ValueError),
+        ),  # check when both are set (ERROR)
+        (None, None, pytest.raises(ValueError)),  # check when neither is set (ERROR)
+    ],
+)
+def test_message_to_and_from_taskins(
+    content_fn: Callable[
+        [
+            RecordMaker,
+        ],
+        RecordSet,
+    ],
+    error_fn: Callable[[int], Error],
+    context: Any,
+) -> None:
     """Test Message to and from TaskIns."""
     # Prepare
+
     maker = RecordMaker(state=1)
     metadata = maker.metadata()
     # pylint: disable-next=protected-access
     metadata._src_node_id = 0  # Assume driver node
-    original = Message(
-        metadata=metadata,
-        content=maker.recordset(1, 1, 1),
-    )
 
-    # Execute
-    taskins = message_to_taskins(original)
-    taskins.task_id = metadata.message_id
-    deserialized = message_from_taskins(taskins)
+    with ExitStack() as stack:
+        if context:
+            stack.enter_context(context)
 
-    # Assert
-    assert original.content == deserialized.content
-    assert metadata == deserialized.metadata
+        original = Message(
+            metadata=metadata,
+            content=None if content_fn is None else content_fn(maker),
+            error=None if error_fn is None else error_fn(0),
+        )
 
+        # Execute
+        taskins = message_to_taskins(original)
+        taskins.task_id = metadata.message_id
+        deserialized = message_from_taskins(taskins)
 
-def test_message_to_and_from_taskres() -> None:
+        # Assert
+        if original.has_content():
+            assert original.content == deserialized.content
+        if original.has_error():
+            assert original.error == deserialized.error
+        assert metadata == deserialized.metadata
+
+
+@pytest.mark.parametrize(
+    "content_fn, error_fn, context",
+    [
+        (
+            lambda maker: maker.recordset(1, 1, 1),
+            None,
+            None,
+        ),  # check when only content is set
+        (None, lambda code: Error(code=code), None),  # check when only error is set
+        (
+            lambda maker: maker.recordset(1, 1, 1),
+            lambda code: Error(code=code),
+            pytest.raises(ValueError),
+        ),  # check when both are set (ERROR)
+        (None, None, pytest.raises(ValueError)),  # check when neither is set (ERROR)
+    ],
+)
+def test_message_to_and_from_taskres(
+    content_fn: Callable[
+        [
+            RecordMaker,
+        ],
+        RecordSet,
+    ],
+    error_fn: Callable[[int], Error],
+    context: Any,
+) -> None:
     """Test Message to and from TaskRes."""
     # Prepare
     maker = RecordMaker(state=2)
     metadata = maker.metadata()
     metadata.dst_node_id = 0  # Assume driver node
-    original = Message(
-        metadata=metadata,
-        content=maker.recordset(1, 1, 1),
-    )
 
-    # Execute
-    taskres = message_to_taskres(original)
-    taskres.task_id = metadata.message_id
-    deserialized = message_from_taskres(taskres)
+    with ExitStack() as stack:
+        if context:
+            stack.enter_context(context)
 
-    # Assert
-    assert original.content == deserialized.content
-    assert metadata == deserialized.metadata
+        original = Message(
+            metadata=metadata,
+            content=None if content_fn is None else content_fn(maker),
+            error=None if error_fn is None else error_fn(0),
+        )
+
+        # Execute
+        taskres = message_to_taskres(original)
+        taskres.task_id = metadata.message_id
+        deserialized = message_from_taskres(taskres)
+
+        # Assert
+        if original.has_content():
+            assert original.content == deserialized.content
+        if original.has_error():
+            assert original.error == deserialized.error
+        assert metadata == deserialized.metadata
diff --git a/src/py/flwr/server/driver/driver_test.py b/src/py/flwr/server/driver/driver_test.py
index bd5c23a407fd..2bf253222f94 100644
--- a/src/py/flwr/server/driver/driver_test.py
+++ b/src/py/flwr/server/driver/driver_test.py
@@ -20,6 +20,8 @@
 from unittest.mock import Mock, patch
 
 from flwr.common import RecordSet
+from flwr.common.message import Error
+from flwr.common.serde import error_to_proto, recordset_to_proto
 from flwr.proto.driver_pb2 import (  # pylint: disable=E0611
     GetNodesRequest,
     PullTaskResRequest,
@@ -132,9 +134,13 @@ def test_pull_messages_with_given_message_ids(self) -> None:
         """Test pulling messages with specific message IDs."""
         # Prepare
         mock_response = Mock()
+        # A Message must have either content or error set so we prepare
+        # two tasks that contain these.
         mock_response.task_res_list = [
-            TaskRes(task=Task(ancestry=["id2"])),
-            TaskRes(task=Task(ancestry=["id3"])),
+            TaskRes(
+                task=Task(ancestry=["id2"], recordset=recordset_to_proto(RecordSet()))
+            ),
+            TaskRes(task=Task(ancestry=["id3"], error=error_to_proto(Error(code=0)))),
         ]
         self.mock_grpc_driver.pull_task_res.return_value = mock_response
         msg_ids = ["id1", "id2", "id3"]
@@ -157,7 +163,12 @@ def test_send_and_receive_messages_complete(self) -> None:
         # Prepare
         mock_response = Mock(task_ids=["id1"])
         self.mock_grpc_driver.push_task_ins.return_value = mock_response
-        mock_response = Mock(task_res_list=[TaskRes(task=Task(ancestry=["id1"]))])
+        # The response message must include either `content` (i.e. a recordset) or
+        # an `Error`. We choose the latter in this case
+        error_proto = error_to_proto(Error(code=0))
+        mock_response = Mock(
+            task_res_list=[TaskRes(task=Task(ancestry=["id1"], error=error_proto))]
+        )
         self.mock_grpc_driver.pull_task_res.return_value = mock_response
         msgs = [self.driver.create_message(RecordSet(), "", 0, "", "")]
 

From d767c922ac93b878710efa38a6c532b99d789892 Mon Sep 17 00:00:00 2001
From: Danny <volleyballlive@googlemail.com>
Date: Fri, 1 Mar 2024 14:46:37 +0100
Subject: [PATCH 084/102] Fix typos in docs/source/* (#3045)

---
 doc/source/contributor-how-to-create-new-messages.rst  |  2 +-
 doc/source/contributor-how-to-release-flower.rst       |  6 +++---
 ...ntributor-tutorial-get-started-as-a-contributor.rst |  2 +-
 doc/source/example-mxnet-walk-through.rst              |  2 +-
 .../example-pytorch-from-centralized-to-federated.rst  |  8 ++++----
 doc/source/example-walkthrough-pytorch-mnist.rst       |  4 ++--
 doc/source/explanation-federated-evaluation.rst        |  2 +-
 doc/source/how-to-configure-logging.rst                |  2 +-
 doc/source/how-to-implement-strategies.rst             |  4 ++--
 doc/source/how-to-monitor-simulation.rst               |  4 ++--
 doc/source/how-to-upgrade-to-flower-1.0.rst            |  4 ++--
 doc/source/how-to-use-strategies.rst                   |  2 +-
 doc/source/ref-changelog.md                            | 10 +++++-----
 .../tutorial-series-customize-the-client-pytorch.ipynb |  6 +++---
 ...torial-series-get-started-with-flower-pytorch.ipynb |  2 +-
 .../tutorial-series-what-is-federated-learning.ipynb   |  2 +-
 16 files changed, 31 insertions(+), 31 deletions(-)

diff --git a/doc/source/contributor-how-to-create-new-messages.rst b/doc/source/contributor-how-to-create-new-messages.rst
index 5d9f4600361c..3f1849bdce47 100644
--- a/doc/source/contributor-how-to-create-new-messages.rst
+++ b/doc/source/contributor-how-to-create-new-messages.rst
@@ -75,7 +75,7 @@ Once that is done, we will compile the file with:
 
   $ python -m flwr_tool.protoc
 
-If it compiles succesfully, you should see the following message:
+If it compiles successfully, you should see the following message:
 
 .. code-block:: shell
 
diff --git a/doc/source/contributor-how-to-release-flower.rst b/doc/source/contributor-how-to-release-flower.rst
index acfac4197ec1..4853d87bc4c1 100644
--- a/doc/source/contributor-how-to-release-flower.rst
+++ b/doc/source/contributor-how-to-release-flower.rst
@@ -22,7 +22,7 @@ Create a pull request which contains the following changes:
 2. Update all files which contain the current version number if necessary.
 3. Add a new ``Unreleased`` section in ``changelog.md``.
 
-Merge the pull request on the same day (i.e., before a new nighly release gets published to PyPI).
+Merge the pull request on the same day (i.e., before a new nightly release gets published to PyPI).
 
 Publishing a pre-release
 ------------------------
@@ -30,11 +30,11 @@ Publishing a pre-release
 Pre-release naming
 ~~~~~~~~~~~~~~~~~~
 
-PyPI supports pre-releases (alpha, beta, release candiate). Pre-releases MUST use one of the following naming patterns:
+PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases MUST use one of the following naming patterns:
 
 - Alpha: ``MAJOR.MINOR.PATCHaN``
 - Beta: ``MAJOR.MINOR.PATCHbN``
-- Release candiate (RC): ``MAJOR.MINOR.PATCHrcN``
+- Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``
 
 Examples include:
 
diff --git a/doc/source/contributor-tutorial-get-started-as-a-contributor.rst b/doc/source/contributor-tutorial-get-started-as-a-contributor.rst
index 01810c7244d3..9136fea96bf6 100644
--- a/doc/source/contributor-tutorial-get-started-as-a-contributor.rst
+++ b/doc/source/contributor-tutorial-get-started-as-a-contributor.rst
@@ -70,7 +70,7 @@ Convenience Scripts
 
 The Flower repository contains a number of convenience scripts to make
 recurring development tasks easier and less error-prone. See the :code:`/dev`
-subdirectory for a full list. The following scripts are amonst the most
+subdirectory for a full list. The following scripts are amongst the most
 important ones:
 
 Create/Delete Virtual Environment
diff --git a/doc/source/example-mxnet-walk-through.rst b/doc/source/example-mxnet-walk-through.rst
index a395a8a723cb..c215f709ffb2 100644
--- a/doc/source/example-mxnet-walk-through.rst
+++ b/doc/source/example-mxnet-walk-through.rst
@@ -234,7 +234,7 @@ Our *client* needs to import :code:`flwr`, but also :code:`mxnet` to update the
 
 Implementing a Flower *client* basically means implementing a subclass of either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`.
 Our implementation will be based on :code:`flwr.client.NumPyClient` and we'll call it :code:`MNISTClient`.
-:code:`NumPyClient` is slighly easier to implement than :code:`Client` if you use a framework with good NumPy interoperability (like PyTorch or MXNet) because it avoids some of the boilerplate that would otherwise be necessary.
+:code:`NumPyClient` is slightly easier to implement than :code:`Client` if you use a framework with good NumPy interoperability (like PyTorch or MXNet) because it avoids some of the boilerplate that would otherwise be necessary.
 :code:`MNISTClient` needs to implement four methods, two methods for getting/setting model parameters, one method for training the model, and one method for testing the model:
 
 #. :code:`set_parameters (optional)`
diff --git a/doc/source/example-pytorch-from-centralized-to-federated.rst b/doc/source/example-pytorch-from-centralized-to-federated.rst
index 58101135b8c0..0c458a136a81 100644
--- a/doc/source/example-pytorch-from-centralized-to-federated.rst
+++ b/doc/source/example-pytorch-from-centralized-to-federated.rst
@@ -174,7 +174,7 @@ However, with Flower you can evolve your pre-existing code into a federated lear
 
 The concept is easy to understand.
 We have to start a *server* and then use the code in :code:`cifar.py` for the *clients* that are connected to the *server*.
-The *server* sends model parameters to the clients. The *clients* run the training and update the paramters.
+The *server* sends model parameters to the clients. The *clients* run the training and update the parameters.
 The updated parameters are sent back to the *server* which averages all received parameter updates.
 This describes one round of the federated learning process and we repeat this for multiple rounds. 
 
@@ -195,7 +195,7 @@ We can already start the *server*:
     python3 server.py
 
 Finally, we will define our *client* logic in :code:`client.py` and build upon the previously defined centralized training in :code:`cifar.py`.
-Our *client* needs to import :code:`flwr`, but also :code:`torch` to update the paramters on our PyTorch model:
+Our *client* needs to import :code:`flwr`, but also :code:`torch` to update the parameters on our PyTorch model:
 
 .. code-block:: python
 
@@ -212,7 +212,7 @@ Our *client* needs to import :code:`flwr`, but also :code:`torch` to update the
 
 Implementing a Flower *client* basically means implementing a subclass of either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`.
 Our implementation will be based on :code:`flwr.client.NumPyClient` and we'll call it :code:`CifarClient`.
-:code:`NumPyClient` is slighly easier to implement than :code:`Client` if you use a framework with good NumPy interoperability (like PyTorch or TensorFlow/Keras) because it avoids some of the boilerplate that would otherwise be necessary.
+:code:`NumPyClient` is slightly easier to implement than :code:`Client` if you use a framework with good NumPy interoperability (like PyTorch or TensorFlow/Keras) because it avoids some of the boilerplate that would otherwise be necessary.
 :code:`CifarClient` needs to implement four methods, two methods for getting/setting model parameters, one method for training the model, and one method for testing the model:
 
 #. :code:`set_parameters`
@@ -278,7 +278,7 @@ We included type annotations to give you a better understanding of the data type
             return float(loss), self.num_examples["testset"], {"accuracy": float(accuracy)}
 
 All that's left to do it to define a function that loads both model and data, creates a :code:`CifarClient`, and starts this client.
-You load your data and model by using :code:`cifar.py`. Start :code:`CifarClient` with the function :code:`fl.client.start_client()` by pointing it at the same IP adress we used in :code:`server.py`: 
+You load your data and model by using :code:`cifar.py`. Start :code:`CifarClient` with the function :code:`fl.client.start_client()` by pointing it at the same IP address we used in :code:`server.py`: 
 
 .. code-block:: python
 
diff --git a/doc/source/example-walkthrough-pytorch-mnist.rst b/doc/source/example-walkthrough-pytorch-mnist.rst
index 8717c196f043..f8eacc8647fe 100644
--- a/doc/source/example-walkthrough-pytorch-mnist.rst
+++ b/doc/source/example-walkthrough-pytorch-mnist.rst
@@ -332,7 +332,7 @@ These functions can both be found inside the same :code:`quickstart-pytorch.mnis
             Neural network model used in this example.
 
         train_loader: torch.utils.data.DataLoader
-            DataLoader used in traning.
+            DataLoader used in training.
 
         epochs: int
             Number of epochs to run in each round.
@@ -344,7 +344,7 @@ These functions can both be found inside the same :code:`quickstart-pytorch.mnis
         Returns
         -------
         num_examples_train: int
-            Number of total samples used during traning.
+            Number of total samples used during training.
 
         """
         model.train()
diff --git a/doc/source/explanation-federated-evaluation.rst b/doc/source/explanation-federated-evaluation.rst
index 632241f70d36..bcdca9bae700 100644
--- a/doc/source/explanation-federated-evaluation.rst
+++ b/doc/source/explanation-federated-evaluation.rst
@@ -120,7 +120,7 @@ Federated evaluation can be configured from the server side. Built-in strategies
 
     # Create strategy
     strategy = fl.server.strategy.FedAvg(
-        # ... other FedAvg agruments
+        # ... other FedAvg arguments
         fraction_evaluate=0.2,
         min_evaluate_clients=2,
         min_available_clients=10,
diff --git a/doc/source/how-to-configure-logging.rst b/doc/source/how-to-configure-logging.rst
index 3fcfe85b9592..d5559429a73c 100644
--- a/doc/source/how-to-configure-logging.rst
+++ b/doc/source/how-to-configure-logging.rst
@@ -129,4 +129,4 @@ Log to a remote service
 
 The :code:`fl.common.logger.configure` function, also allows specifying a host to which logs can be pushed (via :code:`POST`) through a native Python :code:`logging.handler.HTTPHandler`.
 This is a particularly useful feature in :code:`gRPC`-based Federated Learning workloads where otherwise gathering logs from all entities (i.e. the server and the clients) might be cumbersome.
-Note that in Flower simulation, the server automatically displays all logs. You can still specify a :code:`HTTPHandler` should you whish to backup or analyze the logs somewhere else.
+Note that in Flower simulation, the server automatically displays all logs. You can still specify a :code:`HTTPHandler` should you wish to backup or analyze the logs somewhere else.
diff --git a/doc/source/how-to-implement-strategies.rst b/doc/source/how-to-implement-strategies.rst
index 7997503b65a8..01bbb3042973 100644
--- a/doc/source/how-to-implement-strategies.rst
+++ b/doc/source/how-to-implement-strategies.rst
@@ -233,7 +233,7 @@ The return value is a list of tuples, each representing the instructions that wi
 * Use the :code:`client_manager` to randomly sample all (or a subset of) available clients (each represented as a :code:`ClientProxy` object)
 * Pair each :code:`ClientProxy` with the same :code:`FitIns` holding the current global model :code:`parameters` and :code:`config` dict
 
-More sophisticated implementations can use :code:`configure_fit` to implement custom client selection logic. A client will only participate in a round if the corresponding :code:`ClientProxy` is included in the the list returned from :code:`configure_fit`.
+More sophisticated implementations can use :code:`configure_fit` to implement custom client selection logic. A client will only participate in a round if the corresponding :code:`ClientProxy` is included in the list returned from :code:`configure_fit`.
 
 .. note::
 
@@ -280,7 +280,7 @@ The return value is a list of tuples, each representing the instructions that wi
 * Use the :code:`client_manager` to randomly sample all (or a subset of) available clients (each represented as a :code:`ClientProxy` object)
 * Pair each :code:`ClientProxy` with the same :code:`EvaluateIns` holding the current global model :code:`parameters` and :code:`config` dict
 
-More sophisticated implementations can use :code:`configure_evaluate` to implement custom client selection logic. A client will only participate in a round if the corresponding :code:`ClientProxy` is included in the the list returned from :code:`configure_evaluate`.
+More sophisticated implementations can use :code:`configure_evaluate` to implement custom client selection logic. A client will only participate in a round if the corresponding :code:`ClientProxy` is included in the list returned from :code:`configure_evaluate`.
 
 .. note::
 
diff --git a/doc/source/how-to-monitor-simulation.rst b/doc/source/how-to-monitor-simulation.rst
index 61a3be68deec..f6c26a701d94 100644
--- a/doc/source/how-to-monitor-simulation.rst
+++ b/doc/source/how-to-monitor-simulation.rst
@@ -161,7 +161,7 @@ However, you can overwrite the defaults. When starting a simulation, do the foll
   ram_memory = 16_000 * 1024 * 1024  # 16 GB
   fl.simulation.start_simulation(
       # ...
-      # all the args you were specyfing before
+      # all the args you were specifying before
       # ...
       ray_init_args = {
               "include_dashboard": True, # we need this one for tracking
@@ -187,7 +187,7 @@ Let’s also specify the resource for a single client.
 
   fl.simulation.start_simulation(
       # ...
-      # all the args you were specyfing before
+      # all the args you were specifying before
       # ...
       ray_init_args = {
               "include_dashboard": True, # we need this one for tracking
diff --git a/doc/source/how-to-upgrade-to-flower-1.0.rst b/doc/source/how-to-upgrade-to-flower-1.0.rst
index c4429d61d0a9..3a55a1a953f5 100644
--- a/doc/source/how-to-upgrade-to-flower-1.0.rst
+++ b/doc/source/how-to-upgrade-to-flower-1.0.rst
@@ -81,11 +81,11 @@ Optional improvements
 
 Along with the necessary changes above, there are a number of potential improvements that just became possible:
 
-- Remove "placeholder" methods from subclasses of ``Client`` or ``NumPyClient``. If you, for example, use server-side evaluation, then empy placeholder implementations of ``evaluate`` are no longer necessary.
+- Remove "placeholder" methods from subclasses of ``Client`` or ``NumPyClient``. If you, for example, use server-side evaluation, then empty placeholder implementations of ``evaluate`` are no longer necessary.
 - Configure the round timeout via ``start_simulation``: ``start_simulation(..., config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), ...)``
 
 
 Further help
 ------------
 
-Most official `Flower code examples <https://github.com/adap/flower/tree/main/examples>`_ are already updated to Flower 1.0, they can serve as a reference for using the Flower 1.0 API. If there are further questionsm, `join the Flower Slack <https://flower.ai/join-slack/>`_ and use the channgel ``#questions``.
+Most official `Flower code examples <https://github.com/adap/flower/tree/main/examples>`_ are already updated to Flower 1.0, they can serve as a reference for using the Flower 1.0 API. If there are further questions, `join the Flower Slack <https://flower.ai/join-slack/>`_ and use the channel ``#questions``.
diff --git a/doc/source/how-to-use-strategies.rst b/doc/source/how-to-use-strategies.rst
index 6d24f97bd7f6..d0e2cd63a091 100644
--- a/doc/source/how-to-use-strategies.rst
+++ b/doc/source/how-to-use-strategies.rst
@@ -45,7 +45,7 @@ Configuring client fit and client evaluate
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The server can pass new configuration values to the client each round by providing a function to :code:`on_fit_config_fn`. The provided function will be called by the strategy and must return a dictionary of configuration key values pairs that will be sent to the client.
-It must return a dictionary of arbitraty configuration values  :code:`client.fit` and :code:`client.evaluate` functions during each round of federated learning. 
+It must return a dictionary of arbitrary configuration values  :code:`client.fit` and :code:`client.evaluate` functions during each round of federated learning. 
 
 .. code-block:: python
 
diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 54092e15a564..b4bd4a5b89e0 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -26,7 +26,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Support Federated Learning with Apple MLX and Flower** ([#2693](https://github.com/adap/flower/pull/2693))
 
-  Flower has official support for federated learning using [Appple MLX](https://ml-explore.github.io/mlx) via the new `quickstart-mlx` code example.
+  Flower has official support for federated learning using [Apple MLX](https://ml-explore.github.io/mlx) via the new `quickstart-mlx` code example.
 
 - **Introduce new XGBoost cyclic strategy** ([#2666](https://github.com/adap/flower/pull/2666), [#2668](https://github.com/adap/flower/pull/2668))
 
@@ -307,7 +307,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Introduce new Flower Baseline: FedProx MNIST** ([#1513](https://github.com/adap/flower/pull/1513), [#1680](https://github.com/adap/flower/pull/1680), [#1681](https://github.com/adap/flower/pull/1681), [#1679](https://github.com/adap/flower/pull/1679))
 
-  This new baseline replicates the MNIST+CNN task from the paper [Federated Optimization in Heterogeneous Networks (Li et al., 2018)](https://arxiv.org/abs/1812.06127). It uses the `FedProx` strategy, which aims at making convergence more robust in heterogenous settings.
+  This new baseline replicates the MNIST+CNN task from the paper [Federated Optimization in Heterogeneous Networks (Li et al., 2018)](https://arxiv.org/abs/1812.06127). It uses the `FedProx` strategy, which aims at making convergence more robust in heterogeneous settings.
 
 - **Introduce new Flower Baseline: FedAvg FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))
 
@@ -823,7 +823,7 @@ We would like to give our **special thanks** to all the contributors who made Fl
 
 - **Renamed q-FedAvg strategy** ([#802](https://github.com/adap/flower/pull/802))
 
-  The strategy named `QffedAvg` was renamed to `QFedAvg` to better reflect the notation given in the original paper (q-FFL is the optimization objective, q-FedAvg is the proposed solver). Note the the original (now deprecated) `QffedAvg` class is still available for compatibility reasons (it will be removed in a future release).
+  The strategy named `QffedAvg` was renamed to `QFedAvg` to better reflect the notation given in the original paper (q-FFL is the optimization objective, q-FedAvg is the proposed solver). Note the original (now deprecated) `QffedAvg` class is still available for compatibility reasons (it will be removed in a future release).
 
 - **Deprecated and renamed code example** `simulation_pytorch` **to** `simulation_pytorch_legacy` ([#791](https://github.com/adap/flower/pull/791))
 
@@ -864,7 +864,7 @@ We would like to give our **special thanks** to all the contributors who made Fl
 
   The Flower server is now fully serialization-agnostic. Prior usage of class `Weights` (which represents parameters as deserialized NumPy ndarrays) was replaced by class `Parameters` (e.g., in `Strategy`). `Parameters` objects are fully serialization-agnostic and represents parameters as byte arrays, the `tensor_type` attributes indicates how these byte arrays should be interpreted (e.g., for serialization/deserialization).
 
-  Built-in strategies implement this approach by handling serialization and deserialization to/from `Weights` internally. Custom/3rd-party Strategy implementations should update to the slighly changed Strategy method definitions. Strategy authors can consult PR [#721](https://github.com/adap/flower/pull/721) to see how strategies can easily migrate to the new format.
+  Built-in strategies implement this approach by handling serialization and deserialization to/from `Weights` internally. Custom/3rd-party Strategy implementations should update to the slightly changed Strategy method definitions. Strategy authors can consult PR [#721](https://github.com/adap/flower/pull/721) to see how strategies can easily migrate to the new format.
 
 - Deprecated `flwr.server.Server.evaluate`, use `flwr.server.Server.evaluate_round` instead ([#717](https://github.com/adap/flower/pull/717))
 
@@ -885,7 +885,7 @@ What's new?
   )
   model.compile("adam", "sparse_categorical_crossentropy", metrics=["accuracy"])
 
-  # Create strategy and initilize parameters on the server-side
+  # Create strategy and initialize parameters on the server-side
   strategy = fl.server.strategy.FedAvg(
       # ... (other constructor arguments)
       initial_parameters=model.get_weights(),
diff --git a/doc/source/tutorial-series-customize-the-client-pytorch.ipynb b/doc/source/tutorial-series-customize-the-client-pytorch.ipynb
index ce09c6cc46c1..dbdd1094173c 100644
--- a/doc/source/tutorial-series-customize-the-client-pytorch.ipynb
+++ b/doc/source/tutorial-series-customize-the-client-pytorch.ipynb
@@ -500,7 +500,7 @@
     "        # We convert our ndarray into a sparse matrix\n",
     "        ndarray = torch.tensor(ndarray).to_sparse_csr()\n",
     "\n",
-    "        # And send it by utilizng the sparse matrix attributes\n",
+    "        # And send it byutilizing the sparse matrix attributes\n",
     "        # WARNING: NEVER set allow_pickle to true.\n",
     "        # Reason: loading pickled data can execute arbitrary code\n",
     "        # Source: https://numpy.org/doc/stable/reference/generated/numpy.save.html\n",
@@ -550,7 +550,7 @@
    "source": [
     "### Client-side\n",
     "\n",
-    "To be able to able to serialize our `ndarray`s into sparse parameters, we will just have to call our custom functions in our `flwr.client.Client`.\n",
+    "To be able to serialize our `ndarray`s into sparse parameters, we will just have to call our custom functions in our `flwr.client.Client`.\n",
     "\n",
     "Indeed, in `get_parameters` we need to serialize the parameters we got from our network using our custom `ndarrays_to_sparse_parameters` defined above.\n",
     "\n",
@@ -813,7 +813,7 @@
     "            for _, fit_res in results\n",
     "        ]\n",
     "\n",
-    "        # We serialize the aggregated result using our cutom method\n",
+    "        # We serialize the aggregated result using our custom method\n",
     "        parameters_aggregated = ndarrays_to_sparse_parameters(\n",
     "            aggregate(weights_results)\n",
     "        )\n",
diff --git a/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb b/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb
index 205531c54ee6..fab3dafba5e5 100644
--- a/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb
+++ b/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb
@@ -488,7 +488,7 @@
     "# client will be allocated 1x CPU and 0x GPUs\n",
     "client_resources = {\"num_cpus\": 1, \"num_gpus\": 0.0}\n",
     "if DEVICE.type == \"cuda\":\n",
-    "    # here we are asigning an entire GPU for each client.\n",
+    "    # here we are assigning an entire GPU for each client.\n",
     "    client_resources = {\"num_cpus\": 1, \"num_gpus\": 1.0}\n",
     "    # Refer to our documentation for more details about Flower Simulations\n",
     "    # and how to setup these `client_resources`.\n",
diff --git a/doc/source/tutorial-series-what-is-federated-learning.ipynb b/doc/source/tutorial-series-what-is-federated-learning.ipynb
index d77182838f21..7d77d1770457 100755
--- a/doc/source/tutorial-series-what-is-federated-learning.ipynb
+++ b/doc/source/tutorial-series-what-is-federated-learning.ipynb
@@ -98,7 +98,7 @@
     "- Location data from your electric car to make better range prediction\n",
     "- End-to-end encrypted messages to train better auto-complete models\n",
     "\n",
-    "The popularity of privacy-enhancing systems like the [Brave](https://brave.com/) browser or the [Signal](https://signal.org/) messenger shows that users care about privacy. In fact, they choose the privacy-enhancing version over other alternatives, if such an alernative exists. But what can we do to apply machine learning and data science to these cases to utilize private data? After all, these are all areas that would benefit significantly from recent advances in AI."
+    "The popularity of privacy-enhancing systems like the [Brave](https://brave.com/) browser or the [Signal](https://signal.org/) messenger shows that users care about privacy. In fact, they choose the privacy-enhancing version over other alternatives, if such an alternative exists. But what can we do to apply machine learning and data science to these cases to utilize private data? After all, these are all areas that would benefit significantly from recent advances in AI."
    ]
   },
   {

From 950088d200b0e1564bc357496ccb7b7794fa730e Mon Sep 17 00:00:00 2001
From: Danny <volleyballlive@googlemail.com>
Date: Fri, 1 Mar 2024 15:00:49 +0100
Subject: [PATCH 085/102] Fix typos in docs/locals/* (#3044)

---
 doc/locales/fr/LC_MESSAGES/framework-docs.po  | 52 +++++++++----------
 .../pt_BR/LC_MESSAGES/framework-docs.po       | 44 ++++++++--------
 .../zh_Hans/LC_MESSAGES/framework-docs.po     | 48 ++++++++---------
 3 files changed, 72 insertions(+), 72 deletions(-)

diff --git a/doc/locales/fr/LC_MESSAGES/framework-docs.po b/doc/locales/fr/LC_MESSAGES/framework-docs.po
index cace04c6980f..fbceb3bc4c07 100644
--- a/doc/locales/fr/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/fr/LC_MESSAGES/framework-docs.po
@@ -473,7 +473,7 @@ msgid "Once that is done, we will compile the file with:"
 msgstr "Une fois que c'est fait, nous compilerons le fichier avec :"
 
 #: ../../source/contributor-how-to-create-new-messages.rst:78
-msgid "If it compiles succesfully, you should see the following message:"
+msgid "If it compiles successfully, you should see the following message:"
 msgstr "S'il se compile avec succès, tu devrais voir le message suivant :"
 
 #: ../../source/contributor-how-to-create-new-messages.rst:87
@@ -961,11 +961,11 @@ msgstr "Ajoute une nouvelle section ``Unreleased`` dans ``changelog.md``."
 
 #: ../../source/contributor-how-to-release-flower.rst:25
 msgid ""
-"Merge the pull request on the same day (i.e., before a new nighly release"
+"Merge the pull request on the same day (i.e., before a new nightly release"
 " gets published to PyPI)."
 msgstr ""
 "Fusionne la pull request le jour même (c'est-à-dire avant qu'une nouvelle"
-" version nighly ne soit publiée sur PyPI)."
+" version nightly ne soit publiée sur PyPI)."
 
 #: ../../source/contributor-how-to-release-flower.rst:28
 msgid "Publishing a pre-release"
@@ -977,10 +977,10 @@ msgstr "Nom de la pré-version"
 
 #: ../../source/contributor-how-to-release-flower.rst:33
 msgid ""
-"PyPI supports pre-releases (alpha, beta, release candiate). Pre-releases "
+"PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases "
 "MUST use one of the following naming patterns:"
 msgstr ""
-"PyPI prend en charge les préversions (alpha, bêta, version candiate). Les"
+"PyPI prend en charge les préversions (alpha, bêta, version candidate). Les"
 " préversions DOIVENT utiliser l'un des modèles de dénomination suivants :"
 
 #: ../../source/contributor-how-to-release-flower.rst:35
@@ -992,7 +992,7 @@ msgid "Beta: ``MAJOR.MINOR.PATCHbN``"
 msgstr "Bêta : ``MAJOR.MINOR.PATCHbN``"
 
 #: ../../source/contributor-how-to-release-flower.rst:37
-msgid "Release candiate (RC): ``MAJOR.MINOR.PATCHrcN``"
+msgid "Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``"
 msgstr "Candidat à la publication (RC) : ``MAJOR.MINOR.PATCHrcN``"
 
 #: ../../source/contributor-how-to-release-flower.rst:39
@@ -2547,7 +2547,7 @@ msgid ""
 "The Flower repository contains a number of convenience scripts to make "
 "recurring development tasks easier and less error-prone. See the "
 ":code:`/dev` subdirectory for a full list. The following scripts are "
-"amonst the most important ones:"
+"amongst the most important ones:"
 msgstr ""
 "La repo de Flower contient un certain nombre de scripts de commodité pour"
 " rendre les tâches de développement récurrentes plus faciles et moins "
@@ -3446,7 +3446,7 @@ msgid ""
 "Implementing a Flower *client* basically means implementing a subclass of"
 " either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. "
 "Our implementation will be based on :code:`flwr.client.NumPyClient` and "
-"we'll call it :code:`MNISTClient`. :code:`NumPyClient` is slighly easier "
+"we'll call it :code:`MNISTClient`. :code:`NumPyClient` is slightly easier "
 "to implement than :code:`Client` if you use a framework with good NumPy "
 "interoperability (like PyTorch or MXNet) because it avoids some of the "
 "boilerplate that would otherwise be necessary. :code:`MNISTClient` needs "
@@ -3457,7 +3457,7 @@ msgstr ""
 "Implementing a Flower *client* basically means implementing a subclass of"
 " either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. "
 "Our implementation will be based on :code:`flwr.client.NumPyClient` and "
-"we'll call it :code:`MNISTClient`. :code:`NumPyClient` is slighly easier "
+"we'll call it :code:`MNISTClient`. :code:`NumPyClient` is slightly easier "
 "to implement than :code:`Client` if you use a framework with good NumPy "
 "interoperability (like PyTorch or MXNet) because it avoids some of the "
 "boilerplate that would otherwise be necessary. :code:`MNISTClient` needs "
@@ -3698,7 +3698,7 @@ msgid ""
 "The concept is easy to understand. We have to start a *server* and then "
 "use the code in :code:`cifar.py` for the *clients* that are connected to "
 "the *server*. The *server* sends model parameters to the clients. The "
-"*clients* run the training and update the paramters. The updated "
+"*clients* run the training and update the parameters. The updated "
 "parameters are sent back to the *server* which averages all received "
 "parameter updates. This describes one round of the federated learning "
 "process and we repeat this for multiple rounds."
@@ -3717,7 +3717,7 @@ msgid ""
 "Finally, we will define our *client* logic in :code:`client.py` and build"
 " upon the previously defined centralized training in :code:`cifar.py`. "
 "Our *client* needs to import :code:`flwr`, but also :code:`torch` to "
-"update the paramters on our PyTorch model:"
+"update the parameters on our PyTorch model:"
 msgstr ""
 "Enfin, nous allons définir notre logique *client* dans :code:`client.py` "
 "et nous appuyer sur la formation centralisée définie précédemment dans "
@@ -3729,7 +3729,7 @@ msgid ""
 "Implementing a Flower *client* basically means implementing a subclass of"
 " either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. "
 "Our implementation will be based on :code:`flwr.client.NumPyClient` and "
-"we'll call it :code:`CifarClient`. :code:`NumPyClient` is slighly easier "
+"we'll call it :code:`CifarClient`. :code:`NumPyClient` is slightly easier "
 "to implement than :code:`Client` if you use a framework with good NumPy "
 "interoperability (like PyTorch or TensorFlow/Keras) because it avoids "
 "some of the boilerplate that would otherwise be necessary. "
@@ -3740,7 +3740,7 @@ msgstr ""
 "Implementing a Flower *client* basically means implementing a subclass of"
 " either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. "
 "Our implementation will be based on :code:`flwr.client.NumPyClient` and "
-"we'll call it :code:`CifarClient`. :code:`NumPyClient` is slighly easier "
+"we'll call it :code:`CifarClient`. :code:`NumPyClient` is slightly easier "
 "to implement than :code:`Client` if you use a framework with good NumPy "
 "interoperability (like PyTorch or TensorFlow/Keras) because it avoids "
 "some of the boilerplate that would otherwise be necessary. "
@@ -5695,7 +5695,7 @@ msgid ""
 "otherwise gathering logs from all entities (i.e. the server and the "
 "clients) might be cumbersome. Note that in Flower simulation, the server "
 "automatically displays all logs. You can still specify a "
-":code:`HTTPHandler` should you whish to backup or analyze the logs "
+":code:`HTTPHandler` should you wish to backup or analyze the logs "
 "somewhere else."
 msgstr ""
 
@@ -7695,7 +7695,7 @@ msgstr ""
 msgid ""
 "Remove \"placeholder\" methods from subclasses of ``Client`` or "
 "``NumPyClient``. If you, for example, use server-side evaluation, then "
-"empy placeholder implementations of ``evaluate`` are no longer necessary."
+"empty placeholder implementations of ``evaluate`` are no longer necessary."
 msgstr ""
 "Supprime les méthodes \"placeholder\" des sous-classes de ``Client`` ou "
 "de ``NumPyClient``. Si tu utilises, par exemple, l'évaluation côté "
@@ -7721,8 +7721,8 @@ msgid ""
 "Most official `Flower code examples "
 "<https://github.com/adap/flower/tree/main/examples>`_ are already updated"
 " to Flower 1.0, they can serve as a reference for using the Flower 1.0 "
-"API. If there are further questionsm, `join the Flower Slack "
-"<https://flower.ai/join-slack/>`_ and use the channgel ``#questions``."
+"API. If there are further questions, `join the Flower Slack "
+"<https://flower.ai/join-slack/>`_ and use the channel ``#questions``."
 msgstr ""
 "La plupart des `exemples de code Flower officiels "
 "<https://github.com/adap/flower/tree/main/examples>`_ sont déjà mis à "
@@ -7931,7 +7931,7 @@ msgid ""
 "providing a function to :code:`on_fit_config_fn`. The provided function "
 "will be called by the strategy and must return a dictionary of "
 "configuration key values pairs that will be sent to the client. It must "
-"return a dictionary of arbitraty configuration values  :code:`client.fit`"
+"return a dictionary of arbitrary configuration values  :code:`client.fit`"
 " and :code:`client.evaluate` functions during each round of federated "
 "learning."
 msgstr ""
@@ -12668,7 +12668,7 @@ msgstr ""
 
 #: ../../source/ref-changelog.md:29
 msgid ""
-"Flower has official support for federated learning using [Appple "
+"Flower has official support for federated learning using [Apple "
 "MLX](https://ml-explore.github.io/mlx) via the new `quickstart-mlx` code "
 "example."
 msgstr ""
@@ -14060,7 +14060,7 @@ msgid ""
 "This new baseline replicates the MNIST+CNN task from the paper [Federated"
 " Optimization in Heterogeneous Networks (Li et al., "
 "2018)](https://arxiv.org/abs/1812.06127). It uses the `FedProx` strategy,"
-" which aims at making convergence more robust in heterogenous settings."
+" which aims at making convergence more robust in heterogeneous settings."
 msgstr ""
 "Cette nouvelle ligne de base reproduit la tâche MNIST+CNN de l'article "
 "[Federated Optimization in Heterogeneous Networks (Li et al., 2018)] "
@@ -16666,7 +16666,7 @@ msgstr ""
 msgid ""
 "The strategy named `QffedAvg` was renamed to `QFedAvg` to better reflect "
 "the notation given in the original paper (q-FFL is the optimization "
-"objective, q-FedAvg is the proposed solver). Note the the original (now "
+"objective, q-FedAvg is the proposed solver). Note the original (now "
 "deprecated) `QffedAvg` class is still available for compatibility reasons"
 " (it will be removed in a future release)."
 msgstr ""
@@ -16865,7 +16865,7 @@ msgstr ""
 msgid ""
 "Built-in strategies implement this approach by handling serialization and"
 " deserialization to/from `Weights` internally. Custom/3rd-party Strategy "
-"implementations should update to the slighly changed Strategy method "
+"implementations should update to the slightly changed Strategy method "
 "definitions. Strategy authors can consult PR "
 "[#721](https://github.com/adap/flower/pull/721) to see how strategies can"
 " easily migrate to the new format."
@@ -19093,7 +19093,7 @@ msgid ":code:`get_model_parameters()`"
 msgstr ":code:`get_model_parameters()`"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:46
-msgid "Returns the paramters of a :code:`sklearn` LogisticRegression model"
+msgid "Returns the parameters of a :code:`sklearn` LogisticRegression model"
 msgstr ""
 "Renvoie les paramètres d'un modèle de régression logistique "
 ":code:`sklearn`"
@@ -20567,7 +20567,7 @@ msgstr "Côté client"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:615
 msgid ""
-"To be able to able to serialize our ``ndarray``\\ s into sparse "
+"To be able to serialize our ``ndarray``\\ s into sparse "
 "parameters, we will just have to call our custom functions in our "
 "``flwr.client.Client``."
 msgstr ""
@@ -22090,7 +22090,7 @@ msgid ""
 "The popularity of privacy-enhancing systems like the `Brave "
 "<https://brave.com/>`__ browser or the `Signal <https://signal.org/>`__ "
 "messenger shows that users care about privacy. In fact, they choose the "
-"privacy-enhancing version over other alternatives, if such an alernative "
+"privacy-enhancing version over other alternatives, if such an alternative "
 "exists. But what can we do to apply machine learning and data science to "
 "these cases to utilize private data? After all, these are all areas that "
 "would benefit significantly from recent advances in AI."
@@ -23814,7 +23814,7 @@ msgstr ""
 #~ "that in Flower simulation, the server"
 #~ " automatically displays all logs. You "
 #~ "can still specify a :code:`HTTPHandler` "
-#~ "should you whish to backup or "
+#~ "should you wish to backup or "
 #~ "analyze the logs somewhere else."
 #~ msgstr ""
 #~ "La fonction :code:`fl.common.logger.configure` "
diff --git a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
index df354f7a76cc..047f833788e7 100644
--- a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
@@ -442,7 +442,7 @@ msgid "Once that is done, we will compile the file with:"
 msgstr ""
 
 #: ../../source/contributor-how-to-create-new-messages.rst:78
-msgid "If it compiles succesfully, you should see the following message:"
+msgid "If it compiles successfully, you should see the following message:"
 msgstr ""
 
 #: ../../source/contributor-how-to-create-new-messages.rst:87
@@ -823,7 +823,7 @@ msgstr ""
 
 #: ../../source/contributor-how-to-release-flower.rst:25
 msgid ""
-"Merge the pull request on the same day (i.e., before a new nighly release"
+"Merge the pull request on the same day (i.e., before a new nightly release"
 " gets published to PyPI)."
 msgstr ""
 
@@ -837,7 +837,7 @@ msgstr ""
 
 #: ../../source/contributor-how-to-release-flower.rst:33
 msgid ""
-"PyPI supports pre-releases (alpha, beta, release candiate). Pre-releases "
+"PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases "
 "MUST use one of the following naming patterns:"
 msgstr ""
 
@@ -850,7 +850,7 @@ msgid "Beta: ``MAJOR.MINOR.PATCHbN``"
 msgstr ""
 
 #: ../../source/contributor-how-to-release-flower.rst:37
-msgid "Release candiate (RC): ``MAJOR.MINOR.PATCHrcN``"
+msgid "Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``"
 msgstr ""
 
 #: ../../source/contributor-how-to-release-flower.rst:39
@@ -2093,7 +2093,7 @@ msgid ""
 "The Flower repository contains a number of convenience scripts to make "
 "recurring development tasks easier and less error-prone. See the "
 ":code:`/dev` subdirectory for a full list. The following scripts are "
-"amonst the most important ones:"
+"amongst the most important ones:"
 msgstr ""
 
 #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:77
@@ -2730,7 +2730,7 @@ msgid ""
 "Implementing a Flower *client* basically means implementing a subclass of"
 " either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. "
 "Our implementation will be based on :code:`flwr.client.NumPyClient` and "
-"we'll call it :code:`MNISTClient`. :code:`NumPyClient` is slighly easier "
+"we'll call it :code:`MNISTClient`. :code:`NumPyClient` is slightly easier "
 "to implement than :code:`Client` if you use a framework with good NumPy "
 "interoperability (like PyTorch or MXNet) because it avoids some of the "
 "boilerplate that would otherwise be necessary. :code:`MNISTClient` needs "
@@ -2892,7 +2892,7 @@ msgid ""
 "The concept is easy to understand. We have to start a *server* and then "
 "use the code in :code:`cifar.py` for the *clients* that are connected to "
 "the *server*. The *server* sends model parameters to the clients. The "
-"*clients* run the training and update the paramters. The updated "
+"*clients* run the training and update the parameters. The updated "
 "parameters are sent back to the *server* which averages all received "
 "parameter updates. This describes one round of the federated learning "
 "process and we repeat this for multiple rounds."
@@ -2903,7 +2903,7 @@ msgid ""
 "Finally, we will define our *client* logic in :code:`client.py` and build"
 " upon the previously defined centralized training in :code:`cifar.py`. "
 "Our *client* needs to import :code:`flwr`, but also :code:`torch` to "
-"update the paramters on our PyTorch model:"
+"update the parameters on our PyTorch model:"
 msgstr ""
 
 #: ../../source/example-pytorch-from-centralized-to-federated.rst:213
@@ -2911,7 +2911,7 @@ msgid ""
 "Implementing a Flower *client* basically means implementing a subclass of"
 " either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. "
 "Our implementation will be based on :code:`flwr.client.NumPyClient` and "
-"we'll call it :code:`CifarClient`. :code:`NumPyClient` is slighly easier "
+"we'll call it :code:`CifarClient`. :code:`NumPyClient` is slightly easier "
 "to implement than :code:`Client` if you use a framework with good NumPy "
 "interoperability (like PyTorch or TensorFlow/Keras) because it avoids "
 "some of the boilerplate that would otherwise be necessary. "
@@ -2941,7 +2941,7 @@ msgid ""
 "data, creates a :code:`CifarClient`, and starts this client. You load "
 "your data and model by using :code:`cifar.py`. Start :code:`CifarClient` "
 "with the function :code:`fl.client.start_client()` by pointing it at the "
-"same IP adress we used in :code:`server.py`:"
+"same IP address we used in :code:`server.py`:"
 msgstr ""
 
 #: ../../source/example-pytorch-from-centralized-to-federated.rst:307
@@ -4342,7 +4342,7 @@ msgid ""
 "otherwise gathering logs from all entities (i.e. the server and the "
 "clients) might be cumbersome. Note that in Flower simulation, the server "
 "automatically displays all logs. You can still specify a "
-":code:`HTTPHandler` should you whish to backup or analyze the logs "
+":code:`HTTPHandler` should you wish to backup or analyze the logs "
 "somewhere else."
 msgstr ""
 
@@ -5954,7 +5954,7 @@ msgstr ""
 msgid ""
 "Remove \"placeholder\" methods from subclasses of ``Client`` or "
 "``NumPyClient``. If you, for example, use server-side evaluation, then "
-"empy placeholder implementations of ``evaluate`` are no longer necessary."
+"empty placeholder implementations of ``evaluate`` are no longer necessary."
 msgstr ""
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:85
@@ -5973,8 +5973,8 @@ msgid ""
 "Most official `Flower code examples "
 "<https://github.com/adap/flower/tree/main/examples>`_ are already updated"
 " to Flower 1.0, they can serve as a reference for using the Flower 1.0 "
-"API. If there are further questionsm, `join the Flower Slack "
-"<https://flower.ai/join-slack/>`_ and use the channgel ``#questions``."
+"API. If there are further questions, `join the Flower Slack "
+"<https://flower.ai/join-slack/>`_ and use the channel ``#questions``."
 msgstr ""
 
 #: ../../source/how-to-use-built-in-mods.rst:2
@@ -6161,7 +6161,7 @@ msgid ""
 "providing a function to :code:`on_fit_config_fn`. The provided function "
 "will be called by the strategy and must return a dictionary of "
 "configuration key values pairs that will be sent to the client. It must "
-"return a dictionary of arbitraty configuration values  :code:`client.fit`"
+"return a dictionary of arbitrary configuration values  :code:`client.fit`"
 " and :code:`client.evaluate` functions during each round of federated "
 "learning."
 msgstr ""
@@ -10709,7 +10709,7 @@ msgstr ""
 
 #: ../../source/ref-changelog.md:29
 msgid ""
-"Flower has official support for federated learning using [Appple "
+"Flower has official support for federated learning using [Apple "
 "MLX](https://ml-explore.github.io/mlx) via the new `quickstart-mlx` code "
 "example."
 msgstr ""
@@ -11763,7 +11763,7 @@ msgid ""
 "This new baseline replicates the MNIST+CNN task from the paper [Federated"
 " Optimization in Heterogeneous Networks (Li et al., "
 "2018)](https://arxiv.org/abs/1812.06127). It uses the `FedProx` strategy,"
-" which aims at making convergence more robust in heterogenous settings."
+" which aims at making convergence more robust in heterogeneous settings."
 msgstr ""
 
 #: ../../source/ref-changelog.md:312
@@ -13553,7 +13553,7 @@ msgstr ""
 msgid ""
 "The strategy named `QffedAvg` was renamed to `QFedAvg` to better reflect "
 "the notation given in the original paper (q-FFL is the optimization "
-"objective, q-FedAvg is the proposed solver). Note the the original (now "
+"objective, q-FedAvg is the proposed solver). Note the original (now "
 "deprecated) `QffedAvg` class is still available for compatibility reasons"
 " (it will be removed in a future release)."
 msgstr ""
@@ -13683,7 +13683,7 @@ msgstr ""
 msgid ""
 "Built-in strategies implement this approach by handling serialization and"
 " deserialization to/from `Weights` internally. Custom/3rd-party Strategy "
-"implementations should update to the slighly changed Strategy method "
+"implementations should update to the slightly changed Strategy method "
 "definitions. Strategy authors can consult PR "
 "[#721](https://github.com/adap/flower/pull/721) to see how strategies can"
 " easily migrate to the new format."
@@ -15338,7 +15338,7 @@ msgid ":code:`get_model_parameters()`"
 msgstr ""
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:46
-msgid "Returns the paramters of a :code:`sklearn` LogisticRegression model"
+msgid "Returns the parameters of a :code:`sklearn` LogisticRegression model"
 msgstr ""
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:47
@@ -16488,7 +16488,7 @@ msgstr ""
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:615
 msgid ""
-"To be able to able to serialize our ``ndarray``\\ s into sparse "
+"To be able to serialize our ``ndarray``\\ s into sparse "
 "parameters, we will just have to call our custom functions in our "
 "``flwr.client.Client``."
 msgstr ""
@@ -17504,7 +17504,7 @@ msgid ""
 "The popularity of privacy-enhancing systems like the `Brave "
 "<https://brave.com/>`__ browser or the `Signal <https://signal.org/>`__ "
 "messenger shows that users care about privacy. In fact, they choose the "
-"privacy-enhancing version over other alternatives, if such an alernative "
+"privacy-enhancing version over other alternatives, if such an alternative "
 "exists. But what can we do to apply machine learning and data science to "
 "these cases to utilize private data? After all, these are all areas that "
 "would benefit significantly from recent advances in AI."
diff --git a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
index a4255e51f4b6..37158d25800f 100644
--- a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
@@ -479,7 +479,7 @@ msgid "Once that is done, we will compile the file with:"
 msgstr "完成后，我们将使用："
 
 #: ../../source/contributor-how-to-create-new-messages.rst:78
-msgid "If it compiles succesfully, you should see the following message:"
+msgid "If it compiles successfully, you should see the following message:"
 msgstr "如果编译成功，你应该会看到以下信息："
 
 #: ../../source/contributor-how-to-create-new-messages.rst:87
@@ -909,7 +909,7 @@ msgstr "在 ``changelog.md`` 中添加新的 ``Unreleased`` 部分。"
 
 #: ../../source/contributor-how-to-release-flower.rst:25
 msgid ""
-"Merge the pull request on the same day (i.e., before a new nighly release"
+"Merge the pull request on the same day (i.e., before a new nightly release"
 " gets published to PyPI)."
 msgstr "在同一天合并拉取请求（即在新版本发布到 PyPI 之前）。"
 
@@ -923,9 +923,9 @@ msgstr "释放前命名"
 
 #: ../../source/contributor-how-to-release-flower.rst:33
 msgid ""
-"PyPI supports pre-releases (alpha, beta, release candiate). Pre-releases "
+"PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases "
 "MUST use one of the following naming patterns:"
-msgstr "PyPI 支持预发布版本（alpha、beta、release candiate）。预发布版本必须使用以下命名模式之一："
+msgstr "PyPI 支持预发布版本（alpha、beta、release candidate）。预发布版本必须使用以下命名模式之一："
 
 #: ../../source/contributor-how-to-release-flower.rst:35
 msgid "Alpha: ``MAJOR.MINOR.PATCHaN``"
@@ -936,7 +936,7 @@ msgid "Beta: ``MAJOR.MINOR.PATCHbN``"
 msgstr "贝塔： ``MAJOR.MINOR.PATCHbN``"
 
 #: ../../source/contributor-how-to-release-flower.rst:37
-msgid "Release candiate (RC): ``MAJOR.MINOR.PATCHrcN``"
+msgid "Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``"
 msgstr "版本代号 (RC)： ``MAJOR.MINOR.PATCHrcN``"
 
 #: ../../source/contributor-how-to-release-flower.rst:39
@@ -2270,7 +2270,7 @@ msgid ""
 "The Flower repository contains a number of convenience scripts to make "
 "recurring development tasks easier and less error-prone. See the "
 ":code:`/dev` subdirectory for a full list. The following scripts are "
-"amonst the most important ones:"
+"amongst the most important ones:"
 msgstr "Flower 软件仓库包含大量便捷脚本，可使重复性开发任务更轻松、更不易出错。完整列表请参见 :code:`/dev` 子目录。以下是最重要的脚本："
 
 #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:77
@@ -3004,7 +3004,7 @@ msgid ""
 "Implementing a Flower *client* basically means implementing a subclass of"
 " either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. "
 "Our implementation will be based on :code:`flwr.client.NumPyClient` and "
-"we'll call it :code:`MNISTClient`. :code:`NumPyClient` is slighly easier "
+"we'll call it :code:`MNISTClient`. :code:`NumPyClient` is slightly easier "
 "to implement than :code:`Client` if you use a framework with good NumPy "
 "interoperability (like PyTorch or MXNet) because it avoids some of the "
 "boilerplate that would otherwise be necessary. :code:`MNISTClient` needs "
@@ -3199,7 +3199,7 @@ msgid ""
 "The concept is easy to understand. We have to start a *server* and then "
 "use the code in :code:`cifar.py` for the *clients* that are connected to "
 "the *server*. The *server* sends model parameters to the clients. The "
-"*clients* run the training and update the paramters. The updated "
+"*clients* run the training and update the parameters. The updated "
 "parameters are sent back to the *server* which averages all received "
 "parameter updates. This describes one round of the federated learning "
 "process and we repeat this for multiple rounds."
@@ -3212,7 +3212,7 @@ msgid ""
 "Finally, we will define our *client* logic in :code:`client.py` and build"
 " upon the previously defined centralized training in :code:`cifar.py`. "
 "Our *client* needs to import :code:`flwr`, but also :code:`torch` to "
-"update the paramters on our PyTorch model:"
+"update the parameters on our PyTorch model:"
 msgstr ""
 "最后，我们将在 :code:`client.py` 中定义我们的 *client* 逻辑，并以之前在 :code:`cifar.py` "
 "中定义的集中式训练为基础。我们的 *client* 不仅需要导入 :code:`flwr`，还需要导入 :code:`torch`，以更新 "
@@ -3223,7 +3223,7 @@ msgid ""
 "Implementing a Flower *client* basically means implementing a subclass of"
 " either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. "
 "Our implementation will be based on :code:`flwr.client.NumPyClient` and "
-"we'll call it :code:`CifarClient`. :code:`NumPyClient` is slighly easier "
+"we'll call it :code:`CifarClient`. :code:`NumPyClient` is slightly easier "
 "to implement than :code:`Client` if you use a framework with good NumPy "
 "interoperability (like PyTorch or TensorFlow/Keras) because it avoids "
 "some of the boilerplate that would otherwise be necessary. "
@@ -3263,7 +3263,7 @@ msgid ""
 "data, creates a :code:`CifarClient`, and starts this client. You load "
 "your data and model by using :code:`cifar.py`. Start :code:`CifarClient` "
 "with the function :code:`fl.client.start_client()` by pointing it at the "
-"same IP adress we used in :code:`server.py`:"
+"same IP address we used in :code:`server.py`:"
 msgstr "剩下的就是定义模型和数据加载函数了。创建一个:code:`CifarClient`类，并运行这个客服端。您将通过:code:`cifar.py`加载数据和模型。另外，通过:code:`fl.client.start_client()`函数来运行客户端:code:`CifarClient`，需要保证IP地址和:code:`server.py`中所使用的一致："
 
 #: ../../source/example-pytorch-from-centralized-to-federated.rst:307
@@ -4816,7 +4816,7 @@ msgid ""
 "otherwise gathering logs from all entities (i.e. the server and the "
 "clients) might be cumbersome. Note that in Flower simulation, the server "
 "automatically displays all logs. You can still specify a "
-":code:`HTTPHandler` should you whish to backup or analyze the logs "
+":code:`HTTPHandler` should you wish to backup or analyze the logs "
 "somewhere else."
 msgstr ""
 "此外，:code:`fl.common.logger.configure`函数还允许指定主机，通过本地 Python "
@@ -6650,7 +6650,7 @@ msgstr "除了上述必要的改动之外，还有一些潜在的改进措施：
 msgid ""
 "Remove \"placeholder\" methods from subclasses of ``Client`` or "
 "``NumPyClient``. If you, for example, use server-side evaluation, then "
-"empy placeholder implementations of ``evaluate`` are no longer necessary."
+"empty placeholder implementations of ``evaluate`` are no longer necessary."
 msgstr ""
 "删除 ``Client`` 或 ``NumPyClient`` 子类中的 \"占位符 "
 "\"方法。例如，如果你使用服务器端评估，那么就不再需要``evaluate``的 \"空占位符 \"实现。"
@@ -6674,8 +6674,8 @@ msgid ""
 "Most official `Flower code examples "
 "<https://github.com/adap/flower/tree/main/examples>`_ are already updated"
 " to Flower 1.0, they can serve as a reference for using the Flower 1.0 "
-"API. If there are further questionsm, `join the Flower Slack "
-"<https://flower.ai/join-slack/>`_ and use the channgel ``#questions``."
+"API. If there are further questions, `join the Flower Slack "
+"<https://flower.ai/join-slack/>`_ and use the channel ``#questions``."
 msgstr ""
 "大多数官方的 `Flower 代码示例 <https://github.com/adap/flower/tree/main/examples>`_"
 " 已经更新到 Flower 1.0，它们可以作为使用 Flower 1.0 API 的参考。如果还有其他问题，请加入 Flower Slack "
@@ -6866,7 +6866,7 @@ msgid ""
 "providing a function to :code:`on_fit_config_fn`. The provided function "
 "will be called by the strategy and must return a dictionary of "
 "configuration key values pairs that will be sent to the client. It must "
-"return a dictionary of arbitraty configuration values  :code:`client.fit`"
+"return a dictionary of arbitrary configuration values  :code:`client.fit`"
 " and :code:`client.evaluate` functions during each round of federated "
 "learning."
 msgstr ""
@@ -11608,7 +11608,7 @@ msgstr ""
 
 #: ../../source/ref-changelog.md:29
 msgid ""
-"Flower has official support for federated learning using [Appple "
+"Flower has official support for federated learning using [Apple "
 "MLX](https://ml-explore.github.io/mlx) via the new `quickstart-mlx` code "
 "example."
 msgstr ""
@@ -12977,7 +12977,7 @@ msgid ""
 "This new baseline replicates the MNIST+CNN task from the paper [Federated"
 " Optimization in Heterogeneous Networks (Li et al., "
 "2018)](https://arxiv.org/abs/1812.06127). It uses the `FedProx` strategy,"
-" which aims at making convergence more robust in heterogenous settings."
+" which aims at making convergence more robust in heterogeneous settings."
 msgstr ""
 "这条新Baseline复现了论文[Federated Optimization in Heterogeneous Networks (Li et "
 "al., 2018)](https://arxiv.org/abs/1812.06127)中的 MNIST+CNN 任务。它使用 "
@@ -15288,7 +15288,7 @@ msgstr "**更名为 q-FedAvg 策略** ([#802](https://github.com/adap/flower/pul
 msgid ""
 "The strategy named `QffedAvg` was renamed to `QFedAvg` to better reflect "
 "the notation given in the original paper (q-FFL is the optimization "
-"objective, q-FedAvg is the proposed solver). Note the the original (now "
+"objective, q-FedAvg is the proposed solver). Note the original (now "
 "deprecated) `QffedAvg` class is still available for compatibility reasons"
 " (it will be removed in a future release)."
 msgstr ""
@@ -15444,7 +15444,7 @@ msgstr ""
 msgid ""
 "Built-in strategies implement this approach by handling serialization and"
 " deserialization to/from `Weights` internally. Custom/3rd-party Strategy "
-"implementations should update to the slighly changed Strategy method "
+"implementations should update to the slightly changed Strategy method "
 "definitions. Strategy authors can consult PR "
 "[#721](https://github.com/adap/flower/pull/721) to see how strategies can"
 " easily migrate to the new format."
@@ -17339,7 +17339,7 @@ msgid ":code:`get_model_parameters()`"
 msgstr ":code:`get_model_parameters()`"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:46
-msgid "Returns the paramters of a :code:`sklearn` LogisticRegression model"
+msgid "Returns the parameters of a :code:`sklearn` LogisticRegression model"
 msgstr "返回 :code:`sklearn` LogisticRegression 模型的参数"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:47
@@ -18678,7 +18678,7 @@ msgstr "客户端"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:615
 msgid ""
-"To be able to able to serialize our ``ndarray``\\ s into sparse "
+"To be able to serialize our ``ndarray``\\ s into sparse "
 "parameters, we will just have to call our custom functions in our "
 "``flwr.client.Client``."
 msgstr "为了能够将我们的 ``ndarray`` 序列化为稀疏参数，我们只需在 ``flwr.client.Client`` 中调用我们的自定义函数。"
@@ -19835,7 +19835,7 @@ msgid ""
 "The popularity of privacy-enhancing systems like the `Brave "
 "<https://brave.com/>`__ browser or the `Signal <https://signal.org/>`__ "
 "messenger shows that users care about privacy. In fact, they choose the "
-"privacy-enhancing version over other alternatives, if such an alernative "
+"privacy-enhancing version over other alternatives, if such an alternative "
 "exists. But what can we do to apply machine learning and data science to "
 "these cases to utilize private data? After all, these are all areas that "
 "would benefit significantly from recent advances in AI."
@@ -20461,7 +20461,7 @@ msgstr ""
 #~ "by using :code:`cifar.py`. Start "
 #~ ":code:`CifarClient` with the function "
 #~ ":code:`fl.client.start_numpy_client()` by pointing "
-#~ "it at the same IP adress we "
+#~ "it at the same IP address we "
 #~ "used in :code:`server.py`:"
 #~ msgstr ""
 #~ "剩下要做的就是定义一个加载模型和数据的函数，创建一个 :code:`CifarClient` 并启动该客户端。使用"

From fa3d1ecefff48046b5e41087ff43f54dc587c195 Mon Sep 17 00:00:00 2001
From: Danny <volleyballlive@googlemail.com>
Date: Fri, 1 Mar 2024 15:12:14 +0100
Subject: [PATCH 086/102] Fix typos in examples/* (#3041)

---
 .../src/main/java/flwr/android_client/MainActivity.java   | 2 +-
 examples/ios/FLiOS/CoreMLProto/NeuralNetwork.pb.swift     | 6 +++---
 examples/ios/FLiOS/CoreMLProto/NeuralNetwork.proto        | 8 ++++----
 examples/simulation-pytorch/sim.ipynb                     | 2 +-
 4 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/examples/android/client/app/src/main/java/flwr/android_client/MainActivity.java b/examples/android/client/app/src/main/java/flwr/android_client/MainActivity.java
index cbf804140954..4de369a7e684 100644
--- a/examples/android/client/app/src/main/java/flwr/android_client/MainActivity.java
+++ b/examples/android/client/app/src/main/java/flwr/android_client/MainActivity.java
@@ -60,7 +60,7 @@ public void onChanged(List<WorkInfo> workInfos) {
                 if (workInfos.size() > 0) {
                     WorkInfo info = workInfos.get(0);
                     int progress = info.getProgress().getInt("progress", -1);
-                    // You can recieve any message from the Worker Thread
+                    // You can receive any message from the Worker Thread
                     refreshRecyclerView();
                 }
             }
diff --git a/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.pb.swift b/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.pb.swift
index 6075e689cb1e..3348ceae9e0e 100644
--- a/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.pb.swift
+++ b/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.pb.swift
@@ -4308,7 +4308,7 @@ struct CoreML_Specification_ConvolutionLayerParams {
 ///   The output blob's shape is ``[batch, channelsOut, depthOut, heightOut, widthOut]``.
 ///
 /// Type of padding can be ``custom``, ``valid``, or ``same``. Padded values are all zeros.
-/// Output spatial dimensions depend on the the type of padding. For details, refer to the
+/// Output spatial dimensions depend on the type of padding. For details, refer to the
 /// descriptions of the ``PaddingType`` field of this ``Convolution3DLayerParams`` message.
 ///
 /// Example
@@ -5219,7 +5219,7 @@ struct CoreML_Specification_Pooling3DLayerParams {
   /// SAME padding adds enough padding to each dimension such that the output
   /// has the same spatial dimensions as the input. Padding is added evenly to both
   /// sides of each dimension unless the total padding to add is odd, in which case the extra padding
-  /// is added to the back/bottom/right side of the respective dimension.  For example, if the the
+  /// is added to the back/bottom/right side of the respective dimension.  For example, if the
   /// total horizontal padding is 3, then there will be 1 padding on the left, and 2 padding on the right.
   enum Pooling3DPaddingType: SwiftProtobuf.Enum {
     typealias RawValue = Int
@@ -9493,7 +9493,7 @@ struct CoreML_Specification_ConstantPaddingLayerParams {
 
   ///*
   /// Length of this repeated field must be twice the rank of the first input.
-  /// 2*i-th and (2*i+1)-th values represent the amount of padding to be applied to the the i-th input
+  /// 2*i-th and (2*i+1)-th values represent the amount of padding to be applied to the i-th input
   /// dimension, "before" and "after" the input values, respectively.
   var padAmounts: [UInt64] = []
 
diff --git a/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.proto b/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.proto
index 6b2ebb1c8ba1..e80a3566c2c3 100644
--- a/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.proto
+++ b/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.proto
@@ -1554,7 +1554,7 @@ message ConvolutionLayerParams {
  *   The output blob's shape is ``[batch, channelsOut, depthOut, heightOut, widthOut]``.
  *
  * Type of padding can be ``custom``, ``valid``, or ``same``. Padded values are all zeros.
- * Output spatial dimensions depend on the the type of padding. For details, refer to the
+ * Output spatial dimensions depend on the type of padding. For details, refer to the
  * descriptions of the ``PaddingType`` field of this ``Convolution3DLayerParams`` message.
  *
  * Example
@@ -2056,7 +2056,7 @@ message Pooling3DLayerParams {
      * SAME padding adds enough padding to each dimension such that the output
      * has the same spatial dimensions as the input. Padding is added evenly to both
      * sides of each dimension unless the total padding to add is odd, in which case the extra padding
-     * is added to the back/bottom/right side of the respective dimension.  For example, if the the
+     * is added to the back/bottom/right side of the respective dimension.  For example, if the
      * total horizontal padding is 3, then there will be 1 padding on the left, and 2 padding on the right.
      */
     enum Pooling3DPaddingType {
@@ -4779,7 +4779,7 @@ enum ScatterMode {
  *
  * The output has the same shape as the first input.
  * "indices" input must have rank less than or equal to the "updates" input and its shape
- * must be a subset of the the shape of the "updates" input.
+ * must be a subset of the shape of the "updates" input.
  *
  * e.g:
  *
@@ -5064,7 +5064,7 @@ message ConstantPaddingLayerParams {
 
     /**
      * Length of this repeated field must be twice the rank of the first input.
-     * 2*i-th and (2*i+1)-th values represent the amount of padding to be applied to the the i-th input
+     * 2*i-th and (2*i+1)-th values represent the amount of padding to be applied to the i-th input
      * dimension, "before" and "after" the input values, respectively.
      */
     repeated uint64 padAmounts = 2;
diff --git a/examples/simulation-pytorch/sim.ipynb b/examples/simulation-pytorch/sim.ipynb
index 93a79d2f0e0a..e27721a7fa5f 100644
--- a/examples/simulation-pytorch/sim.ipynb
+++ b/examples/simulation-pytorch/sim.ipynb
@@ -290,7 +290,7 @@
     "        self.model.to(self.device)  # send model to device\n",
     "\n",
     "    def set_parameters(self, parameters):\n",
-    "        \"\"\"With the model paramters received from the server,\n",
+    "        \"\"\"With the model parameters received from the server,\n",
     "        overwrite the uninitialise model in this class with them.\"\"\"\n",
     "\n",
     "        params_dict = zip(self.model.state_dict().keys(), parameters)\n",

From 4b1ed7d3dba8d01c423c21e94a79ff8ec80be183 Mon Sep 17 00:00:00 2001
From: Danny <volleyballlive@googlemail.com>
Date: Fri, 1 Mar 2024 16:33:34 +0100
Subject: [PATCH 087/102] Fix typos in src/py* src/flwr/* e2e/* dev (#3042)

Co-authored-by: Taner Topal <taner@flower.dev>
---
 dev/publish-nightly.sh                                        | 2 +-
 e2e/scikit-learn/utils.py                                     | 2 +-
 src/py/flwr/common/record/recordset_test.py                   | 4 ++--
 src/py/flwr/server/strategy/dp_adaptive_clipping.py           | 4 ++--
 .../tensorflow_fashion_mnist/fashion_mnist_test.py            | 2 +-
 src/py/flwr_experimental/baseline/config/config.py            | 2 +-
 src/py/flwr_experimental/ops/cluster.py                       | 2 +-
 7 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/dev/publish-nightly.sh b/dev/publish-nightly.sh
index 2d2e82325fb9..f3e8d170f6c5 100755
--- a/dev/publish-nightly.sh
+++ b/dev/publish-nightly.sh
@@ -20,7 +20,7 @@ cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
 
 # This script will build and publish a nightly release of Flower under the condition
 # that at least one commit was made in the last 24 hours.
-# It will rename the the package name in the pyproject.toml to from "flwr" to "flwr-nightly".
+# It will rename the package name in the pyproject.toml to from "flwr" to "flwr-nightly".
 # The version name in the pyproject.toml will be appended with "-dev" and the current date.
 # The result will be a release on PyPi of the package "flwr-nightly" of version e.g. 
 # "0.1.1.dev20200716" as seen at https://pypi.org/project/flwr-nightly/
diff --git a/e2e/scikit-learn/utils.py b/e2e/scikit-learn/utils.py
index d6804fdcce12..2b8dcf8655ee 100644
--- a/e2e/scikit-learn/utils.py
+++ b/e2e/scikit-learn/utils.py
@@ -10,7 +10,7 @@
 
 
 def get_model_parameters(model: LogisticRegression) -> LogRegParams:
-    """Returns the paramters of a sklearn LogisticRegression model."""
+    """Returns the parameters of a sklearn LogisticRegression model."""
     if model.fit_intercept:
         params = [
             model.coef_,
diff --git a/src/py/flwr/common/record/recordset_test.py b/src/py/flwr/common/record/recordset_test.py
index 66560d8337f9..bcf5c75a1e02 100644
--- a/src/py/flwr/common/record/recordset_test.py
+++ b/src/py/flwr/common/record/recordset_test.py
@@ -70,7 +70,7 @@ def test_parameters_to_array_and_back() -> None:
     """Test conversion between legacy Parameters and Array."""
     ndarrays = get_ndarrays()
 
-    # Array represents a single array, unlike Paramters, which represent a
+    # Array represents a single array, unlike parameters, which represent a
     # list of arrays
     ndarray = ndarrays[0]
 
@@ -296,7 +296,7 @@ def test_set_metrics_to_metricsrecord_with_and_without_keeping_input(
         (str, lambda x: x.flatten().astype("float").tolist()),  # str: List[float]
         (str, lambda x: x.flatten().astype("bool").tolist()),  # str: List[bool]
         (str, lambda x: [x.flatten().tobytes()]),  # str: List[bytes]
-        (str, lambda x: []),  # str: empyt list
+        (str, lambda x: []),  # str: emptyt list
     ],
 )
 def test_set_configs_to_configsrecord_with_correct_types(
diff --git a/src/py/flwr/server/strategy/dp_adaptive_clipping.py b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
index deff3e11054b..d9422c791167 100644
--- a/src/py/flwr/server/strategy/dp_adaptive_clipping.py
+++ b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
@@ -63,7 +63,7 @@ class DifferentialPrivacyServerSideAdaptiveClipping(Strategy):
     num_sampled_clients : int
         The number of clients that are sampled on each round.
     initial_clipping_norm : float
-        The initial value of clipping norm. Deafults to 0.1.
+        The initial value of clipping norm. Defaults to 0.1.
         Andrew et al. recommends to set to 0.1.
     target_clipped_quantile : float
         The desired quantile of updates which should be clipped. Defaults to 0.5.
@@ -263,7 +263,7 @@ class DifferentialPrivacyClientSideAdaptiveClipping(Strategy):
     num_sampled_clients : int
         The number of clients that are sampled on each round.
     initial_clipping_norm : float
-        The initial value of clipping norm. Deafults to 0.1.
+        The initial value of clipping norm. Defaults to 0.1.
         Andrew et al. recommends to set to 0.1.
     target_clipped_quantile : float
         The desired quantile of updates which should be clipped. Defaults to 0.5.
diff --git a/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist_test.py b/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist_test.py
index 1213410fbc34..f6b922b27eab 100644
--- a/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist_test.py
+++ b/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist_test.py
@@ -21,7 +21,7 @@
 
 
 def test_shuffle() -> None:
-    """Test if shuffle is deterministic depending on the the provided seed."""
+    """Test if shuffle is deterministic depending on the provided seed."""
     # Prepare
     x_tt = np.arange(8)
     y_tt = np.arange(8)
diff --git a/src/py/flwr_experimental/baseline/config/config.py b/src/py/flwr_experimental/baseline/config/config.py
index 5170ea7f7e26..16c144bb6a2f 100644
--- a/src/py/flwr_experimental/baseline/config/config.py
+++ b/src/py/flwr_experimental/baseline/config/config.py
@@ -23,7 +23,7 @@
 from flwr_experimental.ops.instance import Instance
 
 # We assume that devices which are older will have at most
-# ~80% of the the Samsung Galaxy Note 5 compute performance.
+# ~80% of the Samsung Galaxy Note 5 compute performance.
 SCORE_MISSING = int(226 * 0.80)
 
 DEVICE_DISTRIBUTION = [
diff --git a/src/py/flwr_experimental/ops/cluster.py b/src/py/flwr_experimental/ops/cluster.py
index 692704676f4a..53a4e9617427 100644
--- a/src/py/flwr_experimental/ops/cluster.py
+++ b/src/py/flwr_experimental/ops/cluster.py
@@ -139,7 +139,7 @@ def group_instances_by_specs(instances: List[Instance]) -> List[List[Instance]]:
 
 
 class Cluster:
-    """Compute enviroment independend compute cluster."""
+    """Compute environment independend compute cluster."""
 
     def __init__(
         self,

From aa54e469ca4e5e3f7ea47fa61eb5435e967c4a99 Mon Sep 17 00:00:00 2001
From: Danny <volleyballlive@googlemail.com>
Date: Fri, 1 Mar 2024 17:04:19 +0100
Subject: [PATCH 088/102] Fix broken links (#3046)

---
 doc/locales/fr/LC_MESSAGES/framework-docs.po  | 26 ++++++++---------
 .../pt_BR/LC_MESSAGES/framework-docs.po       | 14 +++++-----
 .../zh_Hans/LC_MESSAGES/framework-docs.po     | 28 +++++++++----------
 doc/source/ref-changelog.md                   | 10 +++----
 4 files changed, 39 insertions(+), 39 deletions(-)

diff --git a/doc/locales/fr/LC_MESSAGES/framework-docs.po b/doc/locales/fr/LC_MESSAGES/framework-docs.po
index fbceb3bc4c07..e7c7783c48ff 100644
--- a/doc/locales/fr/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/fr/LC_MESSAGES/framework-docs.po
@@ -13976,13 +13976,13 @@ msgid ""
 " "
 "[strategy](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py),"
 " and a [code "
-"example](https://github.com/adap/flower/tree/main/examples/quickstart_xgboost_horizontal)"
+"example](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart)"
 " that demonstrates the usage of this new strategy in an XGBoost project."
 msgstr ""
 "Nous avons ajouté une nouvelle [stratégie] `FedXgbNnAvg` "
 "(https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py),"
 " et un [exemple de code] "
-"(https://github.com/adap/flower/tree/main/examples/quickstart_xgboost_horizontal)"
+"(https://github.com/adap/flower/tree/main/examples/xgboost-quickstart)"
 " qui démontre l'utilisation de cette nouvelle stratégie dans un projet "
 "XGBoost."
 
@@ -14199,12 +14199,12 @@ msgstr ""
 msgid ""
 "TabNet is a powerful and flexible framework for training machine learning"
 " models on tabular data. We now have a federated example using Flower: "
-"[https://github.com/adap/flower/tree/main/examples/tabnet](https://github.com/adap/flower/tree/main/examples/quickstart_tabnet)."
+"[quickstart-tabnet](https://github.com/adap/flower/tree/main/examples/quickstart-tabnet)."
 msgstr ""
 "TabNet est un cadre puissant et flexible pour former des modèles "
 "d'apprentissage automatique sur des données tabulaires. Nous avons "
 "maintenant un exemple fédéré utilisant Flower : "
-"[https://github.com/adap/flower/tree/main/examples/tabnet](https://github.com/adap/flower/tree/main/examples/quickstart_tabnet)."
+"[quickstart-tabnet](https://github.com/adap/flower/tree/main/examples/quickstart-tabnet)."
 
 #: ../../source/ref-changelog.md:334
 msgid ""
@@ -14394,14 +14394,14 @@ msgstr ""
 
 #: ../../source/ref-changelog.md:374
 msgid ""
-"A new code example (`quickstart_fastai`) demonstrates federated learning "
+"A new code example (`quickstart-fastai`) demonstrates federated learning "
 "with [fastai](https://www.fast.ai/) and Flower. You can find it here: "
-"[quickstart_fastai](https://github.com/adap/flower/tree/main/examples/quickstart_fastai)."
+"[quickstart-fastai](https://github.com/adap/flower/tree/main/examples/quickstart-fastai)."
 msgstr ""
-"Un nouvel exemple de code (`quickstart_fastai`) démontre l'apprentissage "
+"Un nouvel exemple de code (`quickstart-fastai`) démontre l'apprentissage "
 "fédéré avec [fastai](https://www.fast.ai/) et Flower. Tu peux le trouver "
 "ici : "
-"[quickstart_fastai](https://github.com/adap/flower/tree/main/examples/quickstart_fastai)."
+"[quickstart-fastai](https://github.com/adap/flower/tree/main/examples/quickstart-fastai)."
 
 #: ../../source/ref-changelog.md:376
 msgid ""
@@ -14844,13 +14844,13 @@ msgstr ""
 
 #: ../../source/ref-changelog.md:453
 msgid ""
-"A new code example (`quickstart_pandas`) demonstrates federated analytics"
+"A new code example (`quickstart-pandas`) demonstrates federated analytics"
 " with Pandas and Flower. You can find it here: "
-"[quickstart_pandas](https://github.com/adap/flower/tree/main/examples/quickstart_pandas)."
+"[quickstart-pandas](https://github.com/adap/flower/tree/main/examples/quickstart-pandas)."
 msgstr ""
-"Un nouvel exemple de code (`quickstart_pandas`) démontre l'analyse "
+"Un nouvel exemple de code (`quickstart-pandas`) démontre l'analyse "
 "fédérée avec Pandas et Flower. Tu peux le trouver ici : "
-"[quickstart_pandas](https://github.com/adap/flower/tree/main/examples/quickstart_pandas)."
+"[quickstart-pandas](https://github.com/adap/flower/tree/main/examples/quickstart-pandas)."
 
 #: ../../source/ref-changelog.md:455
 msgid ""
@@ -15315,7 +15315,7 @@ msgid ""
 "[@mrinaald](https://github.com/mrinaald), "
 "[@zliel](https://github.com/zliel), "
 "[@MeiruiJiang](https://github.com/MeiruiJiang), "
-"[@sandracl72](https://github.com/sandracl72), "
+"[@sancarlim](https://github.com/sancarlim), "
 "[@gubertoli](https://github.com/gubertoli), "
 "[@Vingt100](https://github.com/Vingt100), "
 "[@MakGulati](https://github.com/MakGulati), "
diff --git a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
index 047f833788e7..ab1c43135c65 100644
--- a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
@@ -11711,7 +11711,7 @@ msgid ""
 " "
 "[strategy](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py),"
 " and a [code "
-"example](https://github.com/adap/flower/tree/main/examples/quickstart_xgboost_horizontal)"
+"example](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart)"
 " that demonstrates the usage of this new strategy in an XGBoost project."
 msgstr ""
 
@@ -11852,7 +11852,7 @@ msgstr ""
 msgid ""
 "TabNet is a powerful and flexible framework for training machine learning"
 " models on tabular data. We now have a federated example using Flower: "
-"[https://github.com/adap/flower/tree/main/examples/tabnet](https://github.com/adap/flower/tree/main/examples/quickstart_tabnet)."
+"[quickstart-tabnet](https://github.com/adap/flower/tree/main/examples/quickstart-tabnet)."
 msgstr ""
 
 #: ../../source/ref-changelog.md:334
@@ -11990,9 +11990,9 @@ msgstr ""
 
 #: ../../source/ref-changelog.md:374
 msgid ""
-"A new code example (`quickstart_fastai`) demonstrates federated learning "
+"A new code example (`quickstart-fastai`) demonstrates federated learning "
 "with [fastai](https://www.fast.ai/) and Flower. You can find it here: "
-"[quickstart_fastai](https://github.com/adap/flower/tree/main/examples/quickstart_fastai)."
+"[quickstart-fastai](https://github.com/adap/flower/tree/main/examples/quickstart-fastai)."
 msgstr ""
 
 #: ../../source/ref-changelog.md:376
@@ -12281,9 +12281,9 @@ msgstr ""
 
 #: ../../source/ref-changelog.md:453
 msgid ""
-"A new code example (`quickstart_pandas`) demonstrates federated analytics"
+"A new code example (`quickstart-pandas`) demonstrates federated analytics"
 " with Pandas and Flower. You can find it here: "
-"[quickstart_pandas](https://github.com/adap/flower/tree/main/examples/quickstart_pandas)."
+"[quickstart-pandas](https://github.com/adap/flower/tree/main/examples/quickstart-pandas)."
 msgstr ""
 
 #: ../../source/ref-changelog.md:455
@@ -12608,7 +12608,7 @@ msgid ""
 "[@mrinaald](https://github.com/mrinaald), "
 "[@zliel](https://github.com/zliel), "
 "[@MeiruiJiang](https://github.com/MeiruiJiang), "
-"[@sandracl72](https://github.com/sandracl72), "
+"[@sancarlim](https://github.com/sancarlim), "
 "[@gubertoli](https://github.com/gubertoli), "
 "[@Vingt100](https://github.com/Vingt100), "
 "[@MakGulati](https://github.com/MakGulati), "
diff --git a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
index 37158d25800f..e37298dfa972 100644
--- a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
@@ -12905,11 +12905,11 @@ msgid ""
 " "
 "[strategy](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py),"
 " and a [code "
-"example](https://github.com/adap/flower/tree/main/examples/quickstart_xgboost_horizontal)"
+"example](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart)"
 " that demonstrates the usage of this new strategy in an XGBoost project."
 msgstr ""
 "XGBoost 是一种基于树的集合机器学习算法，它使用梯度提升来提高模型的准确性。我们添加了一个新的 "
-"\"FedXgbNnAvg\"[策略](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py)和一个[代码示例](https://github.com/adap/flower/tree/main/examples/quickstart_xgboost_horizontal)，演示如何在"
+"\"FedXgbNnAvg\"[策略](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py)和一个[代码示例](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart)，演示如何在"
 " XGBoost 项目中使用这个新策略。"
 
 #: ../../source/ref-changelog.md:300
@@ -13094,10 +13094,10 @@ msgstr ""
 msgid ""
 "TabNet is a powerful and flexible framework for training machine learning"
 " models on tabular data. We now have a federated example using Flower: "
-"[https://github.com/adap/flower/tree/main/examples/tabnet](https://github.com/adap/flower/tree/main/examples/quickstart_tabnet)."
+"[quickstart-tabnet](https://github.com/adap/flower/tree/main/examples/quickstart-tabnet)."
 msgstr ""
 "TabNet 是一个强大而灵活的框架，用于在表格数据上训练机器学习模型。我们现在有一个使用 Flower "
-"的联邦示例：[https://github.com/adap/flower/tree/main/examples/tabnet](https://github.com/adap/flower/tree/main/examples/quickstart_tabnet)。"
+"的联邦示例：[quickstart-tabnet](https://github.com/adap/flower/tree/main/examples/quickstart-tabnet)。"
 
 #: ../../source/ref-changelog.md:334
 msgid ""
@@ -13303,13 +13303,13 @@ msgstr ""
 
 #: ../../source/ref-changelog.md:374
 msgid ""
-"A new code example (`quickstart_fastai`) demonstrates federated learning "
+"A new code example (`quickstart-fastai`) demonstrates federated learning "
 "with [fastai](https://www.fast.ai/) and Flower. You can find it here: "
-"[quickstart_fastai](https://github.com/adap/flower/tree/main/examples/quickstart_fastai)."
+"[quickstart-fastai](https://github.com/adap/flower/tree/main/examples/quickstart-fastai)."
 msgstr ""
-"一个新的代码示例（`quickstart_fastai`）演示了使用 [fastai](https://www.fast.ai/) 和 "
+"一个新的代码示例（`quickstart-fastai`）演示了使用 [fastai](https://www.fast.ai/) 和 "
 "Flower 的联邦学习。您可以在这里找到它： "
-"[quickstart_fastai](https://github.com/adap/flower/tree/main/examples/quickstart_fastai)。"
+"[quickstart-fastai](https://github.com/adap/flower/tree/main/examples/quickstart-fastai)。"
 
 #: ../../source/ref-changelog.md:376
 msgid ""
@@ -13696,12 +13696,12 @@ msgstr ""
 
 #: ../../source/ref-changelog.md:453
 msgid ""
-"A new code example (`quickstart_pandas`) demonstrates federated analytics"
+"A new code example (`quickstart-pandas`) demonstrates federated analytics"
 " with Pandas and Flower. You can find it here: "
-"[quickstart_pandas](https://github.com/adap/flower/tree/main/examples/quickstart_pandas)."
+"[quickstart-pandas](https://github.com/adap/flower/tree/main/examples/quickstart-pandas)."
 msgstr ""
-"新代码示例（`quickstart_pandas`）演示了使用 Pandas 和 Flower 进行联邦分析。您可以在此处找到它： "
-"[quickstart_pandas](https://github.com/adap/flower/tree/main/examples/quickstart_pandas)。"
+"新代码示例（`quickstart-pandas`）演示了使用 Pandas 和 Flower 进行联邦分析。您可以在此处找到它： "
+"[quickstart-pandas](https://github.com/adap/flower/tree/main/examples/quickstart-pandas)。"
 
 #: ../../source/ref-changelog.md:455
 msgid ""
@@ -14104,7 +14104,7 @@ msgid ""
 "[@mrinaald](https://github.com/mrinaald), "
 "[@zliel](https://github.com/zliel), "
 "[@MeiruiJiang](https://github.com/MeiruiJiang), "
-"[@sandracl72](https://github.com/sandracl72), "
+"[@sancarlim](https://github.com/sancarlim), "
 "[@gubertoli](https://github.com/gubertoli), "
 "[@Vingt100](https://github.com/Vingt100), "
 "[@MakGulati](https://github.com/MakGulati), "
@@ -14141,7 +14141,7 @@ msgstr ""
 "[@mrinaald](https://github.com/mrinaald), "
 "[@zliel](https://github.com/zliel), "
 "[@MeiruiJiang](https://github.com/MeiruiJiang), "
-"[@sandracl72](https://github.com/sandracl72), "
+"[@sancarlim](https://github.com/sancarlim), "
 "[@gubertoli](https://github.com/gubertoli), "
 "[@Vingt100](https://github.com/Vingt100), "
 "[@MakGulati](https://github.com/MakGulati), "
diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index b4bd4a5b89e0..43c1fc601f10 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -295,7 +295,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Introduce support for XGBoost (**`FedXgbNnAvg` **strategy and example)** ([#1694](https://github.com/adap/flower/pull/1694), [#1709](https://github.com/adap/flower/pull/1709), [#1715](https://github.com/adap/flower/pull/1715), [#1717](https://github.com/adap/flower/pull/1717), [#1763](https://github.com/adap/flower/pull/1763), [#1795](https://github.com/adap/flower/pull/1795))
 
-  XGBoost is a tree-based ensemble machine learning algorithm that uses gradient boosting to improve model accuracy. We added a new `FedXgbNnAvg` [strategy](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py), and a [code example](https://github.com/adap/flower/tree/main/examples/quickstart_xgboost_horizontal) that demonstrates the usage of this new strategy in an XGBoost project.
+  XGBoost is a tree-based ensemble machine learning algorithm that uses gradient boosting to improve model accuracy. We added a new `FedXgbNnAvg` [strategy](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy/fedxgb_nn_avg.py), and a [code example](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart) that demonstrates the usage of this new strategy in an XGBoost project.
 
 - **Introduce iOS SDK (preview)** ([#1621](https://github.com/adap/flower/pull/1621), [#1764](https://github.com/adap/flower/pull/1764))
 
@@ -329,7 +329,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Add new example using** `TabNet` **and Flower** ([#1725](https://github.com/adap/flower/pull/1725))
 
-  TabNet is a powerful and flexible framework for training machine learning models on tabular data. We now have a federated example using Flower: [https://github.com/adap/flower/tree/main/examples/tabnet](https://github.com/adap/flower/tree/main/examples/quickstart_tabnet).
+  TabNet is a powerful and flexible framework for training machine learning models on tabular data. We now have a federated example using Flower: [quickstart-tabnet](https://github.com/adap/flower/tree/main/examples/quickstart-tabnet).
 
 - **Add new how-to guide for monitoring simulations** ([#1649](https://github.com/adap/flower/pull/1649))
 
@@ -371,7 +371,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Add new example of Federated Learning using fastai and Flower** ([#1598](https://github.com/adap/flower/pull/1598))
 
-  A new code example (`quickstart_fastai`) demonstrates federated learning with [fastai](https://www.fast.ai/) and Flower. You can find it here: [quickstart_fastai](https://github.com/adap/flower/tree/main/examples/quickstart_fastai).
+  A new code example (`quickstart-fastai`) demonstrates federated learning with [fastai](https://www.fast.ai/) and Flower. You can find it here: [quickstart-fastai](https://github.com/adap/flower/tree/main/examples/quickstart-fastai).
 
 - **Make Android example compatible with** `flwr >= 1.0.0` **and the latest versions of Android** ([#1603](https://github.com/adap/flower/pull/1603))
 
@@ -450,7 +450,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Add new Federated Analytics with Pandas example** ([#1469](https://github.com/adap/flower/pull/1469), [#1535](https://github.com/adap/flower/pull/1535))
 
-  A new code example (`quickstart_pandas`) demonstrates federated analytics with Pandas and Flower. You can find it here: [quickstart_pandas](https://github.com/adap/flower/tree/main/examples/quickstart_pandas).
+  A new code example (`quickstart-pandas`) demonstrates federated analytics with Pandas and Flower. You can find it here: [quickstart-pandas](https://github.com/adap/flower/tree/main/examples/quickstart-pandas).
 
 - **Add new strategies: Krum and MultiKrum** ([#1481](https://github.com/adap/flower/pull/1481))
 
@@ -549,7 +549,7 @@ None
 
 We would like to give our **special thanks** to all the contributors who made Flower 1.0 possible (in reverse [GitHub Contributors](https://github.com/adap/flower/graphs/contributors) order):
 
-[@rtaiello](https://github.com/rtaiello), [@g-pichler](https://github.com/g-pichler), [@rob-luke](https://github.com/rob-luke), [@andreea-zaharia](https://github.com/andreea-zaharia), [@kinshukdua](https://github.com/kinshukdua), [@nfnt](https://github.com/nfnt), [@tatiana-s](https://github.com/tatiana-s), [@TParcollet](https://github.com/TParcollet), [@vballoli](https://github.com/vballoli), [@negedng](https://github.com/negedng), [@RISHIKESHAVAN](https://github.com/RISHIKESHAVAN), [@hei411](https://github.com/hei411), [@SebastianSpeitel](https://github.com/SebastianSpeitel), [@AmitChaulwar](https://github.com/AmitChaulwar), [@Rubiel1](https://github.com/Rubiel1), [@FANTOME-PAN](https://github.com/FANTOME-PAN), [@Rono-BC](https://github.com/Rono-BC), [@lbhm](https://github.com/lbhm), [@sishtiaq](https://github.com/sishtiaq), [@remde](https://github.com/remde), [@Jueun-Park](https://github.com/Jueun-Park), [@architjen](https://github.com/architjen), [@PratikGarai](https://github.com/PratikGarai), [@mrinaald](https://github.com/mrinaald), [@zliel](https://github.com/zliel), [@MeiruiJiang](https://github.com/MeiruiJiang), [@sandracl72](https://github.com/sandracl72), [@gubertoli](https://github.com/gubertoli), [@Vingt100](https://github.com/Vingt100), [@MakGulati](https://github.com/MakGulati), [@cozek](https://github.com/cozek), [@jafermarq](https://github.com/jafermarq), [@sisco0](https://github.com/sisco0), [@akhilmathurs](https://github.com/akhilmathurs), [@CanTuerk](https://github.com/CanTuerk), [@mariaboerner1987](https://github.com/mariaboerner1987), [@pedropgusmao](https://github.com/pedropgusmao), [@tanertopal](https://github.com/tanertopal), [@danieljanes](https://github.com/danieljanes).
+[@rtaiello](https://github.com/rtaiello), [@g-pichler](https://github.com/g-pichler), [@rob-luke](https://github.com/rob-luke), [@andreea-zaharia](https://github.com/andreea-zaharia), [@kinshukdua](https://github.com/kinshukdua), [@nfnt](https://github.com/nfnt), [@tatiana-s](https://github.com/tatiana-s), [@TParcollet](https://github.com/TParcollet), [@vballoli](https://github.com/vballoli), [@negedng](https://github.com/negedng), [@RISHIKESHAVAN](https://github.com/RISHIKESHAVAN), [@hei411](https://github.com/hei411), [@SebastianSpeitel](https://github.com/SebastianSpeitel), [@AmitChaulwar](https://github.com/AmitChaulwar), [@Rubiel1](https://github.com/Rubiel1), [@FANTOME-PAN](https://github.com/FANTOME-PAN), [@Rono-BC](https://github.com/Rono-BC), [@lbhm](https://github.com/lbhm), [@sishtiaq](https://github.com/sishtiaq), [@remde](https://github.com/remde), [@Jueun-Park](https://github.com/Jueun-Park), [@architjen](https://github.com/architjen), [@PratikGarai](https://github.com/PratikGarai), [@mrinaald](https://github.com/mrinaald), [@zliel](https://github.com/zliel), [@MeiruiJiang](https://github.com/MeiruiJiang), [@sancarlim](https://github.com/sancarlim), [@gubertoli](https://github.com/gubertoli), [@Vingt100](https://github.com/Vingt100), [@MakGulati](https://github.com/MakGulati), [@cozek](https://github.com/cozek), [@jafermarq](https://github.com/jafermarq), [@sisco0](https://github.com/sisco0), [@akhilmathurs](https://github.com/akhilmathurs), [@CanTuerk](https://github.com/CanTuerk), [@mariaboerner1987](https://github.com/mariaboerner1987), [@pedropgusmao](https://github.com/pedropgusmao), [@tanertopal](https://github.com/tanertopal), [@danieljanes](https://github.com/danieljanes).
 
 ### Incompatible changes
 

From b6179b648549ad8fd0bb270dc6602c3e054cc2be Mon Sep 17 00:00:00 2001
From: Danny <volleyballlive@googlemail.com>
Date: Fri, 1 Mar 2024 17:15:21 +0100
Subject: [PATCH 089/102] Fix typos in baseline/* (#3043)

---
 baselines/depthfl/README.md                                 | 2 +-
 baselines/doc/source/how-to-use-baselines.rst               | 6 +++---
 baselines/fedavgm/README.md                                 | 2 +-
 baselines/fedbn/README.md                                   | 2 +-
 baselines/fedmlb/README.md                                  | 4 ++--
 baselines/fedpara/README.md                                 | 2 +-
 baselines/fedper/README.md                                  | 6 +++---
 baselines/fedprox/README.md                                 | 2 +-
 baselines/fedstar/README.md                                 | 6 +++---
 baselines/fedvssl/README.md                                 | 2 +-
 baselines/fedwav2vec2/README.md                             | 4 ++--
 .../flwr_baselines/publications/fedavg_mnist/client.py      | 2 +-
 baselines/heterofl/README.md                                | 2 +-
 baselines/hfedxgboost/hfedxgboost/client.py                 | 2 +-
 baselines/moon/README.md                                    | 2 +-
 baselines/niid_bench/README.md                              | 4 ++--
 16 files changed, 25 insertions(+), 25 deletions(-)

diff --git a/baselines/depthfl/README.md b/baselines/depthfl/README.md
index b8ab7ed18571..ab26f9c8f8c9 100644
--- a/baselines/depthfl/README.md
+++ b/baselines/depthfl/README.md
@@ -124,7 +124,7 @@ python -m depthfl.main --multirun exclusive_learning=true model_size=1,2,3,4
 
 **Table 2** 
 
-100% (a), 75%(b), 50%(c), 25% (d) cases are exclusive learning scenario. 100% (a) exclusive learning means, the global model and every local model are equal to the smallest local model, and 100% clients participate in learning. Likewise, 25% (d) exclusive learning means, the global model and every local model are equal to the larget local model, and only 25% clients participate in learning.
+100% (a), 75%(b), 50%(c), 25% (d) cases are exclusive learning scenario. 100% (a) exclusive learning means, the global model and every local model are equal to the smallest local model, and 100% clients participate in learning. Likewise, 25% (d) exclusive learning means, the global model and every local model are equal to the largest local model, and only 25% clients participate in learning.
 
 | Scaling Method | Dataset | Global Model | 100% (a) | 75% (b) | 50% (c) | 25% (d) | 
 | :---: | :---: | :---: | :---: | :---: | :---: | :---: | 
diff --git a/baselines/doc/source/how-to-use-baselines.rst b/baselines/doc/source/how-to-use-baselines.rst
index ed47438ad5a9..4704a9b6074e 100644
--- a/baselines/doc/source/how-to-use-baselines.rst
+++ b/baselines/doc/source/how-to-use-baselines.rst
@@ -33,7 +33,7 @@ Setting up your machine
 
 Common to all baselines is `Poetry <https://python-poetry.org/docs/>`_, a tool to manage Python dependencies. Baselines also make use of `Pyenv <https://github.com/pyenv/pyenv>`_. You'll need to install both on your system before running a baseline. What follows is a step-by-step guide on getting :code:`pyenv` and :code:`Poetry` installed on your system.
 
-Let's begin by installing :code:`pyenv`. We'll be following the standard procedure. Please refere to the `pyenv docs <https://github.com/pyenv/pyenv#installation>`_ for alternative ways of installing it.
+Let's begin by installing :code:`pyenv`. We'll be following the standard procedure. Please refer to the `pyenv docs <https://github.com/pyenv/pyenv#installation>`_ for alternative ways of installing it.
 
 .. code-block:: bash
 
@@ -49,7 +49,7 @@ Let's begin by installing :code:`pyenv`. We'll be following the standard procedu
   command -v pyenv >/dev/null || export PATH="$PYENV_ROOT/bin:$PATH"
   eval "$(pyenv init -)"
 
-Verify your installtion by opening a new terminal and
+Verify your installation by opening a new terminal and
 
 .. code-block:: bash
 
@@ -63,7 +63,7 @@ Then you can proceed and install any version of Python. Most baselines currently
 
   pyenv install 3.10.6
   # this will take a little while
-  # once done, you should see that that version is avaialble
+  # once done, you should see that that version is available
   pyenv versions
   # system
   # * 3.10.6  # <-- you just installed this
diff --git a/baselines/fedavgm/README.md b/baselines/fedavgm/README.md
index 0953331964a7..5b8ddfcad6a8 100644
--- a/baselines/fedavgm/README.md
+++ b/baselines/fedavgm/README.md
@@ -104,7 +104,7 @@ poetry shell
 ```
 
 ### Google Colab
-If you want to setup the environemnt on Google Colab, please executed the script `conf-colab.sh`, just use the Colab terminal and the following:
+If you want to setup the environment on Google Colab, please executed the script `conf-colab.sh`, just use the Colab terminal and the following:
 
 ```bash
 chmod +x conf-colab.sh
diff --git a/baselines/fedbn/README.md b/baselines/fedbn/README.md
index d50c6f5bb605..37388894ff9d 100644
--- a/baselines/fedbn/README.md
+++ b/baselines/fedbn/README.md
@@ -41,7 +41,7 @@ A more detailed explanation of the datasets is given in the following table.
 
 |                    | MNIST              | MNIST-M                                                  | SVHN                      | USPS                                                         | SynthDigits                                                                      |
 | ------------------ | ------------------ | -------------------------------------------------------- | ------------------------- | ------------------------------------------------------------ | -------------------------------------------------------------------------------- |
-| data type          | handwritten digits | MNIST modification randomly colored with colored patches | Street view house numbers | handwritten digits from envelopes by the U.S. Postal Service | Syntehtic digits Windows TM font varying the orientation, blur and stroke colors |
+| data type          | handwritten digits | MNIST modification randomly colored with colored patches | Street view house numbers | handwritten digits from envelopes by the U.S. Postal Service | Synthetic digits Windows TM font varying the orientation, blur and stroke colors |
 | color              | greyscale          | RGB                                                      | RGB                       | greyscale                                                    | RGB                                                                              |
 | pixelsize          | 28x28              | 28 x 28                                                  | 32 x32                    | 16 x16                                                       | 32 x32                                                                           |
 | labels             | 0-9                | 0-9                                                      | 1-10                      | 0-9                                                          | 1-10                                                                             |
diff --git a/baselines/fedmlb/README.md b/baselines/fedmlb/README.md
index 47cc69b48e09..0efad9db1d4f 100644
--- a/baselines/fedmlb/README.md
+++ b/baselines/fedmlb/README.md
@@ -222,7 +222,7 @@ You can override settings directly from the command line in this way:
 ```bash
 python -m fedmlb.main clients_per_round=10 # this will run using 10 clients per round instead of 5 clients as the default config 
 
-# this will select the dataset partitioned with 0.6 concentration paramater instead of 0.3 as the default config
+# this will select the dataset partitioned with 0.6 concentration parameter instead of 0.3 as the default config
 python -m fedmlb.main dataset_config.alpha_dirichlet=0.6
 ```
 
@@ -249,7 +249,7 @@ python -m fedmlb.main dataset_config.alpha_dirichlet=0.6 total_clients=500 clien
 ```
 
 #### Tiny-Imagenet
-For Tiny-ImageNet, as in the orginal paper, batch size of local updates should be set 
+For Tiny-ImageNet, as in the original paper, batch size of local updates should be set 
 to 100 in settings with 100 clients and to 20 in settings with 500 clients;
 this is equal to set the amount of local_updates to 50 (as the default) -- 
 so no change to batch size is required --, in fact
diff --git a/baselines/fedpara/README.md b/baselines/fedpara/README.md
index 89cca76a2aa2..55acd228c4db 100644
--- a/baselines/fedpara/README.md
+++ b/baselines/fedpara/README.md
@@ -88,7 +88,7 @@ As for the parameters ratio ($\gamma$) we use the following model sizes. As in t
 
 - The Jacobian correction was not incorporated into our implementation, primarily due to the lack of explicit instructions in the paper regarding the specific implementation of the dual update principle mentioned in the Jacobian correction section.
 
-- It was observed that data generation is crutial for model convergence
+- It was observed that data generation is crucial for model convergence
 
 ## Environment Setup
 To construct the Python environment follow these steps:
diff --git a/baselines/fedper/README.md b/baselines/fedper/README.md
index 157bc22d2da5..757c396cc170 100644
--- a/baselines/fedper/README.md
+++ b/baselines/fedper/README.md
@@ -19,7 +19,7 @@ dataset: [CIFAR-10, FLICKR-AES]
 
 **What’s implemented:** The code in this directory replicates the experiments in _Federated Learning with Personalization Layers_ (Arivazhagan et al., 2019) for CIFAR10 and FLICKR-AES datasets, which proposed the `FedPer` model. Specifically, it replicates the results found in figures 2, 4, 7, and 8 in their paper. __Note__ that there is typo in the caption of Figure 4 in the article, it should be CIFAR10 and __not__ CIFAR100. 
 
-**Datasets:** CIFAR10 from PyTorch's Torchvision and FLICKR-AES. FLICKR-AES was proposed as dataset in _Personalized Image Aesthetics_ (Ren et al., 2017) and can be downloaded using a link provided on thier [GitHub](https://github.com/alanspike/personalizedImageAesthetics). One must first download FLICKR-AES-001.zip (5.76GB), extract all inside and place in baseline/FedPer/datasets. To this location, also download the other 2 related files: (1) FLICKR-AES_image_labeled_by_each_worker.csv, and (2) FLICKR-AES_image_score.txt. Images are also scaled to 224x224 for both datasets. This is not explicitly stated in the paper but seems to be boosting performance. Also, for FLICKR dataset, it is stated in the paper that they use data from clients with more than 60 and less than 290 rated images. This amounts to circa 60 clients and we randomly select 30 out of these (as in paper). Therefore, the results might differ somewhat but only slighly. Since the pre-processing steps in the paper are somewhat obscure, the metric values in the plots below may differ slightly, but not the overall results and findings. 
+**Datasets:** CIFAR10 from PyTorch's Torchvision and FLICKR-AES. FLICKR-AES was proposed as dataset in _Personalized Image Aesthetics_ (Ren et al., 2017) and can be downloaded using a link provided on their [GitHub](https://github.com/alanspike/personalizedImageAesthetics). One must first download FLICKR-AES-001.zip (5.76GB), extract all inside and place in baseline/FedPer/datasets. To this location, also download the other 2 related files: (1) FLICKR-AES_image_labeled_by_each_worker.csv, and (2) FLICKR-AES_image_score.txt. Images are also scaled to 224x224 for both datasets. This is not explicitly stated in the paper but seems to be boosting performance. Also, for FLICKR dataset, it is stated in the paper that they use data from clients with more than 60 and less than 290 rated images. This amounts to circa 60 clients and we randomly select 30 out of these (as in paper). Therefore, the results might differ somewhat but only slightly. Since the pre-processing steps in the paper are somewhat obscure, the metric values in the plots below may differ slightly, but not the overall results and findings. 
 
 ```bash
 # These steps are not needed if you are only interested in CIFAR-10
@@ -61,7 +61,7 @@ It's worth mentioning that GPU memory for each client is ~7.5GB. When training o
 
 Please see how models are implemented using a so called model_manager and model_split class since FedPer uses head and base layers in a neural network. These classes are defined in the models.py file and thereafter called when building new models in the directory /implemented_models. Please, extend and add new models as you wish. 
 
-**Dataset:** CIFAR10, FLICKR-AES. CIFAR10 will be partitioned based on number of classes for data that each client shall recieve e.g. 4 allocated classes could be [1, 3, 5, 9]. FLICKR-AES is an unbalanced dataset, so there we only apply random sampling. 
+**Dataset:** CIFAR10, FLICKR-AES. CIFAR10 will be partitioned based on number of classes for data that each client shall receive e.g. 4 allocated classes could be [1, 3, 5, 9]. FLICKR-AES is an unbalanced dataset, so there we only apply random sampling. 
 
 **Training Hyperparameters:** The hyperparameters can be found in conf/base.yaml file which is the configuration file for the main script. 
 
@@ -77,7 +77,7 @@ Please see how models are implemented using a so called model_manager and model_
 | algorithm | fedavg|
 
 **Stateful Clients:**
-In this Baseline (FedPer), we must store the state of the local client head while aggregation of body parameters happen at the server. Flower is currently making this possible but for the time being, we reside to storing client _head_ state in a folder called client_states. We store the values after each fit and evaluate function carried out on each client, and call for the state before executing these funcitons. Moreover, the state of a unique client is accessed using the client ID. 
+In this Baseline (FedPer), we must store the state of the local client head while aggregation of body parameters happen at the server. Flower is currently making this possible but for the time being, we reside to storing client _head_ state in a folder called client_states. We store the values after each fit and evaluate function carried out on each client, and call for the state before executing these functions. Moreover, the state of a unique client is accessed using the client ID. 
 
 > NOTE: This is a work-around so that the local head parameters are not reset before each fit and evaluate. Nevertheless, it can come to change with future releases. 
 
diff --git a/baselines/fedprox/README.md b/baselines/fedprox/README.md
index a31663746d99..f0abc1451635 100644
--- a/baselines/fedprox/README.md
+++ b/baselines/fedprox/README.md
@@ -91,7 +91,7 @@ To run using FedAvg:
 # This is done so to match the experimental setup in the FedProx paper
 python -m fedprox.main --config-name fedavg
 
-# this config can also be overriden from the CLI
+# this config can also be overridden from the CLI
 ```
 
 ## Expected results
diff --git a/baselines/fedstar/README.md b/baselines/fedstar/README.md
index 9005ae01679d..fe782e3e829f 100644
--- a/baselines/fedstar/README.md
+++ b/baselines/fedstar/README.md
@@ -44,11 +44,11 @@ Next, you'll need to download the datasets. In the case of SpeechCommands, some
 # Make the shell script executable
 chmod +x setup_datasets.sh
 
-# The below script will download the datasets and create a directory structure requir to run this experiment.
+# The below script will download the datasets and create a directory structure require to run this experiment.
 ./setup_datasets.sh
 
 # If you want to run the SpeechCommands experiment, pre-process the dataset
-# This will genereate a few training example from the _silence_ category
+# This will generate a few training example from the _silence_ category
 python -m fedstar.dataset_preparation
 # Please note the above will make following changes:
 #    * Add new files to datasets/speech_commands/Data/Train/_silence_
@@ -64,7 +64,7 @@ python -m fedstar.dataset_preparation
 
 ```python
 # For Eg:- We have a system with two GPUs with 8GB and 4GB VRAM.
-#          The modified varaible will looks like below.
+#          The modified variable will looks like below.
 gpu_free_mem = [8000,4000]
 ```
 
diff --git a/baselines/fedvssl/README.md b/baselines/fedvssl/README.md
index a9de7a52f54c..dcce0159bc19 100644
--- a/baselines/fedvssl/README.md
+++ b/baselines/fedvssl/README.md
@@ -152,7 +152,7 @@ To run using `FedAvg`:
 # This is done so to match the experimental setup in the paper
 python -m fedvssl.main strategy.fedavg=true
 
-# This config can also be overriden.
+# This config can also be overridden.
 ```
 
 Running any of the above will create a directory structure in the form of `outputs/<DATE>/<TIME>/fedvssl_results` to save the global checkpoints and the local clients' training logs.
diff --git a/baselines/fedwav2vec2/README.md b/baselines/fedwav2vec2/README.md
index 0b41c6172976..c7191cd50855 100644
--- a/baselines/fedwav2vec2/README.md
+++ b/baselines/fedwav2vec2/README.md
@@ -59,7 +59,7 @@ For more details, please refer to the relevant section in the paper.
 | `sb_config` | `fedwav2vec2/conf/sb_config/w2v2.yaml` | Speechbrain config file for architecture model. Please refer to [SpeechBrain](https://github.com/speechbrain/speechbrain) for more information |
 | `rounds` | `100` | Indicate the number of Federated Learning (FL) rounds|
 | `local_epochs` | `20` | Specify the number of training epochs at the client side |
-| `total_clients` | `1943` | Size of client pool, with a maxium set at 1943 clients|
+| `total_clients` | `1943` | Size of client pool, with a maximum set at 1943 clients|
 | `server_cid` | `19999` | ID of the server to distinguish from the client's ID |
 | `server_device` | `cuda` | You can choose between `cpu` or `cuda` for centralised evaluation, but it is recommended to use `cuda`|
 | `parallel_backend` | `false` | Multi-gpus training. Only active if you have more than 1 gpu per client | 
@@ -109,7 +109,7 @@ python -m fedwav2vec2.dataset_preparation
 # Run with default arguments (one client per GPU)
 python -m fedwav2vec2.main
 
-# if you have a large GPU (32GB+) you migth want to fit two per GPU
+# if you have a large GPU (32GB+) you might want to fit two per GPU
 python -m fedwav2vec2.main client_resources.num_gpus=0.5
 
 # the global model can be saved at the end of each round if you specify a checkpoint path
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/client.py b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/client.py
index b7ce7cad7fac..023cfa8d4716 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/client.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/client.py
@@ -80,7 +80,7 @@ def gen_client_fn(
     Parameters
     ----------
     device : torch.device
-        The device on which the the client will train on and test on.
+        The device on which the client will train on and test on.
     iid : bool
         The way to partition the data for each client, i.e. whether the data
         should be independent and identically distributed between the clients
diff --git a/baselines/heterofl/README.md b/baselines/heterofl/README.md
index 6e9c32077e9b..dcb54900878f 100644
--- a/baselines/heterofl/README.md
+++ b/baselines/heterofl/README.md
@@ -95,7 +95,7 @@ To run HeteroFL experiments in poetry activated environment:
 # The main experiment implemented in your baseline using default hyperparameters (that should be setup in the Hydra configs)
 # should run (including dataset download and necessary partitioning) by executing the command:
 
-python -m heterofl.main  # Which runs the heterofl with arguments availbale in heterfl/conf/base.yaml
+python -m heterofl.main  # Which runs the heterofl with arguments available in heterfl/conf/base.yaml
 
 # We could override the settings that were specified in base.yaml using the command-line-arguments
 # Here's an example for changing the dataset name, non-iid and model
diff --git a/baselines/hfedxgboost/hfedxgboost/client.py b/baselines/hfedxgboost/hfedxgboost/client.py
index 990a1cf75a81..bc5bfa5807a4 100644
--- a/baselines/hfedxgboost/hfedxgboost/client.py
+++ b/baselines/hfedxgboost/hfedxgboost/client.py
@@ -214,7 +214,7 @@ def fit(self, ins: FitIns) -> FitRes:
         aggregated_trees = ins.parameters[1]  # type: ignore # noqa: E501 # pylint: disable=line-too-long
 
         if isinstance(aggregated_trees, list):
-            print("Client " + self.cid + ": recieved", len(aggregated_trees), "trees")
+            print("Client " + self.cid + ": received", len(aggregated_trees), "trees")
         else:
             print("Client " + self.cid + ": only had its own tree")
         trainloader: Any = single_tree_preds_from_each_client(
diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index 05ab4ef68469..ef5f568aaa31 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -105,7 +105,7 @@ python -m moon.main --config-name cifar100_fedprox
 
 ## Expected Results
 
-You can find the output logs of a single run in this [link](https://drive.google.com/drive/folders/1YZEU2NcHWEHVyuJMlc1QvBSAvNMjH-aR?usp=share_link). After running the above commands, you can see the accuracy list at the end of the ouput, which is the test accuracy of the global model. For example, in one running, for CIFAR-10 with MOON, the accuracy after running 100 rounds is 0.7071. 
+You can find the output logs of a single run in this [link](https://drive.google.com/drive/folders/1YZEU2NcHWEHVyuJMlc1QvBSAvNMjH-aR?usp=share_link). After running the above commands, you can see the accuracy list at the end of the output, which is the test accuracy of the global model. For example, in one running, for CIFAR-10 with MOON, the accuracy after running 100 rounds is 0.7071. 
 
 For CIFAR-10 with FedProx, the accuracy after running 100 rounds is 0.6852. For CIFAR100 with MOON, the accuracy after running 100 rounds is 0.6636. For CIFAR100 with FedProx, the accuracy after running 100 rounds is 0.6494. The results are summarized below:
 
diff --git a/baselines/niid_bench/README.md b/baselines/niid_bench/README.md
index 6553a3cfdb1e..8c6ba11c5755 100644
--- a/baselines/niid_bench/README.md
+++ b/baselines/niid_bench/README.md
@@ -21,7 +21,7 @@ algorithms: [FedAvg, SCAFFOLD, FedProx, FedNova]
 
 **Datasets:** MNIST, CIFAR10, and Fashion-mnist from PyTorch's Torchvision
 
-**Hardware Setup:** These experiments were run on a linux server with 56 CPU threads with 250 GB Ram. There are 105 configurations to run per seed and at any time 7 configurations have been run parallely. The experiments required close to 12 hrs to finish for one seed. Nevertheless, to run a subset of configurations, such as only one FL protocol across all datasets and splits, a machine with 4-8 threads and 16 GB memory can run in reasonable time.
+**Hardware Setup:** These experiments were run on a linux server with 56 CPU threads with 250 GB Ram. There are 105 configurations to run per seed and at any time 7 configurations have been run parallelly. The experiments required close to 12 hrs to finish for one seed. Nevertheless, to run a subset of configurations, such as only one FL protocol across all datasets and splits, a machine with 4-8 threads and 16 GB memory can run in reasonable time.
 
 **Contributors:** Aashish Kolluri, PhD Candidate, National University of Singapore
 
@@ -59,7 +59,7 @@ For FedProx algorithm the proximal parameter is tuned from values {0.001, 0.01,
 ## Environment Setup
 
 ```bash
-# Setup the base poetry enviroment from the niid_bench directory
+# Setup the base poetry environment from the niid_bench directory
 # Set python version
 pyenv local 3.10.6
 # Tell poetry to use python 3.10

From 2ad6f3e419bcceecc9e25e8916f72b26daae4a15 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Fri, 1 Mar 2024 18:00:01 +0100
Subject: [PATCH 090/102] Improve threading in `Simulation Engine`. (#3047)

---
 src/py/flwr/server/run_serverapp.py      |  15 +++-
 src/py/flwr/simulation/run_simulation.py | 101 ++++++++++++++++-------
 2 files changed, 84 insertions(+), 32 deletions(-)

diff --git a/src/py/flwr/server/run_serverapp.py b/src/py/flwr/server/run_serverapp.py
index 19fd16fb0c1a..d4f21cbf20dc 100644
--- a/src/py/flwr/server/run_serverapp.py
+++ b/src/py/flwr/server/run_serverapp.py
@@ -16,9 +16,11 @@
 
 
 import argparse
+import asyncio
 import sys
 from logging import DEBUG, WARN
 from pathlib import Path
+from typing import Optional
 
 from flwr.common import Context, EventType, RecordSet, event
 from flwr.common.logger import log
@@ -27,7 +29,12 @@
 from .server_app import ServerApp, load_server_app
 
 
-def run(server_app_attr: str, driver: Driver, server_app_dir: str) -> None:
+def run(
+    server_app_attr: str,
+    driver: Driver,
+    server_app_dir: str,
+    stop_event: Optional[asyncio.Event] = None,
+) -> None:
     """Run ServerApp with a given Driver."""
     if server_app_dir is not None:
         sys.path.insert(0, server_app_dir)
@@ -44,6 +51,12 @@ def _load() -> ServerApp:
     # Call ServerApp
     server_app(driver=driver, context=context)
 
+    log(DEBUG, "ServerApp finished running.")
+    # Upon completion, trigger stop event if one was passed
+    if stop_event is not None:
+        log(DEBUG, "Triggering stop event.")
+        stop_event.set()
+
 
 def run_server_app() -> None:
     """Run Flower server app."""
diff --git a/src/py/flwr/simulation/run_simulation.py b/src/py/flwr/simulation/run_simulation.py
index ebe76944e779..71cd45fdd0a7 100644
--- a/src/py/flwr/simulation/run_simulation.py
+++ b/src/py/flwr/simulation/run_simulation.py
@@ -20,11 +20,12 @@
 import threading
 import traceback
 from logging import ERROR, INFO, WARNING
+from time import sleep
+from typing import Any, Callable
 
 import grpc
 
 from flwr.common import EventType, event, log
-from flwr.common.exit_handlers import register_exit_handlers
 from flwr.server.driver.driver import Driver
 from flwr.server.run_serverapp import run
 from flwr.server.superlink.driver.driver_grpc import run_driver_api_grpc
@@ -33,6 +34,49 @@
 from flwr.simulation.ray_transport.utils import enable_tf_gpu_growth
 
 
+def run_serverapp_th(
+    server_app_attr: str,
+    driver: Driver,
+    server_app_dir: str,
+    f_stop: asyncio.Event,
+    delay_launch: int = 3,
+) -> threading.Thread:
+    """Run SeverApp in a thread."""
+    serverapp_th = threading.Thread(
+        target=run,
+        kwargs={
+            "server_app_attr": server_app_attr,
+            "driver": driver,
+            "server_app_dir": server_app_dir,
+            "stop_event": f_stop,  # will be set when `run()` finishes
+            # will trigger the shutdown of the Simulation Engine
+        },
+    )
+    sleep(delay_launch)
+    serverapp_th.start()
+    return serverapp_th
+
+
+def get_thread_exception_hook(stop_event: asyncio.Event) -> Callable[[Any], None]:
+    """Return a callback for when the ServerApp thread raises an exception."""
+
+    def execepthook(args: Any) -> None:
+        """Upon exception raised, log exception and trigger stop event."""
+        # log
+        log(
+            ERROR,
+            "The ServerApp thread triggered exception (%s): %s",
+            args.exc_type,
+            args.exc_value,
+        )
+        log(ERROR, traceback.format_exc())
+        # Set stop event
+        stop_event.set()
+        log(WARNING, "Triggered stop event for Simulation Engine.")
+
+    return execepthook
+
+
 def run_simulation() -> None:
     """Run Simulation Engine."""
     args = _parse_args_run_simulation().parse_args()
@@ -63,25 +107,8 @@ def run_simulation() -> None:
         certificates=None,
     )
 
-    # SuperLink with Simulation Engine
     f_stop = asyncio.Event()
-    superlink_th = threading.Thread(
-        target=vce.start_vce,
-        kwargs={
-            "num_supernodes": args.num_supernodes,
-            "client_app_module_name": args.client_app,
-            "backend_name": args.backend,
-            "backend_config_json_stream": backend_config,
-            "working_dir": args.dir,
-            "state_factory": state_factory,
-            "f_stop": f_stop,
-        },
-        daemon=False,
-    )
-
-    superlink_th.start()
-    event(EventType.RUN_SUPERLINK_ENTER)
-
+    serverapp_th = None
     try:
         # Initialize Driver
         driver = Driver(
@@ -89,30 +116,42 @@ def run_simulation() -> None:
             root_certificates=None,
         )
 
-        # Launch server app
-        run(args.server_app, driver, args.dir)
+        # Get and run ServerApp thread
+        serverapp_th = run_serverapp_th(args.server_app, driver, args.dir, f_stop)
+        # Setup an exception hook
+        threading.excepthook = get_thread_exception_hook(f_stop)
+
+        # SuperLink with Simulation Engine
+        event(EventType.RUN_SUPERLINK_ENTER)
+        vce.start_vce(
+            num_supernodes=args.num_supernodes,
+            client_app_module_name=args.client_app,
+            backend_name=args.backend,
+            backend_config_json_stream=backend_config,
+            working_dir=args.dir,
+            state_factory=state_factory,
+            f_stop=f_stop,
+        )
 
     except Exception as ex:
 
         log(ERROR, "An exception occurred: %s", ex)
         log(ERROR, traceback.format_exc())
-        raise RuntimeError(
-            "An error was encountered by the Simulation Engine. Ending simulation."
-        ) from ex
+        raise RuntimeError("An error was encountered. Ending simulation.") from ex
 
     finally:
 
+        # Stop Driver
+        driver_server.stop(grace=0)
         del driver
-
         # Trigger stop event
         f_stop.set()
 
-        register_exit_handlers(
-            grpc_servers=[driver_server],
-            bckg_threads=[superlink_th],
-            event_type=EventType.RUN_SUPERLINK_LEAVE,
-        )
-        superlink_th.join()
+        event(EventType.RUN_SUPERLINK_LEAVE)
+        if serverapp_th:
+            serverapp_th.join()
+
+    log(INFO, "Stopping Simulation Engine now.")
 
 
 def _parse_args_run_simulation() -> argparse.ArgumentParser:

From 4acc46ce5da0f1228d37728e11b7a24927ecc8c4 Mon Sep 17 00:00:00 2001
From: Danny <danny.heinrich@flower.ai>
Date: Sun, 3 Mar 2024 18:53:27 +0100
Subject: [PATCH 091/102] fix(docs): Fix more typos (#3050)

---
 baselines/fedvssl/README.md                       | 6 +++---
 doc/locales/pt_BR/LC_MESSAGES/framework-docs.po   | 4 ++--
 doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po | 6 +++---
 doc/source/ref-changelog.md                       | 4 ++--
 examples/advanced-tensorflow/README.md            | 2 +-
 examples/embedded-devices/README.md               | 2 +-
 examples/quickstart-jax/README.md                 | 2 +-
 examples/simulation-pytorch/README.md             | 2 +-
 examples/simulation-tensorflow/README.md          | 2 +-
 src/py/flwr/common/message.py                     | 4 ++--
 src/py/flwr/simulation/run_simulation.py          | 4 ++--
 11 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/baselines/fedvssl/README.md b/baselines/fedvssl/README.md
index dcce0159bc19..15bf66ab6a21 100644
--- a/baselines/fedvssl/README.md
+++ b/baselines/fedvssl/README.md
@@ -22,13 +22,13 @@ dataset: [UCF-101, Kinectics-400]
 ## About this baseline
 
 **Whats's implemented:** The code in this directory replicates the experiments in *Federated Self-supervised Learning for Video Understanding* (Rehman et al., 2022) for UCF-101, which proposed the FedVSSL algorithm. Specifically, it replicates the results for UCF-101 in Table 4 in the paper.
-As common SSL training pipline, this code has two parts: SSL pre-training in FL and downstream fine-tuning.
+As common SSL training pipeline, this code has two parts: SSL pre-training in FL and downstream fine-tuning.
 
 **Dataset:** [UCF-101](https://www.crcv.ucf.edu/data/UCF101.php)
 
 **Hardware Setup:** These experiments (SSL pre-train + downstream fine-tuning) were on a server with 6x RTX-3090 GPU and 128 CPU threads. Assuming a single client runs per GPU, on a RTX 3090 it takes 34s for a client to complete its local training (1 epoch); on an A100 this goes down to 17s. In this baseline 5 clients participate in each round for a total of 20 rounds. On a single A100 you can run the federated pre-training in under 1h.
 
-**Contributers:** Yasar Abbas Ur Rehman and Yan Gao
+**Contributors:** Yasar Abbas Ur Rehman and Yan Gao
 
 ## Experimental Setup
 
@@ -65,7 +65,7 @@ For UCF-101 dataset, one can simply follow the dataset preparation instruction b
 ## Environment Setup
 Please make sure you have installed CUDA 11.7 on your machine 
 (see [NVIDIA CUDA Toolkit website](https://developer.nvidia.com/cuda-11-7-0-download-archive)).
-To construct the Python environment follow these steps (asuming you arelady have `pyenv` and `Poetry` in your system):
+To construct the Python environment follow these steps (assuming you arelady have `pyenv` and `Poetry` in your system):
 
 ```bash
 # Set directory to use python 3.10 (install with `pyenv install <version>` if you don't have it)
diff --git a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
index ab1c43135c65..5a5d736ece38 100644
--- a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
@@ -13607,7 +13607,7 @@ msgstr ""
 msgid ""
 "Custom metric dictionaries are now used in two user-facing APIs: they are"
 " returned from Strategy methods `aggregate_fit`/`aggregate_evaluate` and "
-"they enable evaluation functions passed to build-in strategies (via "
+"they enable evaluation functions passed to built-in strategies (via "
 "`eval_fn`) to return more than two evaluation metrics. Strategies can "
 "even return *aggregated* metrics dictionaries for the server to keep "
 "track of."
@@ -13615,7 +13615,7 @@ msgstr ""
 
 #: ../../source/ref-changelog.md:847
 msgid ""
-"Stratey implementations should migrate their `aggregate_fit` and "
+"Strategy implementations should migrate their `aggregate_fit` and "
 "`aggregate_evaluate` methods to the new return type (e.g., by simply "
 "returning an empty `{}`), server-side evaluation functions should migrate"
 " from `return loss, accuracy` to `return loss, {\"accuracy\": accuracy}`."
diff --git a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
index e37298dfa972..f22b74db8896 100644
--- a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
@@ -15350,7 +15350,7 @@ msgstr ""
 msgid ""
 "Custom metric dictionaries are now used in two user-facing APIs: they are"
 " returned from Strategy methods `aggregate_fit`/`aggregate_evaluate` and "
-"they enable evaluation functions passed to build-in strategies (via "
+"they enable evaluation functions passed to built-in strategies (via "
 "`eval_fn`) to return more than two evaluation metrics. Strategies can "
 "even return *aggregated* metrics dictionaries for the server to keep "
 "track of."
@@ -15361,12 +15361,12 @@ msgstr ""
 
 #: ../../source/ref-changelog.md:847
 msgid ""
-"Stratey implementations should migrate their `aggregate_fit` and "
+"Strategy implementations should migrate their `aggregate_fit` and "
 "`aggregate_evaluate` methods to the new return type (e.g., by simply "
 "returning an empty `{}`), server-side evaluation functions should migrate"
 " from `return loss, accuracy` to `return loss, {\"accuracy\": accuracy}`."
 msgstr ""
-"Stratey 实现应将其 `aggregate_fit` 和 `aggregate_evaluate` "
+"Strategy 实现应将其 `aggregate_fit` 和 `aggregate_evaluate` "
 "方法迁移到新的返回类型（例如，只需返回空的 `{}`），服务器端评估函数应从 `return loss, accuracy` 迁移到 "
 "`return loss, {\"accuracy\": accuracy}`。"
 
diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 43c1fc601f10..1a6524d29353 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -842,9 +842,9 @@ We would like to give our **special thanks** to all the contributors who made Fl
 
   The Flower server is now fully task-agnostic, all remaining instances of task-specific metrics (such as `accuracy`) have been replaced by custom metrics dictionaries. Flower 0.15 introduced the capability to pass a dictionary containing custom metrics from client to server. As of this release, custom metrics replace task-specific metrics on the server.
 
-  Custom metric dictionaries are now used in two user-facing APIs: they are returned from Strategy methods `aggregate_fit`/`aggregate_evaluate` and they enable evaluation functions passed to build-in strategies (via `eval_fn`) to return more than two evaluation metrics. Strategies can even return *aggregated* metrics dictionaries for the server to keep track of.
+  Custom metric dictionaries are now used in two user-facing APIs: they are returned from Strategy methods `aggregate_fit`/`aggregate_evaluate` and they enable evaluation functions passed to built-in strategies (via `eval_fn`) to return more than two evaluation metrics. Strategies can even return *aggregated* metrics dictionaries for the server to keep track of.
 
-  Stratey implementations should migrate their `aggregate_fit` and `aggregate_evaluate` methods to the new return type (e.g., by simply returning an empty `{}`), server-side evaluation functions should migrate from `return loss, accuracy` to `return loss, {"accuracy": accuracy}`.
+  Strategy implementations should migrate their `aggregate_fit` and `aggregate_evaluate` methods to the new return type (e.g., by simply returning an empty `{}`), server-side evaluation functions should migrate from `return loss, accuracy` to `return loss, {"accuracy": accuracy}`.
 
   Flower 0.15-style return types are deprecated (but still supported), compatibility will be removed in a future release.
 
diff --git a/examples/advanced-tensorflow/README.md b/examples/advanced-tensorflow/README.md
index 59866fd99a06..94707b5cbc98 100644
--- a/examples/advanced-tensorflow/README.md
+++ b/examples/advanced-tensorflow/README.md
@@ -57,7 +57,7 @@ pip install -r requirements.txt
 
 ## Run Federated Learning with TensorFlow/Keras and Flower
 
-The included `run.sh` will call a script to generate certificates (which will be used by server and clients), start the Flower server (using `server.py`), sleep for 10 seconds to ensure the the server is up, and then start 10 Flower clients (using `client.py`). You can simply start everything in a terminal as follows:
+The included `run.sh` will call a script to generate certificates (which will be used by server and clients), start the Flower server (using `server.py`), sleep for 10 seconds to ensure the server is up, and then start 10 Flower clients (using `client.py`). You can simply start everything in a terminal as follows:
 
 ```shell
 # Once you have activated your environment
diff --git a/examples/embedded-devices/README.md b/examples/embedded-devices/README.md
index 5afe3965bd5b..f1c5931b823a 100644
--- a/examples/embedded-devices/README.md
+++ b/examples/embedded-devices/README.md
@@ -192,7 +192,7 @@ On the machine of your choice, launch the server:
 # Launch your server.
 # Will wait for at least 2 clients to be connected, then will train for 3 FL rounds
 # The command below will sample all clients connected (since sample_fraction=1.0)
-# The the server is dataset agnostic (use the same command for MNIST and CIFAR10)
+# The server is dataset agnostic (use the same command for MNIST and CIFAR10)
 python server.py --rounds 3 --min_num_clients 2 --sample_fraction 1.0
 ```
 
diff --git a/examples/quickstart-jax/README.md b/examples/quickstart-jax/README.md
index 9208337b86de..836adf558d88 100644
--- a/examples/quickstart-jax/README.md
+++ b/examples/quickstart-jax/README.md
@@ -52,7 +52,7 @@ pip install -r requirements.txt
 
 ## Run JAX Federated
 
-This JAX example is based on the [Linear Regression with JAX](https://coax.readthedocs.io/en/latest/examples/linear_regression/jax.html) tutorial and uses a sklearn dataset (generating a random dataset for a regression pronlem). Feel free to consult the tutorial if you want to get a better understanding of JAX. If you play around with the dataset, please keep in mind that the data samples are generated randomly depending on the settings being done while calling the dataset function. Please checkout out the [scikit-learn tutorial for further information](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html). The file `jax_training.py` contains all the steps that are described in the tutorial. It loads the train and test dataset and a linear regression model, trains the model with the training set, and evaluates the trained model on the test set.
+This JAX example is based on the [Linear Regression with JAX](https://coax.readthedocs.io/en/latest/examples/linear_regression/jax.html) tutorial and uses a sklearn dataset (generating a random dataset for a regression problem). Feel free to consult the tutorial if you want to get a better understanding of JAX. If you play around with the dataset, please keep in mind that the data samples are generated randomly depending on the settings being done while calling the dataset function. Please checkout out the [scikit-learn tutorial for further information](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html). The file `jax_training.py` contains all the steps that are described in the tutorial. It loads the train and test dataset and a linear regression model, trains the model with the training set, and evaluates the trained model on the test set.
 
 The only things we need are a simple Flower server (in `server.py`) and a Flower client (in `client.py`). The Flower client basically takes model and training code tells Flower how to call it.
 
diff --git a/examples/simulation-pytorch/README.md b/examples/simulation-pytorch/README.md
index d74b5be06a00..93f9e1acbac7 100644
--- a/examples/simulation-pytorch/README.md
+++ b/examples/simulation-pytorch/README.md
@@ -89,7 +89,7 @@ flower-simulation --client-app=sim:client --server-app=sim:server --num-supernod
 You can change the default resources assigned to each `ClientApp` by means of the `--backend-config` argument:
 
 ```bash
-# Tells the VCE to resever 2x CPUs and 25% of available VRAM for each ClientApp
+# Tells the VCE to reserve 2x CPUs and 25% of available VRAM for each ClientApp
 flower-simulation --client-app=sim:client --server-app=sim:server --num-supernodes=100 \
     --backend-config='{"client_resources": {"num_cpus":2, "num_gpus":0.25}}'
 ```
diff --git a/examples/simulation-tensorflow/README.md b/examples/simulation-tensorflow/README.md
index e3dca8e28906..917d7b34c7af 100644
--- a/examples/simulation-tensorflow/README.md
+++ b/examples/simulation-tensorflow/README.md
@@ -90,7 +90,7 @@ flower-simulation --client-app=sim:client --server-app=sim:server --num-supernod
 You can change the default resources assigned to each `ClientApp` using the `--backend-config` argument.
 
 ```bash
-# Tells the VCE to resever 2x CPUs and 25% of available VRAM for each ClientApp
+# Tells the VCE to reserve 2x CPUs and 25% of available VRAM for each ClientApp
 flower-simulation --client-app=sim:client --server-app=sim:server --num-supernodes=100 \
     --backend-config='{"client_resources": {"num_cpus":2, "num_gpus":0.25}}' --enable-tf-gpu-growth
 ```
diff --git a/src/py/flwr/common/message.py b/src/py/flwr/common/message.py
index ecb1615a3441..88cf750f1a94 100644
--- a/src/py/flwr/common/message.py
+++ b/src/py/flwr/common/message.py
@@ -156,14 +156,14 @@ def partition_id(self, value: int) -> None:
 
 @dataclass
 class Error:
-    """A dataclass that stores information about an error that ocurred.
+    """A dataclass that stores information about an error that occurred.
 
     Parameters
     ----------
     code : int
         An identifier for the error.
     reason : Optional[str]
-        A reason for why the error arised (e.g. an exception stack-trace)
+        A reason for why the error arose (e.g. an exception stack-trace)
     """
 
     _code: int
diff --git a/src/py/flwr/simulation/run_simulation.py b/src/py/flwr/simulation/run_simulation.py
index 71cd45fdd0a7..e44136cc360f 100644
--- a/src/py/flwr/simulation/run_simulation.py
+++ b/src/py/flwr/simulation/run_simulation.py
@@ -193,8 +193,8 @@ def _parse_args_run_simulation() -> argparse.ArgumentParser:
         help="Enables GPU growth on the main thread. This is desirable if you make "
         "use of a TensorFlow model on your `ServerApp` while having your `ClientApp` "
         "running on the same GPU. Without enabling this, you might encounter an "
-        "out-of-memory error becasue TensorFlow by default allocates all GPU memory."
-        "Read mor about how `tf.config.experimental.set_memory_growth()` works in "
+        "out-of-memory error because TensorFlow by default allocates all GPU memory."
+        "Read more about how `tf.config.experimental.set_memory_growth()` works in "
         "the TensorFlow documentation: https://www.tensorflow.org/api/stable.",
     )
     parser.add_argument(

From eea064e1e2ed106ba77223763fcb4cb37bcd31c2 Mon Sep 17 00:00:00 2001
From: mohammadnaseri <mohammad@flower.dev>
Date: Mon, 4 Mar 2024 17:24:22 +0000
Subject: [PATCH 092/102] Create differential privacy explainer (#3038)

---
 doc/source/_static/DP/CDP.png                 | Bin 0 -> 144170 bytes
 doc/source/_static/DP/LDP.png                 | Bin 0 -> 220186 bytes
 doc/source/_static/DP/clipping.png            | Bin 0 -> 67702 bytes
 doc/source/_static/DP/dp-intro.png            | Bin 0 -> 197931 bytes
 .../explanation-differential-privacy.rst      | 159 +++++++++++-------
 5 files changed, 99 insertions(+), 60 deletions(-)
 create mode 100644 doc/source/_static/DP/CDP.png
 create mode 100644 doc/source/_static/DP/LDP.png
 create mode 100644 doc/source/_static/DP/clipping.png
 create mode 100644 doc/source/_static/DP/dp-intro.png

diff --git a/doc/source/_static/DP/CDP.png b/doc/source/_static/DP/CDP.png
new file mode 100644
index 0000000000000000000000000000000000000000..13332fffd5c45aa8279bc1002bb7c8377d3d2761
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diff --git a/doc/source/explanation-differential-privacy.rst b/doc/source/explanation-differential-privacy.rst
index fcebd81983cc..69fd333f9b13 100644
--- a/doc/source/explanation-differential-privacy.rst
+++ b/doc/source/explanation-differential-privacy.rst
@@ -1,100 +1,139 @@
-Differential privacy
+Differential Privacy
 ====================
+The information in datasets like healthcare, financial transactions, user preferences, etc., is valuable and has the potential for scientific breakthroughs and provides important business insights.
+However, such data is also sensitive and there is a risk of compromising individual privacy.
 
-Flower provides differential privacy (DP) wrapper classes for the easy integration of the central DP guarantees provided by DP-FedAvg into training pipelines defined in any of the various ML frameworks that Flower is compatible with. 
+Traditional methods like anonymization alone would not work because of attacks like Re-identification and Data Linkage.
+That's where differential privacy comes in. It provides the possibility of analyzing data while ensuring the privacy of individuals.
 
-.. warning::
-  Please note that these components are still experimental; the correct configuration of DP for a specific task is still an unsolved problem.
 
-.. note::
-  The name DP-FedAvg is misleading since it can be applied on top of any FL algorithm that conforms to the general structure prescribed by the FedOpt family of algorithms.
+Differential Privacy
+--------------------
+Imagine two datasets that are identical except for a single record (for instance, Alice's data).
+Differential Privacy (DP) guarantees that any analysis (M), like calculating the average income, will produce nearly identical results for both datasets (O and O' would be similar).
+This preserves group patterns while obscuring individual details, ensuring the individual's information remains hidden in the crowd.
 
-DP-FedAvg
----------
+.. image:: ./_static/DP/dp-intro.png
+  :align: center
+  :width: 400
+  :alt: DP Intro
 
-DP-FedAvg, originally proposed by McMahan et al. [mcmahan]_ and extended by Andrew et al. [andrew]_, is essentially FedAvg with the following modifications. 
 
-* **Clipping** : The influence of each client's update is bounded by clipping it. This is achieved by enforcing a cap on the L2 norm of the update, scaling it down if needed.
-* **Noising** :  Gaussian noise, calibrated to the clipping threshold, is added to the average computed at the server.
+One of the most commonly used mechanisms to achieve DP is adding enough noise to the output of the analysis to mask the contribution of each individual in the data while preserving the overall accuracy of the analysis.
 
-The distribution of the update norm has been shown to vary from task-to-task and to evolve as training progresses. This variability is crucial in understanding its impact on differential privacy guarantees, emphasizing the need for an adaptive approach [andrew]_ that continuously adjusts the clipping threshold to track a prespecified quantile of the update norm distribution.
+Formal Definition
+~~~~~~~~~~~~~~~~~
+Differential Privacy (DP) provides statistical guarantees against the information an adversary can infer through the output of a randomized algorithm.
+It provides an unconditional upper bound on the influence of a single individual on the output of the algorithm by adding noise [1].
+A randomized mechanism
+M provides (:math:`\epsilon`, :math:`\delta`)-differential privacy if for any two neighboring databases, D :sub:`1` and D :sub:`2`, that differ in only a single record,
+and for all possible outputs S ⊆ Range(A):
 
-Simplifying Assumptions
-***********************
+.. math::
 
-We make (and attempt to enforce) a number of assumptions that must be satisfied to ensure that the training process actually realizes the :math:`(\epsilon, \delta)` guarantees the user has in mind when configuring the setup. 
+  \small
+  P[M(D_{1} \in A)] \leq e^{\delta} P[M(D_{2} \in A)] + \delta
 
-* **Fixed-size subsampling** :Fixed-size subsamples of the clients must be taken at each round, as opposed to variable-sized Poisson subsamples. 
-* **Unweighted averaging** : The contributions from all the clients must weighted equally in the aggregate to eliminate the requirement for the server to know in advance the sum of the weights of all clients available for selection.
-* **No client failures** : The set of available clients must stay constant across all rounds of training. In other words, clients cannot drop out or fail. 
 
-The first two are useful for eliminating a multitude of complications associated with calibrating the noise to the clipping threshold, while the third one is required to comply with the assumptions of the privacy analysis.
+The :math:`\epsilon` parameter, also known as the privacy budget, is a metric of privacy loss.
+It also controls the privacy-utility trade-off; lower :math:`\epsilon` values indicate higher levels of privacy but are likely to reduce utility as well.
+The :math:`\delta` parameter accounts for a small probability on which the upper bound :math:`\epsilon` does not hold.
+The amount of noise needed to achieve differential privacy is proportional to the sensitivity of the output, which measures the maximum change in the output due to the inclusion or removal of a single record.
 
-.. note::
-   These restrictions are in line with constraints imposed by Andrew et al. [andrew]_.
 
-Customizable Responsibility for Noise injection
-***********************************************
-In contrast to other implementations where the addition of noise is performed at the server, you can configure the site of noise injection to better match your threat model. We provide users with the flexibility to set up the training such that each client independently adds a small amount of noise to the clipped update, with the result that simply aggregating the noisy updates is equivalent to the explicit addition of noise to the non-noisy aggregate at the server. 
+Differential Privacy in Machine Learning
+----------------------------------------
+DP can be utilized in machine learning to preserve the privacy of the training data.
+Differentially private machine learning algorithms are designed in a way to prevent the algorithm to learn any specific information about any individual data points and subsequently prevent the model from revealing sensitive information.
+Depending on the stage at which noise is introduced, various methods exist for applying DP to machine learning algorithms.
+One approach involves adding noise to the training data (either to the features or labels), while another method entails injecting noise into the gradients of the loss function during model training.
+Additionally, such noise can be incorporated into the model's output.
 
+Differential Privacy in Federated Learning
+------------------------------------------
+Federated learning is a data minimization approach that allows multiple parties to collaboratively train a model without sharing their raw data.
+However, federated learning also introduces new privacy challenges. The model updates between parties and the central server can leak information about the local data.
+These leaks can be exploited by attacks such as membership inference and property inference attacks, or model inversion attacks.
 
-To be precise, if we let :math:`m` be the number of clients sampled each round and :math:`\sigma_\Delta` be the scale of the total Gaussian noise that needs to be added to the sum of the model updates, we can use simple maths to show that this is equivalent to each client adding noise with scale :math:`\sigma_\Delta/\sqrt{m}`.
+DP can play a crucial role in federated learning to provide privacy for the clients' data.
 
-Wrapper-based approach
-----------------------
+Depending on the granularity of privacy provision or the location of noise addition, different forms of DP exist in federated learning.
+In this explainer, we focus on two approaches of DP utilization in federated learning based on where the noise is added: at the server (also known as the center) or at the client (also known as the local).
 
-Introducing DP to an existing workload can be thought of as adding an extra layer of security around it. This inspired us to provide the additional server and client-side logic needed to make the training process differentially private as wrappers for instances of the :code:`Strategy` and :code:`NumPyClient` abstract classes respectively. This wrapper-based approach has the advantage of being easily composable with other wrappers that someone might contribute to the Flower library in the future, e.g., for secure aggregation. Using Inheritance instead can be tedious because that would require the creation of new sub- classes every time a new class implementing :code:`Strategy` or :code:`NumPyClient` is defined.
+- **Central Differential Privacy**: DP is applied by the server and the goal is to prevent the aggregated model from leaking information about each client's data.
 
-Server-side logic
-*****************
+- **Local Differential Privacy**: DP is applied on the client side before sending any information to the server and the goal is to prevent the updates that are sent to the server from leaking any information about the client's data.
 
-The first version of our solution was to define a decorator whose constructor accepted, among other things, a boolean-valued variable indicating whether adaptive clipping was to be enabled or not. We quickly realized that this would clutter its :code:`__init__()` function with variables corresponding to hyperparameters of adaptive clipping that would remain unused when it was disabled. A cleaner implementation could be achieved by splitting the functionality into two decorators, :code:`DPFedAvgFixed` and :code:`DPFedAvgAdaptive`, with the latter sub- classing the former. The constructors for both classes accept a boolean parameter :code:`server_side_noising`, which, as the name suggests, determines where noising is to be performed.
+Central Differential Privacy
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+In this approach, which is also known as user-level DP, the central server is responsible for adding noise to the globally aggregated parameters. It should be noted that trust in the server is required.
 
-DPFedAvgFixed
-:::::::::::::
+.. image:: ./_static/DP/CDP.png
+  :align: center
+  :width: 400
+  :alt: Central Differential Privacy
 
-The server-side capabilities required for the original version of DP-FedAvg, i.e., the one which performed fixed clipping, can be completely captured with the help of wrapper logic for just the following two methods of the :code:`Strategy` abstract class.
+While there are various ways to implement central DP in federated learning, we concentrate on the algorithms proposed by [2] and [3].
+The overall approach is to clip the model updates sent by the clients and add some amount of noise to the aggregated model.
+In each iteration, a random set of clients is chosen with a specific probability for training.
+Each client performs local training on its own data.
+The update of each client is then clipped by some value `S` (sensitivity `S`).
+This would limit the impact of any individual client which is crucial for privacy and often beneficial for robustness.
+A common approach to achieve this is by restricting the `L2` norm of the clients' model updates, ensuring that larger updates are scaled down to fit within the norm `S`.
 
-#. :code:`configure_fit()` : The config dictionary being sent by the wrapped :code:`Strategy` to each client needs to be augmented with an additional value equal to the clipping threshold (keyed under :code:`dpfedavg_clip_norm`) and, if :code:`server_side_noising=true`, another one equal to the scale of the Gaussian noise that needs to be added at the client (keyed under :code:`dpfedavg_noise_stddev`). This entails *post*-processing of the results returned by the wrappee's implementation of :code:`configure_fit()`.
-#. :code:`aggregate_fit()`: We check whether any of the sampled clients dropped out or failed to upload an update before the round timed out. In that case, we need to abort the current round, discarding any successful updates that were received, and move on to the next one. On the other hand, if all clients responded successfully, we must force the averaging of the updates to happen in an unweighted manner by intercepting the :code:`parameters` field of :code:`FitRes` for each received update and setting it to 1. Furthermore, if :code:`server_side_noising=true`, each update is perturbed with an amount of noise equal to what it would have been subjected to had client-side noising being enabled. This entails *pre*-processing of the arguments to this method before passing them on to the wrappee's implementation of :code:`aggregate_fit()`.
+.. image:: ./_static/DP/clipping.png
+  :align: center
+  :width: 300
+  :alt: clipping
 
-.. note::
-  We can't directly change the aggregation function of the wrapped strategy to force it to add noise to the aggregate, hence we simulate client-side noising to implement server-side noising. 
+Afterwards, the Gaussian mechanism is used to add noise in order to distort the sum of all clients' updates.
+The amount of noise is scaled to the sensitivity value to obtain a privacy guarantee.
+The Gaussian mechanism is used with a noise sampled from `N (0, σ²)` where `σ = ( noise_scale * S ) / (number of sampled clients)`.
 
-These changes have been put together into a class called :code:`DPFedAvgFixed`, whose constructor accepts the strategy being decorated, the clipping threshold and the number of clients sampled every round as compulsory arguments. The user is expected to specify the clipping threshold since the order of magnitude of the update norms is highly dependent on the model being trained and providing a default value would be misleading. The number of clients sampled at every round is required to calculate the amount of noise that must be added to each individual update, either by the server or the clients. 
+Clipping
+^^^^^^^^
 
-DPFedAvgAdaptive
-::::::::::::::::
+There are two forms of clipping commonly used in Central DP: Fixed Clipping and Adaptive Clipping.
 
-The additional functionality required to facilitate adaptive clipping has been provided in :code:`DPFedAvgAdaptive`, a subclass of :code:`DPFedAvgFixed`. It overrides the above-mentioned methods to do the following. 
+- **Fixed Clipping** : A predefined fix threshold is set for the magnitude of clients' updates. Any update exceeding this threshold is clipped back to the threshold value.
 
-#. :code:`configure_fit()` : It intercepts the config dict returned by :code:`super.configure_fit()` to add the key-value pair :code:`dpfedavg_adaptive_clip_enabled:True` to it, which the client interprets as an instruction to include an indicator bit (1 if update norm <= clipping threshold, 0 otherwise) in the results returned by it. 
+- **Adaptive Clipping** : The clipping threshold dynamically adjusts based on the observed update distribution [4]. It means that the clipping value is tuned during the rounds with respect to the quantile of the update norm distribution.
 
-#. :code:`aggregate_fit()` : It follows a call to :code:`super.aggregate_fit()` with one to :code:`__update_clip_norm__()`, a procedure which adjusts the clipping threshold on the basis of the indicator bits received from the sampled clients. 
+The choice between fixed and adaptive clipping depends on various factors such as privacy requirements, data distribution, model complexity, and others.
 
+Local Differential Privacy
+~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Client-side logic
-*****************
+In this approach, each client is responsible for performing DP.
+Local DP avoids the need for a fully trusted aggregator, but it should be noted that local DP leads to a decrease in accuracy but better privacy in comparison to central DP.
 
-The client-side capabilities required can be completely captured through wrapper logic for just the :code:`fit()` method of the :code:`NumPyClient` abstract class. To be precise, we need to *post-process* the update computed by the wrapped client to clip it, if necessary, to the threshold value supplied by the server as part of the config dictionary. In addition to this, it may need to perform some extra work if either (or both) of the following keys are also present in the dict.
+.. image:: ./_static/DP/LDP.png
+  :align: center
+  :width: 400
+  :alt: Local Differential Privacy
 
-* :code:`dpfedavg_noise_stddev` : Generate and add the specified amount of noise to the clipped update.
-* :code:`dpfedavg_adaptive_clip_enabled` : Augment the metrics dict in the :code:`FitRes` object being returned to the server with an indicator bit, calculated as described earlier.
 
+In this explainer, we focus on two forms of achieving Local DP:
 
-Performing the :math:`(\epsilon, \delta)` analysis
---------------------------------------------------
+- Each client adds noise to the local updates before sending them to the server. To achieve (:math:`\epsilon`, :math:`\delta`)-DP, considering the sensitivity of the local model to be ∆, Gaussian noise is applied with a noise scale of σ where:
 
-Assume you have trained for :math:`n` rounds with sampling fraction :math:`q` and noise multiplier :math:`z`. In order to calculate the :math:`\epsilon` value this would result in for a particular :math:`\delta`, the following script may be used. 
+.. math::
+    \small
+    \frac{∆ \times \sqrt{2 \times \log\left(\frac{1.25}{\delta}\right)}}{\epsilon}
 
-.. code-block:: python
 
-   import tensorflow_privacy as tfp
-   max_order = 32
-   orders = range(2, max_order + 1)
-   rdp = tfp.compute_rdp_sample_without_replacement(q, z, n, orders)
-   eps, _, _ = tfp.rdp_accountant.get_privacy_spent(rdp, target_delta=delta)
+- Each client adds noise to the gradients of the model during the local training (DP-SGD). More specifically, in this approach, gradients are clipped and an amount of calibrated noise is injected into the gradients.
 
-.. [mcmahan] McMahan et al. "Learning Differentially Private Recurrent Language Models." International Conference on Learning Representations (ICLR), 2017.
 
-.. [andrew] Andrew, Galen, et al. "Differentially Private Learning with Adaptive Clipping." Advances in Neural Information Processing Systems (NeurIPS), 2021.
+Please note that these two approaches are providing privacy at different levels.
+
+
+**References:**
+
+[1] Dwork et al. The Algorithmic Foundations of Differential Privacy.
+
+[2] McMahan et al. Learning Differentially Private Recurrent Language Models.
+
+[3] Geyer et al. Differentially Private Federated Learning: A Client Level Perspective.
+
+[4] Galen et al. Differentially Private Learning with Adaptive Clipping.

From d16807ac4a430216e6f377fec1f029c58f33bfc9 Mon Sep 17 00:00:00 2001
From: mohammadnaseri <mohammad@flower.dev>
Date: Mon, 4 Mar 2024 19:37:51 +0000
Subject: [PATCH 093/102] Create DP in Flower how-to guide (#3039)

Co-authored-by: Daniel J. Beutel <daniel@flower.ai>
---
 doc/source/_static/DP/clientsideCDP.png       | Bin 0 -> 260494 bytes
 doc/source/_static/DP/serversideCDP.png       | Bin 0 -> 300046 bytes
 .../how-to-use-differential-privacy.rst       |  95 ++++++++++++++++++
 doc/source/index.rst                          |   1 +
 4 files changed, 96 insertions(+)
 create mode 100644 doc/source/_static/DP/clientsideCDP.png
 create mode 100644 doc/source/_static/DP/serversideCDP.png
 create mode 100644 doc/source/how-to-use-differential-privacy.rst

diff --git a/doc/source/_static/DP/clientsideCDP.png b/doc/source/_static/DP/clientsideCDP.png
new file mode 100644
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diff --git a/doc/source/how-to-use-differential-privacy.rst b/doc/source/how-to-use-differential-privacy.rst
new file mode 100644
index 000000000000..0084eaa3611c
--- /dev/null
+++ b/doc/source/how-to-use-differential-privacy.rst
@@ -0,0 +1,95 @@
+Use Differential Privacy
+------------------------
+This guide explains how you can utilize differential privacy in the Flower framework. If you are not yet familiar with differential privacy, you can refer to :doc:`explanation-differential-privacy`.
+
+.. warning::
+
+   Differential Privacy in Flower is in a preview phase. If you plan to use these features in a production environment with sensitive data, feel free contact us to discuss your requirements and to receive guidance on how to best use these features.
+
+
+Central Differential Privacy
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+This approach consists of two seprate phases: clipping of the updates and adding noise to the aggregated model.
+For the clipping phase, Flower framework has made it possible to decide whether to perform clipping on the server side or the client side.
+
+- **Server-side Clipping**: This approach has the advantage of the server enforcing uniform clipping across all clients' updates and reducing the communication overhead for clipping values. However, it also has the disadvantage of increasing the computational load on the server due to the need to perform the clipping operation for all clients.
+- **Client-side Clipping**: This approach has the advantage of reducing the computational overhead on the server. However, it also has the disadvantage of lacking centralized control, as the server has less control over the clipping process.
+
+
+
+Server-side Clipping
+^^^^^^^^^^^^^^^^^^^^
+For central DP with server-side clipping, there are two :code:`Strategy` classes that act as wrappers around the actual :code:`Strategy` instance (for example, :code:`FedAvg`).
+The two wrapper classes are :code:`DifferentialPrivacyServerSideFixedClipping` and :code:`DifferentialPrivacyServerSideAdaptiveClipping` for fixed and adaptive clipping.
+
+.. image:: ./_static/DP/serversideCDP.png
+  :align: center
+  :width: 700
+  :alt: server side clipping
+
+
+The code sample below enables the :code:`FedAvg` strategy to use server-side fixed clipping using the :code:`DifferentialPrivacyServerSideFixedClipping` wrapper class.
+The same approach can be used with :code:`DifferentialPrivacyServerSideAdaptiveClipping` by adjusting the corresponding input parameters.
+
+.. code-block:: python
+
+  from flwr.server.strategy import DifferentialPrivacyClientSideFixedClipping
+
+  # Create the strategy
+  strategy = fl.server.strategy.FedAvg(...)
+
+  # Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping wrapper
+  dp_strategy = DifferentialPrivacyServerSideFixedClipping(
+      strategy,
+      cfg.noise_multiplier,
+      cfg.clipping_norm,
+      cfg.num_sampled_clients,
+  )
+
+
+
+Client-side Clipping
+^^^^^^^^^^^^^^^^^^^^
+For central DP with client-side clipping, the server sends the clipping value to selected clients on each round.
+Clients can use existing Flower :code:`Mods` to perform the clipping.
+Two mods are available for fixed and adaptive client-side clipping: :code:`fixedclipping_mod` and :code:`adaptiveclipping_mod` with corresponding server-side wrappers :code:`DifferentialPrivacyClientSideFixedClipping` and :code:`DifferentialPrivacyClientSideAdaptiveClipping`.
+
+.. image:: ./_static/DP/clientsideCDP.png
+  :align: center
+  :width: 800
+  :alt: client side clipping
+
+
+The code sample below enables the :code:`FedAvg` strategy to use differential privacy with client-side fixed clipping using both the :code:`DifferentialPrivacyClientSideFixedClipping` wrapper class and, on the client, :code:`fixedclipping_mod`:
+
+.. code-block:: python
+
+  from flwr.server.strategy import DifferentialPrivacyClientSideFixedClipping
+
+  # Create the strategy
+  strategy = fl.server.strategy.FedAvg(...)
+
+  # Wrap the strategy with the DifferentialPrivacyClientSideFixedClipping wrapper
+  dp_strategy = DifferentialPrivacyClientSideFixedClipping(
+      strategy,
+      cfg.noise_multiplier,
+      cfg.clipping_norm,
+      cfg.num_sampled_clients,
+  )
+
+In addition to the server-side strategy wrapper, the :code:`ClientApp` needs to configure the matching :code:`fixedclipping_mod` to perform the client-side clipping:
+
+.. code-block:: python
+
+  from flwr.client.mod import fixedclipping_mod
+
+  # Add fixedclipping_mod to the client-side mods
+  app = fl.client.ClientApp(
+      client_fn=client_fn,
+      mods=[
+          fixedclipping_mod,
+      ]
+  )
+
+
+Please note that the order of mods, especially those that modify parameters, is important when using multiple modifiers. Typically, differential privacy (DP) modifiers should be the last to operate on parameters.
diff --git a/doc/source/index.rst b/doc/source/index.rst
index ea52a9421b61..894155be03f1 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -93,6 +93,7 @@ Problem-oriented how-to guides show step-by-step how to achieve a specific goal.
    how-to-upgrade-to-flower-1.0
    how-to-use-built-in-mods
    how-to-run-flower-using-docker
+   how-to-use-differential-privacy
 
 .. toctree::
    :maxdepth: 1

From c05df2a839a6197dae40987eadbf1cb604eefdaa Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Tue, 5 Mar 2024 11:38:33 +0100
Subject: [PATCH 094/102] Make `flower-simulation` accept `ClientApp` and
 `ServerApp` objects (#3024)

Co-authored-by: Daniel J. Beutel <daniel@flower.ai>
---
 pyproject.toml                                |   2 +-
 src/py/flwr/server/app.py                     |   6 +-
 src/py/flwr/server/run_serverapp.py           |  23 +-
 .../server/superlink/fleet/vce/vce_api.py     |  18 +-
 .../superlink/fleet/vce/vce_api_test.py       |   8 +-
 src/py/flwr/simulation/__init__.py            |   7 +-
 src/py/flwr/simulation/run_simulation.py      | 344 +++++++++++++++---
 7 files changed, 324 insertions(+), 84 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 72d001e5914d..20f2e2632615 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -58,7 +58,7 @@ flower-fleet-api = "flwr.server:run_fleet_api"
 flower-superlink = "flwr.server:run_superlink"
 flower-client-app = "flwr.client:run_client_app"
 flower-server-app = "flwr.server:run_server_app"
-flower-simulation = "flwr.simulation:run_simulation"
+flower-simulation = "flwr.simulation:run_simulation_from_cli"
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/src/py/flwr/server/app.py b/src/py/flwr/server/app.py
index 788ebeb8a454..01b1f622212f 100644
--- a/src/py/flwr/server/app.py
+++ b/src/py/flwr/server/app.py
@@ -362,7 +362,7 @@ def run_superlink() -> None:
         f_stop = asyncio.Event()  # Does nothing
         _run_fleet_api_vce(
             num_supernodes=args.num_supernodes,
-            client_app_module_name=args.client_app,
+            client_app_attr=args.client_app,
             backend_name=args.backend,
             backend_config_json_stream=args.backend_config,
             working_dir=args.dir,
@@ -438,7 +438,7 @@ def _run_fleet_api_grpc_rere(
 # pylint: disable=too-many-arguments
 def _run_fleet_api_vce(
     num_supernodes: int,
-    client_app_module_name: str,
+    client_app_attr: str,
     backend_name: str,
     backend_config_json_stream: str,
     working_dir: str,
@@ -449,7 +449,7 @@ def _run_fleet_api_vce(
 
     start_vce(
         num_supernodes=num_supernodes,
-        client_app_module_name=client_app_module_name,
+        client_app_attr=client_app_attr,
         backend_name=backend_name,
         backend_config_json_stream=backend_config_json_stream,
         state_factory=state_factory,
diff --git a/src/py/flwr/server/run_serverapp.py b/src/py/flwr/server/run_serverapp.py
index d4f21cbf20dc..4431397a28e6 100644
--- a/src/py/flwr/server/run_serverapp.py
+++ b/src/py/flwr/server/run_serverapp.py
@@ -16,7 +16,6 @@
 
 
 import argparse
-import asyncio
 import sys
 from logging import DEBUG, WARN
 from pathlib import Path
@@ -30,17 +29,27 @@
 
 
 def run(
-    server_app_attr: str,
     driver: Driver,
     server_app_dir: str,
-    stop_event: Optional[asyncio.Event] = None,
+    server_app_attr: Optional[str] = None,
+    loaded_server_app: Optional[ServerApp] = None,
 ) -> None:
     """Run ServerApp with a given Driver."""
+    if not (server_app_attr is None) ^ (loaded_server_app is None):
+        raise ValueError(
+            "Either `server_app_attr` or `loaded_server_app` should be set "
+            "but not both. "
+        )
+
     if server_app_dir is not None:
         sys.path.insert(0, server_app_dir)
 
+    # Load ServerApp if needed
     def _load() -> ServerApp:
-        server_app: ServerApp = load_server_app(server_app_attr)
+        if server_app_attr:
+            server_app: ServerApp = load_server_app(server_app_attr)
+        if loaded_server_app:
+            server_app = loaded_server_app
         return server_app
 
     server_app = _load()
@@ -52,10 +61,6 @@ def _load() -> ServerApp:
     server_app(driver=driver, context=context)
 
     log(DEBUG, "ServerApp finished running.")
-    # Upon completion, trigger stop event if one was passed
-    if stop_event is not None:
-        log(DEBUG, "Triggering stop event.")
-        stop_event.set()
 
 
 def run_server_app() -> None:
@@ -117,7 +122,7 @@ def run_server_app() -> None:
     )
 
     # Run the Server App with the Driver
-    run(server_app_attr, driver, server_app_dir)
+    run(driver=driver, server_app_dir=server_app_dir, server_app_attr=server_app_attr)
 
     # Clean up
     driver.__del__()  # pylint: disable=unnecessary-dunder-call
diff --git a/src/py/flwr/server/superlink/fleet/vce/vce_api.py b/src/py/flwr/server/superlink/fleet/vce/vce_api.py
index ad858cbb9979..d42379960a63 100644
--- a/src/py/flwr/server/superlink/fleet/vce/vce_api.py
+++ b/src/py/flwr/server/superlink/fleet/vce/vce_api.py
@@ -219,16 +219,23 @@ async def run(
 
 # pylint: disable=too-many-arguments,unused-argument,too-many-locals
 def start_vce(
-    client_app_module_name: str,
     backend_name: str,
     backend_config_json_stream: str,
     working_dir: str,
     f_stop: asyncio.Event,
+    client_app: Optional[ClientApp] = None,
+    client_app_attr: Optional[str] = None,
     num_supernodes: Optional[int] = None,
     state_factory: Optional[StateFactory] = None,
     existing_nodes_mapping: Optional[NodeToPartitionMapping] = None,
 ) -> None:
     """Start Fleet API with the Simulation Engine."""
+    if client_app_attr is not None and client_app is not None:
+        raise ValueError(
+            "Both `client_app_attr` and `client_app` are provided, "
+            "but only one is allowed."
+        )
+
     if num_supernodes is not None and existing_nodes_mapping is not None:
         raise ValueError(
             "Both `num_supernodes` and `existing_nodes_mapping` are provided, "
@@ -292,10 +299,15 @@ def backend_fn() -> Backend:
         """Instantiate a Backend."""
         return backend_type(backend_config, work_dir=working_dir)
 
-    log(INFO, "client_app_module_name = %s", client_app_module_name)
+    log(INFO, "client_app_attr = %s", client_app_attr)
 
+    # Load ClientApp if needed
     def _load() -> ClientApp:
-        app: ClientApp = load_client_app(client_app_module_name)
+
+        if client_app_attr:
+            app: ClientApp = load_client_app(client_app_attr)
+        if client_app:
+            app = client_app
         return app
 
     app_fn = _load
diff --git a/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py b/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py
index ea2de2e636ba..16cb45c12624 100644
--- a/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py
+++ b/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py
@@ -132,7 +132,7 @@ def _autoresolve_working_dir(rel_client_app_dir: str = "backend") -> str:
 # pylint: disable=too-many-arguments
 def start_and_shutdown(
     backend: str = "ray",
-    clientapp_module: str = "raybackend_test:client_app",
+    client_app_attr: str = "raybackend_test:client_app",
     working_dir: str = "",
     num_supernodes: Optional[int] = None,
     state_factory: Optional[StateFactory] = None,
@@ -162,7 +162,7 @@ def start_and_shutdown(
 
     start_vce(
         num_supernodes=num_supernodes,
-        client_app_module_name=clientapp_module,
+        client_app_attr=client_app_attr,
         backend_name=backend,
         backend_config_json_stream=backend_config,
         state_factory=state_factory,
@@ -183,7 +183,7 @@ def test_erroneous_no_supernodes_client_mapping(self) -> None:
         with self.assertRaises(ValueError):
             start_and_shutdown(duration=2)
 
-    def test_erroneous_clientapp_module_name(self) -> None:
+    def test_erroneous_client_app_attr(self) -> None:
         """Tests attempt to load a ClientApp that can't be found."""
         num_messages = 7
         num_nodes = 59
@@ -193,7 +193,7 @@ def test_erroneous_clientapp_module_name(self) -> None:
         )
         with self.assertRaises(RuntimeError):
             start_and_shutdown(
-                clientapp_module="totally_fictitious_app:client",
+                client_app_attr="totally_fictitious_app:client",
                 state_factory=state_factory,
                 nodes_mapping=nodes_mapping,
             )
diff --git a/src/py/flwr/simulation/__init__.py b/src/py/flwr/simulation/__init__.py
index b283de70c588..d36d9977d1c5 100644
--- a/src/py/flwr/simulation/__init__.py
+++ b/src/py/flwr/simulation/__init__.py
@@ -17,7 +17,7 @@
 
 import importlib
 
-from flwr.simulation.run_simulation import run_simulation
+from flwr.simulation.run_simulation import run_simulation, run_simulation_from_cli
 
 is_ray_installed = importlib.util.find_spec("ray") is not None
 
@@ -36,7 +36,4 @@ def start_simulation(*args, **kwargs):  # type: ignore
         raise ImportError(RAY_IMPORT_ERROR)
 
 
-__all__ = [
-    "start_simulation",
-    "run_simulation",
-]
+__all__ = ["start_simulation", "run_simulation_from_cli", "run_simulation"]
diff --git a/src/py/flwr/simulation/run_simulation.py b/src/py/flwr/simulation/run_simulation.py
index e44136cc360f..cb68221ea583 100644
--- a/src/py/flwr/simulation/run_simulation.py
+++ b/src/py/flwr/simulation/run_simulation.py
@@ -17,39 +17,153 @@
 import argparse
 import asyncio
 import json
+import logging
 import threading
 import traceback
-from logging import ERROR, INFO, WARNING
+from logging import DEBUG, ERROR, INFO, WARNING
 from time import sleep
-from typing import Any, Callable
+from typing import Dict, Optional
 
 import grpc
 
+from flwr.client import ClientApp
 from flwr.common import EventType, event, log
+from flwr.common.typing import ConfigsRecordValues
 from flwr.server.driver.driver import Driver
 from flwr.server.run_serverapp import run
+from flwr.server.server_app import ServerApp
 from flwr.server.superlink.driver.driver_grpc import run_driver_api_grpc
 from flwr.server.superlink.fleet import vce
 from flwr.server.superlink.state import StateFactory
-from flwr.simulation.ray_transport.utils import enable_tf_gpu_growth
+from flwr.simulation.ray_transport.utils import (
+    enable_tf_gpu_growth as enable_gpu_growth,
+)
 
 
+# Entry point from CLI
+def run_simulation_from_cli() -> None:
+    """Run Simulation Engine from the CLI."""
+    args = _parse_args_run_simulation().parse_args()
+
+    # Load JSON config
+    backend_config_dict = json.loads(args.backend_config)
+
+    _run_simulation(
+        server_app_attr=args.server_app,
+        client_app_attr=args.client_app,
+        num_supernodes=args.num_supernodes,
+        backend_name=args.backend,
+        backend_config=backend_config_dict,
+        working_dir=args.dir,
+        driver_api_address=args.driver_api_address,
+        enable_tf_gpu_growth=args.enable_tf_gpu_growth,
+        verbose_logging=args.verbose,
+    )
+
+
+# Entry point from Python session (script or notebook)
+# pylint: disable=too-many-arguments
+def run_simulation(
+    server_app: ServerApp,
+    client_app: ClientApp,
+    num_supernodes: int,
+    backend_name: str = "ray",
+    backend_config: Optional[Dict[str, ConfigsRecordValues]] = None,
+    enable_tf_gpu_growth: bool = False,
+    verbose_logging: bool = False,
+) -> None:
+    r"""Run a Flower App using the Simulation Engine.
+
+    Parameters
+    ----------
+    server_app : ServerApp
+        The `ServerApp` to be executed. It will send messages to different `ClientApp`
+        instances running on different (virtual) SuperNodes.
+
+    client_app : ClientApp
+        The `ClientApp` to be executed by each of the SuperNodes. It will receive
+        messages sent by the `ServerApp`.
+
+    num_supernodes : int
+        Number of nodes that run a ClientApp. They can be sampled by a
+        Driver in the ServerApp and receive a Message describing what the ClientApp
+        should perform.
+
+    backend_name : str (default: ray)
+        A simulation backend that runs `ClientApp`s.
+
+    backend_config : Optional[Dict[str, ConfigsRecordValues]]
+        'A dictionary, e.g {"<keyA>": <value>, "<keyB>": <value>} to configure a
+        backend. Values supported in <value> are those included by
+        `flwr.common.typing.ConfigsRecordValues`.
+
+    enable_tf_gpu_growth : bool (default: False)
+        A boolean to indicate whether to enable GPU growth on the main thread. This is
+        desirable if you make use of a TensorFlow model on your `ServerApp` while
+        having your `ClientApp` running on the same GPU. Without enabling this, you
+        might encounter an out-of-memory error because TensorFlow, by default, allocates
+        all GPU memory. Read more about how `tf.config.experimental.set_memory_growth()`
+        works in the TensorFlow documentation: https://www.tensorflow.org/api/stable.
+
+    verbose_logging : bool (default: False)
+        When diabled, only INFO, WARNING and ERROR log messages will be shown. If
+        enabled, DEBUG-level logs will be displayed.
+    """
+    _run_simulation(
+        num_supernodes=num_supernodes,
+        client_app=client_app,
+        server_app=server_app,
+        backend_name=backend_name,
+        backend_config=backend_config,
+        enable_tf_gpu_growth=enable_tf_gpu_growth,
+        verbose_logging=verbose_logging,
+    )
+
+
+# pylint: disable=too-many-arguments
 def run_serverapp_th(
-    server_app_attr: str,
+    server_app_attr: Optional[str],
+    server_app: Optional[ServerApp],
     driver: Driver,
     server_app_dir: str,
     f_stop: asyncio.Event,
+    enable_tf_gpu_growth: bool,
     delay_launch: int = 3,
 ) -> threading.Thread:
     """Run SeverApp in a thread."""
+
+    def server_th_with_start_checks(  # type: ignore
+        tf_gpu_growth: bool, stop_event: asyncio.Event, **kwargs
+    ) -> None:
+        """Run SeverApp, after check if GPU memory grouwth has to be set.
+
+        Upon exception, trigger stop event for Simulation Engine.
+        """
+        try:
+            if tf_gpu_growth:
+                log(INFO, "Enabling GPU growth for Tensorflow on the main thread.")
+                enable_gpu_growth()
+
+            # Run ServerApp
+            run(**kwargs)
+        except Exception as ex:  # pylint: disable=broad-exception-caught
+            log(ERROR, "ServerApp thread raised an exception: %s", ex)
+            log(ERROR, traceback.format_exc())
+        finally:
+            log(DEBUG, "ServerApp finished running.")
+            # Upon completion, trigger stop event if one was passed
+            if stop_event is not None:
+                stop_event.set()
+                log(WARNING, "Triggered stop event for Simulation Engine.")
+
     serverapp_th = threading.Thread(
-        target=run,
+        target=server_th_with_start_checks,
+        args=(enable_tf_gpu_growth, f_stop),
         kwargs={
             "server_app_attr": server_app_attr,
+            "loaded_server_app": server_app,
             "driver": driver,
             "server_app_dir": server_app_dir,
-            "stop_event": f_stop,  # will be set when `run()` finishes
-            # will trigger the shutdown of the Simulation Engine
         },
     )
     sleep(delay_launch)
@@ -57,52 +171,31 @@ def run_serverapp_th(
     return serverapp_th
 
 
-def get_thread_exception_hook(stop_event: asyncio.Event) -> Callable[[Any], None]:
-    """Return a callback for when the ServerApp thread raises an exception."""
-
-    def execepthook(args: Any) -> None:
-        """Upon exception raised, log exception and trigger stop event."""
-        # log
-        log(
-            ERROR,
-            "The ServerApp thread triggered exception (%s): %s",
-            args.exc_type,
-            args.exc_value,
-        )
-        log(ERROR, traceback.format_exc())
-        # Set stop event
-        stop_event.set()
-        log(WARNING, "Triggered stop event for Simulation Engine.")
-
-    return execepthook
-
-
-def run_simulation() -> None:
-    """Run Simulation Engine."""
-    args = _parse_args_run_simulation().parse_args()
-
-    # Load JSON config
-    backend_config_dict = json.loads(args.backend_config)
-
-    # Enable GPU memory growth (relevant only for TF)
-    if args.enable_tf_gpu_growth:
-        log(INFO, "Enabling GPU growth for Tensorflow on the main thread.")
-        enable_tf_gpu_growth()
-        # Check that Backend config has also enabled using GPU growth
-        use_tf = backend_config_dict.get("tensorflow", False)
-        if not use_tf:
-            log(WARNING, "Enabling GPU growth for your backend.")
-            backend_config_dict["tensorflow"] = True
-
-    # Convert back to JSON stream
-    backend_config = json.dumps(backend_config_dict)
+# pylint: disable=too-many-locals
+def _main_loop(
+    num_supernodes: int,
+    backend_name: str,
+    backend_config_stream: str,
+    driver_api_address: str,
+    working_dir: str,
+    enable_tf_gpu_growth: bool,
+    client_app: Optional[ClientApp] = None,
+    client_app_attr: Optional[str] = None,
+    server_app: Optional[ServerApp] = None,
+    server_app_attr: Optional[str] = None,
+) -> None:
+    """Launch SuperLink with Simulation Engine, then ServerApp on a separate thread.
 
+    Everything runs on the main thread or a separate one, depening on whether the main
+    thread already contains a running Asyncio event loop. This is the case if running
+    the Simulation Engine on a Jupyter/Colab notebook.
+    """
     # Initialize StateFactory
     state_factory = StateFactory(":flwr-in-memory-state:")
 
     # Start Driver API
     driver_server: grpc.Server = run_driver_api_grpc(
-        address=args.driver_api_address,
+        address=driver_api_address,
         state_factory=state_factory,
         certificates=None,
     )
@@ -112,35 +205,39 @@ def run_simulation() -> None:
     try:
         # Initialize Driver
         driver = Driver(
-            driver_service_address=args.driver_api_address,
+            driver_service_address=driver_api_address,
             root_certificates=None,
         )
 
         # Get and run ServerApp thread
-        serverapp_th = run_serverapp_th(args.server_app, driver, args.dir, f_stop)
-        # Setup an exception hook
-        threading.excepthook = get_thread_exception_hook(f_stop)
+        serverapp_th = run_serverapp_th(
+            server_app_attr=server_app_attr,
+            server_app=server_app,
+            driver=driver,
+            server_app_dir=working_dir,
+            f_stop=f_stop,
+            enable_tf_gpu_growth=enable_tf_gpu_growth,
+        )
 
         # SuperLink with Simulation Engine
         event(EventType.RUN_SUPERLINK_ENTER)
         vce.start_vce(
-            num_supernodes=args.num_supernodes,
-            client_app_module_name=args.client_app,
-            backend_name=args.backend,
-            backend_config_json_stream=backend_config,
-            working_dir=args.dir,
+            num_supernodes=num_supernodes,
+            client_app_attr=client_app_attr,
+            client_app=client_app,
+            backend_name=backend_name,
+            backend_config_json_stream=backend_config_stream,
+            working_dir=working_dir,
             state_factory=state_factory,
             f_stop=f_stop,
         )
 
     except Exception as ex:
-
-        log(ERROR, "An exception occurred: %s", ex)
+        log(ERROR, "An exception occurred !! %s", ex)
         log(ERROR, traceback.format_exc())
         raise RuntimeError("An error was encountered. Ending simulation.") from ex
 
     finally:
-
         # Stop Driver
         driver_server.stop(grace=0)
         del driver
@@ -154,6 +251,128 @@ def run_simulation() -> None:
     log(INFO, "Stopping Simulation Engine now.")
 
 
+# pylint: disable=too-many-arguments,too-many-locals
+def _run_simulation(
+    num_supernodes: int,
+    client_app: Optional[ClientApp] = None,
+    server_app: Optional[ServerApp] = None,
+    backend_name: str = "ray",
+    backend_config: Optional[Dict[str, ConfigsRecordValues]] = None,
+    client_app_attr: Optional[str] = None,
+    server_app_attr: Optional[str] = None,
+    working_dir: str = "",
+    driver_api_address: str = "0.0.0.0:9091",
+    enable_tf_gpu_growth: bool = False,
+    verbose_logging: bool = False,
+) -> None:
+    r"""Launch the Simulation Engine.
+
+    Parameters
+    ----------
+    num_supernodes : int
+        Number of nodes that run a ClientApp. They can be sampled by a
+        Driver in the ServerApp and receive a Message describing what the ClientApp
+        should perform.
+
+    client_app : Optional[ClientApp]
+        The `ClientApp` to be executed by each of the `SuperNodes`. It will receive
+        messages sent by the `ServerApp`.
+
+    server_app : Optional[ServerApp]
+        The `ServerApp` to be executed.
+
+    backend_name : str (default: ray)
+        A simulation backend that runs `ClientApp`s.
+
+    backend_config : Optional[Dict[str, ConfigsRecordValues]]
+        'A dictionary, e.g {"<keyA>":<value>, "<keyB>":<value>} to configure a
+        backend. Values supported in <value> are those included by
+        `flwr.common.typing.ConfigsRecordValues`.
+
+    client_app_attr : str
+        A path to a `ClientApp` module to be loaded: For example: `client:app` or
+        `project.package.module:wrapper.app`."
+
+    server_app_attr : str
+        A path to a `ServerApp` module to be loaded: For example: `server:app` or
+        `project.package.module:wrapper.app`."
+
+    working_dir : str
+        Add specified directory to the PYTHONPATH and load `ClientApp` from there.
+        (Default: current working directory.)
+
+    driver_api_address : str (default: "0.0.0.0:9091")
+        Driver API (gRPC) server address (IPv4, IPv6, or a domain name)
+
+    enable_tf_gpu_growth : bool (default: False)
+        A boolean to indicate whether to enable GPU growth on the main thread. This is
+        desirable if you make use of a TensorFlow model on your `ServerApp` while
+        having your `ClientApp` running on the same GPU. Without enabling this, you
+        might encounter an out-of-memory error becasue TensorFlow by default allocates
+        all GPU memory. Read mor about how `tf.config.experimental.set_memory_growth()`
+        works in the TensorFlow documentation: https://www.tensorflow.org/api/stable.
+
+    verbose_logging : bool (default: False)
+        When diabled, only INFO, WARNING and ERROR log messages will be shown. If
+        enabled, DEBUG-level logs will be displayed.
+    """
+    # Set logging level
+    if not verbose_logging:
+        logger = logging.getLogger("flwr")
+        logger.setLevel(INFO)
+
+    if backend_config is None:
+        backend_config = {}
+
+    if enable_tf_gpu_growth:
+        # Check that Backend config has also enabled using GPU growth
+        use_tf = backend_config.get("tensorflow", False)
+        if not use_tf:
+            log(WARNING, "Enabling GPU growth for your backend.")
+            backend_config["tensorflow"] = True
+
+    # Convert config to original JSON-stream format
+    backend_config_stream = json.dumps(backend_config)
+
+    simulation_engine_th = None
+    args = (
+        num_supernodes,
+        backend_name,
+        backend_config_stream,
+        driver_api_address,
+        working_dir,
+        enable_tf_gpu_growth,
+        client_app,
+        client_app_attr,
+        server_app,
+        server_app_attr,
+    )
+    # Detect if there is an Asyncio event loop already running.
+    # If yes, run everything on a separate thread. In environmnets
+    # like Jupyter/Colab notebooks, there is an event loop present.
+    run_in_thread = False
+    try:
+        _ = (
+            asyncio.get_running_loop()
+        )  # Raises RuntimeError if no event loop is present
+        log(DEBUG, "Asyncio event loop already running.")
+
+        run_in_thread = True
+
+    except RuntimeError:
+        log(DEBUG, "No asyncio event loop runnig")
+
+    finally:
+        if run_in_thread:
+            log(DEBUG, "Starting Simulation Engine on a new thread.")
+            simulation_engine_th = threading.Thread(target=_main_loop, args=args)
+            simulation_engine_th.start()
+            simulation_engine_th.join()
+        else:
+            log(DEBUG, "Starting Simulation Engine on the main thread.")
+            _main_loop(*args)
+
+
 def _parse_args_run_simulation() -> argparse.ArgumentParser:
     """Parse flower-simulation command line arguments."""
     parser = argparse.ArgumentParser(
@@ -213,4 +432,11 @@ def _parse_args_run_simulation() -> argparse.ArgumentParser:
         " Default: current working directory.",
     )
 
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="When unset, only INFO, WARNING and ERROR log messages will be shown. "
+        "If set, DEBUG-level logs will be displayed. ",
+    )
+
     return parser

From a62d4fa8ebcca067f0c372a9260d1500a28f7ad2 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Tue, 5 Mar 2024 11:53:26 +0100
Subject: [PATCH 095/102] Improve Message tests (#3040)

---
 src/py/flwr/common/__init__.py     |   2 +
 src/py/flwr/common/message_test.py | 109 +++++++++++++++++++++++++++++
 src/py/flwr/common/serde_test.py   |  93 +++++++++---------------
 3 files changed, 145 insertions(+), 59 deletions(-)
 create mode 100644 src/py/flwr/common/message_test.py

diff --git a/src/py/flwr/common/__init__.py b/src/py/flwr/common/__init__.py
index 319c15900217..0b2d3f17c47c 100644
--- a/src/py/flwr/common/__init__.py
+++ b/src/py/flwr/common/__init__.py
@@ -20,6 +20,7 @@
 from .grpc import GRPC_MAX_MESSAGE_LENGTH
 from .logger import configure as configure
 from .logger import log as log
+from .message import Error as Error
 from .message import Message as Message
 from .message import Metadata as Metadata
 from .parameter import bytes_to_ndarray as bytes_to_ndarray
@@ -74,6 +75,7 @@
     "EventType",
     "FitIns",
     "FitRes",
+    "Error",
     "GetParametersIns",
     "GetParametersRes",
     "GetPropertiesIns",
diff --git a/src/py/flwr/common/message_test.py b/src/py/flwr/common/message_test.py
new file mode 100644
index 000000000000..ba628bb3235a
--- /dev/null
+++ b/src/py/flwr/common/message_test.py
@@ -0,0 +1,109 @@
+# Copyright 2020 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Message tests."""
+
+
+from contextlib import ExitStack
+from typing import Any, Callable
+
+import pytest
+
+# pylint: enable=E0611
+from . import RecordSet
+from .message import Error, Message
+from .serde_test import RecordMaker
+
+
+@pytest.mark.parametrize(
+    "content_fn, error_fn, context",
+    [
+        (
+            lambda maker: maker.recordset(1, 1, 1),
+            None,
+            None,
+        ),  # check when only content is set
+        (None, lambda code: Error(code=code), None),  # check when only error is set
+        (
+            lambda maker: maker.recordset(1, 1, 1),
+            lambda code: Error(code=code),
+            pytest.raises(ValueError),
+        ),  # check when both are set (ERROR)
+        (None, None, pytest.raises(ValueError)),  # check when neither is set (ERROR)
+    ],
+)
+def test_message_creation(
+    content_fn: Callable[
+        [
+            RecordMaker,
+        ],
+        RecordSet,
+    ],
+    error_fn: Callable[[int], Error],
+    context: Any,
+) -> None:
+    """Test Message creation attempting to pass content and/or error."""
+    # Prepare
+    maker = RecordMaker(state=2)
+    metadata = maker.metadata()
+
+    with ExitStack() as stack:
+        if context:
+            stack.enter_context(context)
+
+        _ = Message(
+            metadata=metadata,
+            content=None if content_fn is None else content_fn(maker),
+            error=None if error_fn is None else error_fn(0),
+        )
+
+
+def create_message_with_content() -> Message:
+    """Create a Message with content."""
+    maker = RecordMaker(state=2)
+    metadata = maker.metadata()
+    return Message(metadata=metadata, content=RecordSet())
+
+
+def create_message_with_error() -> Message:
+    """Create a Message with error."""
+    maker = RecordMaker(state=2)
+    metadata = maker.metadata()
+    return Message(metadata=metadata, error=Error(code=1))
+
+
+@pytest.mark.parametrize(
+    "message_creation_fn",
+    [
+        create_message_with_content,
+        create_message_with_error,
+    ],
+)
+def test_altering_message(
+    message_creation_fn: Callable[
+        [],
+        Message,
+    ],
+) -> None:
+    """Test that a message with content doesn't allow setting an error.
+
+    And viceversa.
+    """
+    message = message_creation_fn()
+
+    with pytest.raises(ValueError):
+        if message.has_content():
+            message.error = Error(code=123)
+        if message.has_error():
+            message.content = RecordSet()
diff --git a/src/py/flwr/common/serde_test.py b/src/py/flwr/common/serde_test.py
index 509ce2700948..8596e5d2f330 100644
--- a/src/py/flwr/common/serde_test.py
+++ b/src/py/flwr/common/serde_test.py
@@ -16,7 +16,6 @@
 
 import random
 import string
-from contextlib import ExitStack
 from typing import Any, Callable, Optional, OrderedDict, Type, TypeVar, Union, cast
 
 import pytest
@@ -301,20 +300,13 @@ def test_recordset_serialization_deserialization() -> None:
 
 
 @pytest.mark.parametrize(
-    "content_fn, error_fn, context",
+    "content_fn, error_fn",
     [
         (
             lambda maker: maker.recordset(1, 1, 1),
             None,
-            None,
         ),  # check when only content is set
-        (None, lambda code: Error(code=code), None),  # check when only error is set
-        (
-            lambda maker: maker.recordset(1, 1, 1),
-            lambda code: Error(code=code),
-            pytest.raises(ValueError),
-        ),  # check when both are set (ERROR)
-        (None, None, pytest.raises(ValueError)),  # check when neither is set (ERROR)
+        (None, lambda code: Error(code=code)),  # check when only error is set
     ],
 )
 def test_message_to_and_from_taskins(
@@ -325,7 +317,6 @@ def test_message_to_and_from_taskins(
         RecordSet,
     ],
     error_fn: Callable[[int], Error],
-    context: Any,
 ) -> None:
     """Test Message to and from TaskIns."""
     # Prepare
@@ -335,44 +326,33 @@ def test_message_to_and_from_taskins(
     # pylint: disable-next=protected-access
     metadata._src_node_id = 0  # Assume driver node
 
-    with ExitStack() as stack:
-        if context:
-            stack.enter_context(context)
-
-        original = Message(
-            metadata=metadata,
-            content=None if content_fn is None else content_fn(maker),
-            error=None if error_fn is None else error_fn(0),
-        )
+    original = Message(
+        metadata=metadata,
+        content=None if content_fn is None else content_fn(maker),
+        error=None if error_fn is None else error_fn(0),
+    )
 
-        # Execute
-        taskins = message_to_taskins(original)
-        taskins.task_id = metadata.message_id
-        deserialized = message_from_taskins(taskins)
+    # Execute
+    taskins = message_to_taskins(original)
+    taskins.task_id = metadata.message_id
+    deserialized = message_from_taskins(taskins)
 
-        # Assert
-        if original.has_content():
-            assert original.content == deserialized.content
-        if original.has_error():
-            assert original.error == deserialized.error
-        assert metadata == deserialized.metadata
+    # Assert
+    if original.has_content():
+        assert original.content == deserialized.content
+    if original.has_error():
+        assert original.error == deserialized.error
+    assert metadata == deserialized.metadata
 
 
 @pytest.mark.parametrize(
-    "content_fn, error_fn, context",
+    "content_fn, error_fn",
     [
         (
             lambda maker: maker.recordset(1, 1, 1),
             None,
-            None,
         ),  # check when only content is set
-        (None, lambda code: Error(code=code), None),  # check when only error is set
-        (
-            lambda maker: maker.recordset(1, 1, 1),
-            lambda code: Error(code=code),
-            pytest.raises(ValueError),
-        ),  # check when both are set (ERROR)
-        (None, None, pytest.raises(ValueError)),  # check when neither is set (ERROR)
+        (None, lambda code: Error(code=code)),  # check when only error is set
     ],
 )
 def test_message_to_and_from_taskres(
@@ -383,7 +363,6 @@ def test_message_to_and_from_taskres(
         RecordSet,
     ],
     error_fn: Callable[[int], Error],
-    context: Any,
 ) -> None:
     """Test Message to and from TaskRes."""
     # Prepare
@@ -391,24 +370,20 @@ def test_message_to_and_from_taskres(
     metadata = maker.metadata()
     metadata.dst_node_id = 0  # Assume driver node
 
-    with ExitStack() as stack:
-        if context:
-            stack.enter_context(context)
-
-        original = Message(
-            metadata=metadata,
-            content=None if content_fn is None else content_fn(maker),
-            error=None if error_fn is None else error_fn(0),
-        )
+    original = Message(
+        metadata=metadata,
+        content=None if content_fn is None else content_fn(maker),
+        error=None if error_fn is None else error_fn(0),
+    )
 
-        # Execute
-        taskres = message_to_taskres(original)
-        taskres.task_id = metadata.message_id
-        deserialized = message_from_taskres(taskres)
+    # Execute
+    taskres = message_to_taskres(original)
+    taskres.task_id = metadata.message_id
+    deserialized = message_from_taskres(taskres)
 
-        # Assert
-        if original.has_content():
-            assert original.content == deserialized.content
-        if original.has_error():
-            assert original.error == deserialized.error
-        assert metadata == deserialized.metadata
+    # Assert
+    if original.has_content():
+        assert original.content == deserialized.content
+    if original.has_error():
+        assert original.error == deserialized.error
+    assert metadata == deserialized.metadata

From a5cf7359997a634cd8ae46de607275301a79b93c Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Tue, 5 Mar 2024 12:51:55 +0100
Subject: [PATCH 096/102] Update logging levels (#3057)

---
 src/py/flwr/server/server.py                           | 10 +++++-----
 src/py/flwr/server/superlink/driver/driver_servicer.py |  2 +-
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/py/flwr/server/server.py b/src/py/flwr/server/server.py
index dc7040193101..e10ccdc19025 100644
--- a/src/py/flwr/server/server.py
+++ b/src/py/flwr/server/server.py
@@ -17,7 +17,7 @@
 
 import concurrent.futures
 import timeit
-from logging import DEBUG, INFO, WARN
+from logging import INFO, WARN
 from typing import Dict, List, Optional, Tuple, Union
 
 from flwr.common import (
@@ -173,7 +173,7 @@ def evaluate_round(
             log(INFO, "evaluate_round %s: no clients selected, cancel", server_round)
             return None
         log(
-            DEBUG,
+            INFO,
             "evaluate_round %s: strategy sampled %s clients (out of %s)",
             server_round,
             len(client_instructions),
@@ -188,7 +188,7 @@ def evaluate_round(
             group_id=server_round,
         )
         log(
-            DEBUG,
+            INFO,
             "evaluate_round %s received %s results and %s failures",
             server_round,
             len(results),
@@ -223,7 +223,7 @@ def fit_round(
             log(INFO, "fit_round %s: no clients selected, cancel", server_round)
             return None
         log(
-            DEBUG,
+            INFO,
             "fit_round %s: strategy sampled %s clients (out of %s)",
             server_round,
             len(client_instructions),
@@ -238,7 +238,7 @@ def fit_round(
             group_id=server_round,
         )
         log(
-            DEBUG,
+            INFO,
             "fit_round %s received %s results and %s failures",
             server_round,
             len(results),
diff --git a/src/py/flwr/server/superlink/driver/driver_servicer.py b/src/py/flwr/server/superlink/driver/driver_servicer.py
index 5be53a8e0c6d..59e51ef52d8e 100644
--- a/src/py/flwr/server/superlink/driver/driver_servicer.py
+++ b/src/py/flwr/server/superlink/driver/driver_servicer.py
@@ -49,7 +49,7 @@ def GetNodes(
         self, request: GetNodesRequest, context: grpc.ServicerContext
     ) -> GetNodesResponse:
         """Get available nodes."""
-        log(INFO, "DriverServicer.GetNodes")
+        log(DEBUG, "DriverServicer.GetNodes")
         state: State = self.state_factory.state()
         all_ids: Set[int] = state.get_nodes(request.run_id)
         nodes: List[Node] = [

From 4fd9f85bf59df4b2171b475fd6c4da2c600b1ad4 Mon Sep 17 00:00:00 2001
From: Taner Topal <taner@flower.dev>
Date: Tue, 5 Mar 2024 20:42:34 +0100
Subject: [PATCH 097/102] Add NumPy template to new command (#3059)

---
 src/py/flwr/cli/new/new.py                    |  1 +
 .../templates/app/code/client.numpy.py.tpl    | 24 +++++++++++++++++++
 .../templates/app/code/server.numpy.py.tpl    | 12 ++++++++++
 .../cli/new/templates/app/flower.toml.tpl     |  4 ++--
 .../templates/app/requirements.numpy.txt.tpl  |  2 ++
 5 files changed, 41 insertions(+), 2 deletions(-)
 create mode 100644 src/py/flwr/cli/new/templates/app/code/client.numpy.py.tpl
 create mode 100644 src/py/flwr/cli/new/templates/app/code/server.numpy.py.tpl
 create mode 100644 src/py/flwr/cli/new/templates/app/requirements.numpy.txt.tpl

diff --git a/src/py/flwr/cli/new/new.py b/src/py/flwr/cli/new/new.py
index d5db6091344d..04e28efdf76a 100644
--- a/src/py/flwr/cli/new/new.py
+++ b/src/py/flwr/cli/new/new.py
@@ -28,6 +28,7 @@
 class MlFramework(str, Enum):
     """Available frameworks."""
 
+    NUMPY = "NumPy"
     PYTORCH = "PyTorch"
     TENSORFLOW = "TensorFlow"
 
diff --git a/src/py/flwr/cli/new/templates/app/code/client.numpy.py.tpl b/src/py/flwr/cli/new/templates/app/code/client.numpy.py.tpl
new file mode 100644
index 000000000000..cf24457c8d2c
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/client.numpy.py.tpl
@@ -0,0 +1,24 @@
+"""$project_name: A Flower / NumPy app."""
+
+import flwr as fl
+import numpy as np
+
+
+# Flower client, adapted from Pytorch quickstart example
+class FlowerClient(fl.client.NumPyClient):
+    def get_parameters(self, config):
+        return [np.ones((1, 1))]
+
+    def fit(self, parameters, config):
+        return ([np.ones((1, 1))], 1, {})
+
+    def evaluate(self, parameters, config):
+        return float(0.0), 1, {"accuracy": float(1.0)}
+
+
+def client_fn(cid: str):
+    return FlowerClient().to_client()
+
+
+# ClientApp for Flower-Next
+app = fl.client.ClientApp(client_fn=client_fn)
diff --git a/src/py/flwr/cli/new/templates/app/code/server.numpy.py.tpl b/src/py/flwr/cli/new/templates/app/code/server.numpy.py.tpl
new file mode 100644
index 000000000000..03f95ae35cfd
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/server.numpy.py.tpl
@@ -0,0 +1,12 @@
+"""$project_name: A Flower / NumPy app."""
+
+import flwr as fl
+
+# Configure the strategy
+strategy = fl.server.strategy.FedAvg()
+
+# Flower ServerApp
+app = fl.server.ServerApp(
+    config=fl.server.ServerConfig(num_rounds=1),
+    strategy=strategy,
+)
diff --git a/src/py/flwr/cli/new/templates/app/flower.toml.tpl b/src/py/flwr/cli/new/templates/app/flower.toml.tpl
index 4dd7117bc3a3..e171783527af 100644
--- a/src/py/flwr/cli/new/templates/app/flower.toml.tpl
+++ b/src/py/flwr/cli/new/templates/app/flower.toml.tpl
@@ -1,10 +1,10 @@
-[flower]
+[project]
 name = "$project_name"
 version = "1.0.0"
 description = ""
 license = "Apache-2.0"
 authors = ["The Flower Authors <hello@flower.ai>"]
 
-[components]
+[flower.components]
 serverapp = "$project_name.server:app"
 clientapp = "$project_name.client:app"
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
new file mode 100644
index 000000000000..dfb385079b23
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/requirements.numpy.txt.tpl
@@ -0,0 +1,2 @@
+flwr>=1.8, <2.0
+numpy >= 1.21.0

From 3043d72d8792918933313334888533f15cedb9b0 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Tue, 5 Mar 2024 21:09:29 +0100
Subject: [PATCH 098/102] Add InnerDirichletPartitioner (#2794)

Co-authored-by: jafermarq <javier@flower.ai>
---
 .../flwr_datasets/partitioner/__init__.py     |   2 +
 .../inner_dirichlet_partitioner.py            | 308 ++++++++++++++++++
 .../inner_dirichlet_partitioner_test.py       | 106 ++++++
 3 files changed, 416 insertions(+)
 create mode 100644 datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner.py
 create mode 100644 datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner_test.py

diff --git a/datasets/flwr_datasets/partitioner/__init__.py b/datasets/flwr_datasets/partitioner/__init__.py
index 73d048ddf3ff..0d1edbfcb04a 100644
--- a/datasets/flwr_datasets/partitioner/__init__.py
+++ b/datasets/flwr_datasets/partitioner/__init__.py
@@ -18,6 +18,7 @@
 from .dirichlet_partitioner import DirichletPartitioner
 from .exponential_partitioner import ExponentialPartitioner
 from .iid_partitioner import IidPartitioner
+from .inner_dirichlet_partitioner import InnerDirichletPartitioner
 from .linear_partitioner import LinearPartitioner
 from .natural_id_partitioner import NaturalIdPartitioner
 from .partitioner import Partitioner
@@ -32,6 +33,7 @@
     "DirichletPartitioner",
     "SizePartitioner",
     "LinearPartitioner",
+    "InnerDirichletPartitioner",
     "SquarePartitioner",
     "ShardPartitioner",
     "ExponentialPartitioner",
diff --git a/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner.py b/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner.py
new file mode 100644
index 000000000000..c25a9b059d18
--- /dev/null
+++ b/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner.py
@@ -0,0 +1,308 @@
+# Copyright 2023 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""InnerDirichlet partitioner."""
+import warnings
+from typing import Dict, List, Optional, Union
+
+import numpy as np
+
+import datasets
+from flwr_datasets.common.typing import NDArrayFloat, NDArrayInt
+from flwr_datasets.partitioner.partitioner import Partitioner
+
+
+class InnerDirichletPartitioner(Partitioner):  # pylint: disable=R0902
+    """Partitioner based on Dirichlet distribution.
+
+    Each partition is created based on the Dirichlet distribution, where the
+    probability corresponds to the fractions of samples of specific classes.
+    This process is iterative (sample by sample assignment), where first, the
+    partition ID to which the class will be assigned is chosen (at random, uniformly),
+    and then the class is decided based on the Dirichlet probabilities (note that when
+    a class gets exhausted - no more samples exists to sample from - the probability of
+    sampling this class is set as zero and the remaining probabilities renormalized).
+
+    Implementation based on: Federated Learning Based on Dynamic Regularization
+    (https://arxiv.org/abs/2111.04263).
+
+    Parameters
+    ----------
+    partition_sizes : Union[List[int], NDArrayInt]
+        The sizes of all partitions.
+    partition_by : str
+        Column name of the labels (targets) based on which Dirichlet sampling works.
+    alpha : Union[int, float, List[float], NDArrayFloat]
+        Concentration parameter to the Dirichlet distribution (a single value for
+        symmetric Dirichlet distribution, or a list/NDArray of length equal to the
+        number of unique classes)
+    shuffle: bool
+        Whether to randomize the order of samples. Shuffling applied after the
+        samples assignment to nodes.
+    seed: int
+        Seed used for dataset shuffling. It has no effect if `shuffle` is False.
+
+    Examples
+    --------
+    >>> from flwr_datasets import FederatedDataset
+    >>> from flwr_datasets.partitioner import InnerDirichletPartitioner
+    >>>
+    >>> partitioner = InnerDirichletPartitioner(
+    >>>     partition_sizes=[6_000] * 10, partition_by="label", alpha=0.5
+    >>> )
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
+    >>> partition = fds.load_partition(0)
+    >>> print(partition[0])  # Print the first example
+    """
+
+    def __init__(  # pylint: disable=R0913
+        self,
+        partition_sizes: Union[List[int], NDArrayInt],
+        partition_by: str,
+        alpha: Union[int, float, List[float], NDArrayFloat],
+        shuffle: bool = True,
+        seed: Optional[int] = 42,
+    ) -> None:
+        super().__init__()
+        # Attributes based on the constructor
+        self._partition_sizes = _instantiate_partition_sizes(partition_sizes)
+        self._initial_alpha = alpha
+        self._alpha: Optional[NDArrayFloat] = None
+        self._partition_by = partition_by
+        self._shuffle = shuffle
+        self._seed = seed
+
+        # Utility attributes
+        self._initialized_alpha = False
+        self._rng = np.random.default_rng(seed=self._seed)  # NumPy random generator
+        # The attributes below are determined during the first call to load_partition
+        self._unique_classes: Optional[Union[List[int], List[str]]] = None
+        self._num_unique_classes: Optional[int] = None
+        self._num_partitions = len(self._partition_sizes)
+
+        # self._avg_num_of_samples_per_node: Optional[float] = None
+        self._node_id_to_indices: Dict[int, List[int]] = {}
+        self._node_id_to_indices_determined = False
+
+    def load_partition(self, node_id: int) -> datasets.Dataset:
+        """Load a partition based on the partition index.
+
+        Parameters
+        ----------
+        node_id : int
+            the index that corresponds to the requested partition
+
+        Returns
+        -------
+        dataset_partition : Dataset
+            single partition of a dataset
+        """
+        # The partitioning is done lazily - only when the first partition is
+        # requested. Only the first call creates the indices assignments for all the
+        # partition indices.
+        self._check_num_partitions_correctness_if_needed()
+        self._check_partition_sizes_correctness_if_needed()
+        self._check_the_sum_of_partition_sizes()
+        self._determine_num_unique_classes_if_needed()
+        self._alpha = self._initialize_alpha_if_needed(self._initial_alpha)
+        self._determine_node_id_to_indices_if_needed()
+        return self.dataset.select(self._node_id_to_indices[node_id])
+
+    def _initialize_alpha_if_needed(
+        self, alpha: Union[int, float, List[float], NDArrayFloat]
+    ) -> NDArrayFloat:
+        """Convert alpha to the used format in the code a NDArrayFloat.
+
+        The alpha can be provided in constructor can be in different format for user
+        convenience. The format into which it's transformed here is used throughout the
+        code for computation.
+
+        Parameters
+        ----------
+        alpha : Union[int, float, List[float], NDArrayFloat]
+            Concentration parameter to the Dirichlet distribution
+
+        Returns
+        -------
+        alpha : NDArrayFloat
+            Concentration parameter in a format ready to used in computation.
+        """
+        if self._initialized_alpha:
+            assert self._alpha is not None
+            return self._alpha
+        if isinstance(alpha, int):
+            assert self._num_unique_classes is not None
+            alpha = np.array([float(alpha)], dtype=float).repeat(
+                self._num_unique_classes
+            )
+        elif isinstance(alpha, float):
+            assert self._num_unique_classes is not None
+            alpha = np.array([alpha], dtype=float).repeat(self._num_unique_classes)
+        elif isinstance(alpha, List):
+            if len(alpha) != self._num_unique_classes:
+                raise ValueError(
+                    "When passing alpha as a List, its length needs needs to be "
+                    "of length equal to the number of unique classes."
+                )
+            alpha = np.asarray(alpha)
+        elif isinstance(alpha, np.ndarray):
+            # pylint: disable=R1720
+            if alpha.ndim == 1 and alpha.shape[0] != self._num_unique_classes:
+                raise ValueError(
+                    "When passing alpha as an NDArray, its length needs needs to be "
+                    "of length equal to the number of unique classes."
+                )
+            elif alpha.ndim == 2:
+                alpha = alpha.flatten()
+                if alpha.shape[0] != self._num_unique_classes:
+                    raise ValueError(
+                        "When passing alpha as an NDArray, its length needs needs to be"
+                        " of length equal to the number of unique classes."
+                    )
+        else:
+            raise ValueError("The given alpha format is not supported.")
+        if not (alpha > 0).all():
+            raise ValueError(
+                f"Alpha values should be strictly greater than zero. "
+                f"Instead it'd be converted to {alpha}"
+            )
+        return alpha
+
+    def _determine_node_id_to_indices_if_needed(self) -> None:  # pylint: disable=R0914
+        """Create an assignment of indices to the partition indices."""
+        if self._node_id_to_indices_determined:
+            return
+
+        # Create class priors for the whole partitioning process
+        assert self._alpha is not None
+        class_priors = self._rng.dirichlet(alpha=self._alpha, size=self._num_partitions)
+        targets = np.asarray(self.dataset[self._partition_by])
+        # List representing indices of each class
+        assert self._num_unique_classes is not None
+        idx_list = [np.where(targets == i)[0] for i in range(self._num_unique_classes)]
+        class_sizes = [len(idx_list[i]) for i in range(self._num_unique_classes)]
+
+        client_indices = [
+            np.zeros(self._partition_sizes[cid]).astype(np.int64)
+            for cid in range(self._num_partitions)
+        ]
+
+        # Node id to number of sample left for allocation for that node id
+        node_id_to_left_to_allocate = dict(
+            zip(range(self._num_partitions), self._partition_sizes)
+        )
+
+        not_full_node_ids = list(range(self._num_partitions))
+        while np.sum(list(node_id_to_left_to_allocate.values())) != 0:
+            # Choose a node
+            current_node_id = self._rng.choice(not_full_node_ids)
+            # If current node is full resample a client
+            if node_id_to_left_to_allocate[current_node_id] == 0:
+                # When the node is full, exclude it from the sampling nodes list
+                not_full_node_ids.pop(not_full_node_ids.index(current_node_id))
+                continue
+            node_id_to_left_to_allocate[current_node_id] -= 1
+            # Access the label distribution of the chosen client
+            current_probabilities = class_priors[current_node_id]
+            while True:
+                # curr_class = np.argmax(np.random.uniform() <= curr_prior)
+                curr_class = self._rng.choice(
+                    list(range(self._num_unique_classes)), p=current_probabilities
+                )
+                # Redraw class label if there are no samples left to be allocated from
+                # that class
+                if class_sizes[curr_class] == 0:
+                    # Class got exhausted, set probabilities to 0
+                    class_priors[:, curr_class] = 0
+                    # Renormalize such that the probability sums to 1
+                    row_sums = class_priors.sum(axis=1, keepdims=True)
+                    class_priors = class_priors / row_sums
+                    # Adjust the current_probabilities (it won't sum up to 1 otherwise)
+                    current_probabilities = class_priors[current_node_id]
+                    continue
+                class_sizes[curr_class] -= 1
+                # Store sample index at the empty array cell
+                index = node_id_to_left_to_allocate[current_node_id]
+                client_indices[current_node_id][index] = idx_list[curr_class][
+                    class_sizes[curr_class]
+                ]
+                break
+
+        node_id_to_indices = {
+            cid: client_indices[cid].tolist() for cid in range(self._num_partitions)
+        }
+        # Shuffle the indices if the shuffle is True.
+        # Note that the samples from this partitioning do not necessarily require
+        # shuffling, the order should exhibit consecutive samples.
+        if self._shuffle:
+            for indices in node_id_to_indices.values():
+                # In place shuffling
+                self._rng.shuffle(indices)
+        self._node_id_to_indices = node_id_to_indices
+        self._node_id_to_indices_determined = True
+
+    def _check_num_partitions_correctness_if_needed(self) -> None:
+        """Test num_partitions when the dataset is given (in load_partition)."""
+        if not self._node_id_to_indices_determined:
+            if self._num_partitions > self.dataset.num_rows:
+                raise ValueError(
+                    "The number of partitions needs to be smaller or equal to "
+                    " the number of samples in the dataset."
+                )
+
+    def _check_partition_sizes_correctness_if_needed(self) -> None:
+        """Test partition_sizes when the dataset is given (in load_partition)."""
+        if not self._node_id_to_indices_determined:
+            if sum(self._partition_sizes) > self.dataset.num_rows:
+                raise ValueError(
+                    "The sum of the `partition_sizes` needs to be smaller or equal to "
+                    "the number of samples in the dataset."
+                )
+
+    def _check_num_partitions_greater_than_zero(self) -> None:
+        """Test num_partition left sides correctness."""
+        if not self._num_partitions > 0:
+            raise ValueError("The number of partitions needs to be greater than zero.")
+
+    def _determine_num_unique_classes_if_needed(self) -> None:
+        self._unique_classes = self.dataset.unique(self._partition_by)
+        assert self._unique_classes is not None
+        self._num_unique_classes = len(self._unique_classes)
+
+    def _check_the_sum_of_partition_sizes(self) -> None:
+        if np.sum(self._partition_sizes) != len(self.dataset):
+            warnings.warn(
+                "The sum of the partition_sizes does not sum to the whole "
+                "dataset size. Make sure that is the desired behavior.",
+                stacklevel=1,
+            )
+
+
+def _instantiate_partition_sizes(
+    partition_sizes: Union[List[int], NDArrayInt]
+) -> NDArrayInt:
+    """Transform list to the ndarray of ints if needed."""
+    if isinstance(partition_sizes, List):
+        partition_sizes = np.asarray(partition_sizes)
+    elif isinstance(partition_sizes, np.ndarray):
+        pass
+    else:
+        raise ValueError(
+            f"The type of partition_sizes is incorrect. Given: "
+            f"{type(partition_sizes)}"
+        )
+
+    if not all(partition_sizes >= 0):
+        raise ValueError("The samples numbers must be greater or equal to zero.")
+    return partition_sizes
diff --git a/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner_test.py b/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner_test.py
new file mode 100644
index 000000000000..0c5fb502870e
--- /dev/null
+++ b/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner_test.py
@@ -0,0 +1,106 @@
+# Copyright 2023 Flower Labs GmbH. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test DirichletPartitioner."""
+# pylint: disable=W0212
+import unittest
+from typing import List, Tuple, Union
+
+from datasets import Dataset
+from flwr_datasets.common.typing import NDArrayFloat, NDArrayInt
+from flwr_datasets.partitioner.inner_dirichlet_partitioner import (
+    InnerDirichletPartitioner,
+)
+
+
+def _dummy_setup(
+    num_rows: int,
+    partition_by: str,
+    partition_sizes: Union[List[int], NDArrayInt],
+    alpha: Union[float, List[float], NDArrayFloat],
+) -> Tuple[Dataset, InnerDirichletPartitioner]:
+    """Create a dummy dataset and partitioner for testing."""
+    data = {
+        partition_by: [i % 3 for i in range(num_rows)],
+        "features": list(range(num_rows)),
+    }
+    dataset = Dataset.from_dict(data)
+    partitioner = InnerDirichletPartitioner(
+        partition_sizes=partition_sizes,
+        alpha=alpha,
+        partition_by=partition_by,
+    )
+    partitioner.dataset = dataset
+    return dataset, partitioner
+
+
+class TestInnerDirichletPartitionerSuccess(unittest.TestCase):
+    """Test InnerDirichletPartitioner used with no exceptions."""
+
+    def test_correct_num_of_partitions(self) -> None:
+        """Test correct number of partitions."""
+        num_rows = 113
+        partition_by = "labels"
+        alpha = 1.0
+        partition_sizes = [20, 20, 30, 43]
+
+        _, partitioner = _dummy_setup(num_rows, partition_by, partition_sizes, alpha)
+        _ = partitioner.load_partition(0)
+        self.assertEqual(
+            len(partitioner._node_id_to_indices.keys()), len(partition_sizes)
+        )
+
+    def test_correct_partition_sizes(self) -> None:
+        """Test correct partition sizes."""
+        num_rows = 113
+        partition_by = "labels"
+        alpha = 1.0
+        partition_sizes = [20, 20, 30, 43]
+
+        _, partitioner = _dummy_setup(num_rows, partition_by, partition_sizes, alpha)
+        _ = partitioner.load_partition(0)
+        sizes_created = [
+            len(indices) for indices in partitioner._node_id_to_indices.values()
+        ]
+        self.assertEqual(sorted(sizes_created), partition_sizes)
+
+
+class TestInnerDirichletPartitionerFailure(unittest.TestCase):
+    """Test InnerDirichletPartitioner failures (exceptions) by incorrect usage."""
+
+    def test_incorrect_shape_of_alpha(self) -> None:
+        """Test the alpha shape not equal to the number of unique classes."""
+        num_rows = 113
+        partition_by = "labels"
+        alpha = [1.0, 1.0]
+        partition_sizes = [20, 20, 30, 43]
+
+        _, partitioner = _dummy_setup(num_rows, partition_by, partition_sizes, alpha)
+        with self.assertRaises(ValueError):
+            _ = partitioner.load_partition(0)
+
+    def test_too_big_sum_of_partition_sizes(self) -> None:
+        """Test sum of partition_sizes greater than the size of the dataset."""
+        num_rows = 113
+        partition_by = "labels"
+        alpha = 1.0
+        partition_sizes = [60, 60, 30, 43]
+
+        _, partitioner = _dummy_setup(num_rows, partition_by, partition_sizes, alpha)
+        with self.assertRaises(ValueError):
+            _ = partitioner.load_partition(0)
+
+
+if __name__ == "__main__":
+    unittest.main()

From 2eb21274c607dc16063ffcc344be77cebb5b0f44 Mon Sep 17 00:00:00 2001
From: Javier <jafermarq@users.noreply.github.com>
Date: Tue, 5 Mar 2024 21:46:53 +0100
Subject: [PATCH 099/102] Refactor `app_dir` arguments (#3061)

---
 src/py/flwr/server/app.py                     |  8 +--
 .../server/superlink/fleet/vce/vce_api.py     |  4 +-
 .../superlink/fleet/vce/vce_api_test.py       | 18 +++---
 src/py/flwr/simulation/run_simulation.py      | 61 +++++++++----------
 4 files changed, 45 insertions(+), 46 deletions(-)

diff --git a/src/py/flwr/server/app.py b/src/py/flwr/server/app.py
index 01b1f622212f..e04cfb37e118 100644
--- a/src/py/flwr/server/app.py
+++ b/src/py/flwr/server/app.py
@@ -365,7 +365,7 @@ def run_superlink() -> None:
             client_app_attr=args.client_app,
             backend_name=args.backend,
             backend_config_json_stream=args.backend_config,
-            working_dir=args.dir,
+            app_dir=args.app_dir,
             state_factory=state_factory,
             f_stop=f_stop,
         )
@@ -441,7 +441,7 @@ def _run_fleet_api_vce(
     client_app_attr: str,
     backend_name: str,
     backend_config_json_stream: str,
-    working_dir: str,
+    app_dir: str,
     state_factory: StateFactory,
     f_stop: asyncio.Event,
 ) -> None:
@@ -453,7 +453,7 @@ def _run_fleet_api_vce(
         backend_name=backend_name,
         backend_config_json_stream=backend_config_json_stream,
         state_factory=state_factory,
-        working_dir=working_dir,
+        app_dir=app_dir,
         f_stop=f_stop,
     )
 
@@ -705,7 +705,7 @@ def _add_args_fleet_api(parser: argparse.ArgumentParser) -> None:
         "`flwr.common.typing.ConfigsRecordValues`. ",
     )
     parser.add_argument(
-        "--dir",
+        "--app-dir",
         default="",
         help="Add specified directory to the PYTHONPATH and load"
         "ClientApp from there."
diff --git a/src/py/flwr/server/superlink/fleet/vce/vce_api.py b/src/py/flwr/server/superlink/fleet/vce/vce_api.py
index d42379960a63..225c6155774b 100644
--- a/src/py/flwr/server/superlink/fleet/vce/vce_api.py
+++ b/src/py/flwr/server/superlink/fleet/vce/vce_api.py
@@ -221,7 +221,7 @@ async def run(
 def start_vce(
     backend_name: str,
     backend_config_json_stream: str,
-    working_dir: str,
+    app_dir: str,
     f_stop: asyncio.Event,
     client_app: Optional[ClientApp] = None,
     client_app_attr: Optional[str] = None,
@@ -297,7 +297,7 @@ def start_vce(
 
     def backend_fn() -> Backend:
         """Instantiate a Backend."""
-        return backend_type(backend_config, work_dir=working_dir)
+        return backend_type(backend_config, work_dir=app_dir)
 
     log(INFO, "client_app_attr = %s", client_app_attr)
 
diff --git a/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py b/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py
index 16cb45c12624..72e9c07ebcca 100644
--- a/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py
+++ b/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py
@@ -119,21 +119,21 @@ def register_messages_into_state(
     return expected_results
 
 
-def _autoresolve_working_dir(rel_client_app_dir: str = "backend") -> str:
-    """Correctly resolve working directory."""
+def _autoresolve_app_dir(rel_client_app_dir: str = "backend") -> str:
+    """Correctly resolve working directory for the app."""
     file_path = Path(__file__)
-    working_dir = Path.cwd()
-    rel_workdir = file_path.relative_to(working_dir)
+    app_dir = Path.cwd()
+    rel_app_dir = file_path.relative_to(app_dir)
 
     # Susbtract lats element and append "backend/test" (wher the client module is.)
-    return str(rel_workdir.parent / rel_client_app_dir)
+    return str(rel_app_dir.parent / rel_client_app_dir)
 
 
 # pylint: disable=too-many-arguments
 def start_and_shutdown(
     backend: str = "ray",
     client_app_attr: str = "raybackend_test:client_app",
-    working_dir: str = "",
+    app_dir: str = "",
     num_supernodes: Optional[int] = None,
     state_factory: Optional[StateFactory] = None,
     nodes_mapping: Optional[NodeToPartitionMapping] = None,
@@ -157,8 +157,8 @@ def start_and_shutdown(
         termination_th.start()
 
     # Resolve working directory if not passed
-    if not working_dir:
-        working_dir = _autoresolve_working_dir()
+    if not app_dir:
+        app_dir = _autoresolve_app_dir()
 
     start_vce(
         num_supernodes=num_supernodes,
@@ -166,7 +166,7 @@ def start_and_shutdown(
         backend_name=backend,
         backend_config_json_stream=backend_config,
         state_factory=state_factory,
-        working_dir=working_dir,
+        app_dir=app_dir,
         f_stop=f_stop,
         existing_nodes_mapping=nodes_mapping,
     )
diff --git a/src/py/flwr/simulation/run_simulation.py b/src/py/flwr/simulation/run_simulation.py
index cb68221ea583..31884f2edc68 100644
--- a/src/py/flwr/simulation/run_simulation.py
+++ b/src/py/flwr/simulation/run_simulation.py
@@ -54,7 +54,7 @@ def run_simulation_from_cli() -> None:
         num_supernodes=args.num_supernodes,
         backend_name=args.backend,
         backend_config=backend_config_dict,
-        working_dir=args.dir,
+        app_dir=args.app_dir,
         driver_api_address=args.driver_api_address,
         enable_tf_gpu_growth=args.enable_tf_gpu_growth,
         verbose_logging=args.verbose,
@@ -125,7 +125,7 @@ def run_serverapp_th(
     server_app_attr: Optional[str],
     server_app: Optional[ServerApp],
     driver: Driver,
-    server_app_dir: str,
+    app_dir: str,
     f_stop: asyncio.Event,
     enable_tf_gpu_growth: bool,
     delay_launch: int = 3,
@@ -163,7 +163,7 @@ def server_th_with_start_checks(  # type: ignore
             "server_app_attr": server_app_attr,
             "loaded_server_app": server_app,
             "driver": driver,
-            "server_app_dir": server_app_dir,
+            "server_app_dir": app_dir,
         },
     )
     sleep(delay_launch)
@@ -177,7 +177,7 @@ def _main_loop(
     backend_name: str,
     backend_config_stream: str,
     driver_api_address: str,
-    working_dir: str,
+    app_dir: str,
     enable_tf_gpu_growth: bool,
     client_app: Optional[ClientApp] = None,
     client_app_attr: Optional[str] = None,
@@ -214,7 +214,7 @@ def _main_loop(
             server_app_attr=server_app_attr,
             server_app=server_app,
             driver=driver,
-            server_app_dir=working_dir,
+            app_dir=app_dir,
             f_stop=f_stop,
             enable_tf_gpu_growth=enable_tf_gpu_growth,
         )
@@ -227,7 +227,7 @@ def _main_loop(
             client_app=client_app,
             backend_name=backend_name,
             backend_config_json_stream=backend_config_stream,
-            working_dir=working_dir,
+            app_dir=app_dir,
             state_factory=state_factory,
             f_stop=f_stop,
         )
@@ -260,7 +260,7 @@ def _run_simulation(
     backend_config: Optional[Dict[str, ConfigsRecordValues]] = None,
     client_app_attr: Optional[str] = None,
     server_app_attr: Optional[str] = None,
-    working_dir: str = "",
+    app_dir: str = "",
     driver_api_address: str = "0.0.0.0:9091",
     enable_tf_gpu_growth: bool = False,
     verbose_logging: bool = False,
@@ -297,7 +297,7 @@ def _run_simulation(
         A path to a `ServerApp` module to be loaded: For example: `server:app` or
         `project.package.module:wrapper.app`."
 
-    working_dir : str
+    app_dir : str
         Add specified directory to the PYTHONPATH and load `ClientApp` from there.
         (Default: current working directory.)
 
@@ -340,7 +340,7 @@ def _run_simulation(
         backend_name,
         backend_config_stream,
         driver_api_address,
-        working_dir,
+        app_dir,
         enable_tf_gpu_growth,
         client_app,
         client_app_attr,
@@ -378,15 +378,21 @@ def _parse_args_run_simulation() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser(
         description="Start a Flower simulation",
     )
+    parser.add_argument(
+        "--server-app",
+        required=True,
+        help="For example: `server:app` or `project.package.module:wrapper.app`",
+    )
     parser.add_argument(
         "--client-app",
         required=True,
         help="For example: `client:app` or `project.package.module:wrapper.app`",
     )
     parser.add_argument(
-        "--server-app",
+        "--num-supernodes",
+        type=int,
         required=True,
-        help="For example: `server:app` or `project.package.module:wrapper.app`",
+        help="Number of simulated SuperNodes.",
     )
     parser.add_argument(
         "--driver-api-address",
@@ -394,18 +400,20 @@ def _parse_args_run_simulation() -> argparse.ArgumentParser:
         type=str,
         help="For example: `server:app` or `project.package.module:wrapper.app`",
     )
-    parser.add_argument(
-        "--num-supernodes",
-        type=int,
-        required=True,
-        help="Number of simulated SuperNodes.",
-    )
     parser.add_argument(
         "--backend",
         default="ray",
         type=str,
         help="Simulation backend that executes the ClientApp.",
     )
+    parser.add_argument(
+        "--backend-config",
+        type=str,
+        default='{"client_resources": {"num_cpus":2, "num_gpus":0.0}, "tensorflow": 0}',
+        help='A JSON formatted stream, e.g \'{"<keyA>":<value>, "<keyB>":<value>}\' to '
+        "configure a backend. Values supported in <value> are those included by "
+        "`flwr.common.typing.ConfigsRecordValues`. ",
+    )
     parser.add_argument(
         "--enable-tf-gpu-growth",
         action="store_true",
@@ -417,26 +425,17 @@ def _parse_args_run_simulation() -> argparse.ArgumentParser:
         "the TensorFlow documentation: https://www.tensorflow.org/api/stable.",
     )
     parser.add_argument(
-        "--backend-config",
-        type=str,
-        default='{"client_resources": {"num_cpus":2, "num_gpus":0.0}, "tensorflow": 0}',
-        help='A JSON formatted stream, e.g \'{"<keyA>":<value>, "<keyB>":<value>}\' to '
-        "configure a backend. Values supported in <value> are those included by "
-        "`flwr.common.typing.ConfigsRecordValues`. ",
+        "--verbose",
+        action="store_true",
+        help="When unset, only INFO, WARNING and ERROR log messages will be shown. "
+        "If set, DEBUG-level logs will be displayed. ",
     )
     parser.add_argument(
-        "--dir",
+        "--app-dir",
         default="",
         help="Add specified directory to the PYTHONPATH and load"
         "ClientApp and ServerApp from there."
         " Default: current working directory.",
     )
 
-    parser.add_argument(
-        "--verbose",
-        action="store_true",
-        help="When unset, only INFO, WARNING and ERROR log messages will be shown. "
-        "If set, DEBUG-level logs will be displayed. ",
-    )
-
     return parser

From 12982983115eabc3964977b254dcf4c486ff0f18 Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.ai>
Date: Tue, 5 Mar 2024 22:43:27 +0100
Subject: [PATCH 100/102] Add Flower Client App connection error handling
 (#2969)

Co-authored-by: Daniel J. Beutel <daniel@flower.ai>
---
 src/py/flwr/client/app.py                     | 101 ++++++++++++++++--
 src/py/flwr/client/grpc_client/connection.py  |   7 ++
 .../client/grpc_client/connection_test.py     |  12 ++-
 .../client/grpc_rere_client/connection.py     |  18 +++-
 src/py/flwr/client/rest_client/connection.py  |  20 +++-
 5 files changed, 141 insertions(+), 17 deletions(-)

diff --git a/src/py/flwr/client/app.py b/src/py/flwr/client/app.py
index 93d654379cfc..43781776f784 100644
--- a/src/py/flwr/client/app.py
+++ b/src/py/flwr/client/app.py
@@ -20,7 +20,9 @@
 import time
 from logging import DEBUG, INFO, WARN
 from pathlib import Path
-from typing import Callable, ContextManager, Optional, Tuple, Union
+from typing import Callable, ContextManager, Optional, Tuple, Type, Union
+
+from grpc import RpcError
 
 from flwr.client.client import Client
 from flwr.client.client_app import ClientApp
@@ -36,6 +38,7 @@
 )
 from flwr.common.exit_handlers import register_exit_handlers
 from flwr.common.logger import log, warn_deprecated_feature, warn_experimental_feature
+from flwr.common.retry_invoker import RetryInvoker, exponential
 
 from .client_app import load_client_app
 from .grpc_client.connection import grpc_connection
@@ -104,6 +107,8 @@ def _load() -> ClientApp:
         transport="rest" if args.rest else "grpc-rere",
         root_certificates=root_certificates,
         insecure=args.insecure,
+        max_retries=args.max_retries,
+        max_wait_time=args.max_wait_time,
     )
     register_exit_handlers(event_type=EventType.RUN_CLIENT_APP_LEAVE)
 
@@ -141,6 +146,22 @@ def _parse_args_run_client_app() -> argparse.ArgumentParser:
         default="0.0.0.0:9092",
         help="Server address",
     )
+    parser.add_argument(
+        "--max-retries",
+        type=int,
+        default=None,
+        help="The maximum number of times the client will try to connect to the"
+        "server before giving up in case of a connection error. By default,"
+        "it is set to None, meaning there is no limit to the number of tries.",
+    )
+    parser.add_argument(
+        "--max-wait-time",
+        type=float,
+        default=None,
+        help="The maximum duration before the client stops trying to"
+        "connect to the server in case of connection error. By default, it"
+        "is set to None, meaning there is no limit to the total time.",
+    )
     parser.add_argument(
         "--dir",
         default="",
@@ -180,6 +201,8 @@ def start_client(
     root_certificates: Optional[Union[bytes, str]] = None,
     insecure: Optional[bool] = None,
     transport: Optional[str] = None,
+    max_retries: Optional[int] = None,
+    max_wait_time: Optional[float] = None,
 ) -> None:
     """Start a Flower client node which connects to a Flower server.
 
@@ -213,6 +236,14 @@ class `flwr.client.Client` (default: None)
         - 'grpc-bidi': gRPC, bidirectional streaming
         - 'grpc-rere': gRPC, request-response (experimental)
         - 'rest': HTTP (experimental)
+    max_retries: Optional[int] (default: None)
+        The maximum number of times the client will try to connect to the
+        server before giving up in case of a connection error. If set to None,
+        there is no limit to the number of tries.
+    max_wait_time: Optional[float] (default: None)
+        The maximum duration before the client stops trying to
+        connect to the server in case of connection error.
+        If set to None, there is no limit to the total time.
 
     Examples
     --------
@@ -254,6 +285,8 @@ class `flwr.client.Client` (default: None)
         root_certificates=root_certificates,
         insecure=insecure,
         transport=transport,
+        max_retries=max_retries,
+        max_wait_time=max_wait_time,
     )
     event(EventType.START_CLIENT_LEAVE)
 
@@ -272,6 +305,8 @@ def _start_client_internal(
     root_certificates: Optional[Union[bytes, str]] = None,
     insecure: Optional[bool] = None,
     transport: Optional[str] = None,
+    max_retries: Optional[int] = None,
+    max_wait_time: Optional[float] = None,
 ) -> None:
     """Start a Flower client node which connects to a Flower server.
 
@@ -299,7 +334,7 @@ class `flwr.client.Client` (default: None)
         The PEM-encoded root certificates as a byte string or a path string.
         If provided, a secure connection using the certificates will be
         established to an SSL-enabled Flower server.
-    insecure : bool (default: True)
+    insecure : Optional[bool] (default: None)
         Starts an insecure gRPC connection when True. Enables HTTPS connection
         when False, using system certificates if `root_certificates` is None.
     transport : Optional[str] (default: None)
@@ -307,6 +342,14 @@ class `flwr.client.Client` (default: None)
         - 'grpc-bidi': gRPC, bidirectional streaming
         - 'grpc-rere': gRPC, request-response (experimental)
         - 'rest': HTTP (experimental)
+    max_retries: Optional[int] (default: None)
+        The maximum number of times the client will try to connect to the
+        server before giving up in case of a connection error. If set to None,
+        there is no limit to the number of tries.
+    max_wait_time: Optional[float] (default: None)
+        The maximum duration before the client stops trying to
+        connect to the server in case of connection error.
+        If set to None, there is no limit to the total time.
     """
     if insecure is None:
         insecure = root_certificates is None
@@ -338,7 +381,45 @@ def _load_client_app() -> ClientApp:
     # Both `client` and `client_fn` must not be used directly
 
     # Initialize connection context manager
-    connection, address = _init_connection(transport, server_address)
+    connection, address, connection_error_type = _init_connection(
+        transport, server_address
+    )
+
+    retry_invoker = RetryInvoker(
+        wait_factory=exponential,
+        recoverable_exceptions=connection_error_type,
+        max_tries=max_retries,
+        max_time=max_wait_time,
+        on_giveup=lambda retry_state: (
+            log(
+                WARN,
+                "Giving up reconnection after %.2f seconds and %s tries.",
+                retry_state.elapsed_time,
+                retry_state.tries,
+            )
+            if retry_state.tries > 1
+            else None
+        ),
+        on_success=lambda retry_state: (
+            log(
+                INFO,
+                "Connection successful after %.2f seconds and %s tries.",
+                retry_state.elapsed_time,
+                retry_state.tries,
+            )
+            if retry_state.tries > 1
+            else None
+        ),
+        on_backoff=lambda retry_state: (
+            log(WARN, "Connection attempt failed, retrying...")
+            if retry_state.tries == 1
+            else log(
+                DEBUG,
+                "Connection attempt failed, retrying in %.2f seconds",
+                retry_state.actual_wait,
+            )
+        ),
+    )
 
     node_state = NodeState()
 
@@ -347,6 +428,7 @@ def _load_client_app() -> ClientApp:
         with connection(
             address,
             insecure,
+            retry_invoker,
             grpc_max_message_length,
             root_certificates,
         ) as conn:
@@ -509,7 +591,7 @@ def start_numpy_client(
 
 def _init_connection(transport: Optional[str], server_address: str) -> Tuple[
     Callable[
-        [str, bool, int, Union[bytes, str, None]],
+        [str, bool, RetryInvoker, int, Union[bytes, str, None]],
         ContextManager[
             Tuple[
                 Callable[[], Optional[Message]],
@@ -520,6 +602,7 @@ def _init_connection(transport: Optional[str], server_address: str) -> Tuple[
         ],
     ],
     str,
+    Type[Exception],
 ]:
     # Parse IP address
     parsed_address = parse_address(server_address)
@@ -535,6 +618,8 @@ def _init_connection(transport: Optional[str], server_address: str) -> Tuple[
     # Use either gRPC bidirectional streaming or REST request/response
     if transport == TRANSPORT_TYPE_REST:
         try:
+            from requests.exceptions import ConnectionError as RequestsConnectionError
+
             from .rest_client.connection import http_request_response
         except ModuleNotFoundError:
             sys.exit(MISSING_EXTRA_REST)
@@ -543,14 +628,14 @@ def _init_connection(transport: Optional[str], server_address: str) -> Tuple[
                 "When using the REST API, please provide `https://` or "
                 "`http://` before the server address (e.g. `http://127.0.0.1:8080`)"
             )
-        connection = http_request_response
+        connection, error_type = http_request_response, RequestsConnectionError
     elif transport == TRANSPORT_TYPE_GRPC_RERE:
-        connection = grpc_request_response
+        connection, error_type = grpc_request_response, RpcError
     elif transport == TRANSPORT_TYPE_GRPC_BIDI:
-        connection = grpc_connection
+        connection, error_type = grpc_connection, RpcError
     else:
         raise ValueError(
             f"Unknown transport type: {transport} (possible: {TRANSPORT_TYPES})"
         )
 
-    return connection, address
+    return connection, address, error_type
diff --git a/src/py/flwr/client/grpc_client/connection.py b/src/py/flwr/client/grpc_client/connection.py
index 3561626dcb39..ddbb5336b2ac 100644
--- a/src/py/flwr/client/grpc_client/connection.py
+++ b/src/py/flwr/client/grpc_client/connection.py
@@ -39,6 +39,7 @@
 )
 from flwr.common.grpc import create_channel
 from flwr.common.logger import log
+from flwr.common.retry_invoker import RetryInvoker
 from flwr.proto.transport_pb2 import (  # pylint: disable=E0611
     ClientMessage,
     Reason,
@@ -62,6 +63,7 @@ def on_channel_state_change(channel_connectivity: str) -> None:
 def grpc_connection(  # pylint: disable=R0915
     server_address: str,
     insecure: bool,
+    retry_invoker: RetryInvoker,  # pylint: disable=unused-argument
     max_message_length: int = GRPC_MAX_MESSAGE_LENGTH,
     root_certificates: Optional[Union[bytes, str]] = None,
 ) -> Iterator[
@@ -80,6 +82,11 @@ def grpc_connection(  # pylint: disable=R0915
         The IPv4 or IPv6 address of the server. If the Flower server runs on the same
         machine on port 8080, then `server_address` would be `"0.0.0.0:8080"` or
         `"[::]:8080"`.
+    insecure : bool
+        Starts an insecure gRPC connection when True. Enables HTTPS connection
+        when False, using system certificates if `root_certificates` is None.
+    retry_invoker: RetryInvoker
+        Unused argument present for compatibilty.
     max_message_length : int
         The maximum length of gRPC messages that can be exchanged with the Flower
         server. The default should be sufficient for most models. Users who train
diff --git a/src/py/flwr/client/grpc_client/connection_test.py b/src/py/flwr/client/grpc_client/connection_test.py
index 30bff068b60a..28e03979fd65 100644
--- a/src/py/flwr/client/grpc_client/connection_test.py
+++ b/src/py/flwr/client/grpc_client/connection_test.py
@@ -26,6 +26,7 @@
 from flwr.common import ConfigsRecord, Message, Metadata, RecordSet
 from flwr.common import recordset_compat as compat
 from flwr.common.constant import MESSAGE_TYPE_GET_PROPERTIES
+from flwr.common.retry_invoker import RetryInvoker, exponential
 from flwr.common.typing import Code, GetPropertiesRes, Status
 from flwr.proto.transport_pb2 import (  # pylint: disable=E0611
     ClientMessage,
@@ -127,7 +128,16 @@ def test_integration_connection() -> None:
     def run_client() -> int:
         messages_received: int = 0
 
-        with grpc_connection(server_address=f"[::]:{port}", insecure=True) as conn:
+        with grpc_connection(
+            server_address=f"[::]:{port}",
+            insecure=True,
+            retry_invoker=RetryInvoker(
+                wait_factory=exponential,
+                recoverable_exceptions=grpc.RpcError,
+                max_tries=1,
+                max_time=None,
+            ),
+        ) as conn:
             receive, send, _, _ = conn
 
             # Setup processing loop
diff --git a/src/py/flwr/client/grpc_rere_client/connection.py b/src/py/flwr/client/grpc_rere_client/connection.py
index 00b7a864c5d6..e6e22998b947 100644
--- a/src/py/flwr/client/grpc_rere_client/connection.py
+++ b/src/py/flwr/client/grpc_rere_client/connection.py
@@ -27,6 +27,7 @@
 from flwr.common.grpc import create_channel
 from flwr.common.logger import log, warn_experimental_feature
 from flwr.common.message import Message, Metadata
+from flwr.common.retry_invoker import RetryInvoker
 from flwr.common.serde import message_from_taskins, message_to_taskres
 from flwr.proto.fleet_pb2 import (  # pylint: disable=E0611
     CreateNodeRequest,
@@ -51,6 +52,7 @@ def on_channel_state_change(channel_connectivity: str) -> None:
 def grpc_request_response(
     server_address: str,
     insecure: bool,
+    retry_invoker: RetryInvoker,
     max_message_length: int = GRPC_MAX_MESSAGE_LENGTH,  # pylint: disable=W0613
     root_certificates: Optional[Union[bytes, str]] = None,
 ) -> Iterator[
@@ -72,6 +74,13 @@ def grpc_request_response(
         The IPv6 address of the server with `http://` or `https://`.
         If the Flower server runs on the same machine
         on port 8080, then `server_address` would be `"http://[::]:8080"`.
+    insecure : bool
+        Starts an insecure gRPC connection when True. Enables HTTPS connection
+        when False, using system certificates if `root_certificates` is None.
+    retry_invoker: RetryInvoker
+        `RetryInvoker` object that will try to reconnect the client to the server
+        after gRPC errors. If None, the client will only try to
+        reconnect once after a failure.
     max_message_length : int
         Ignored, only present to preserve API-compatibility.
     root_certificates : Optional[Union[bytes, str]] (default: None)
@@ -113,7 +122,8 @@ def grpc_request_response(
     def create_node() -> None:
         """Set create_node."""
         create_node_request = CreateNodeRequest()
-        create_node_response = stub.CreateNode(
+        create_node_response = retry_invoker.invoke(
+            stub.CreateNode,
             request=create_node_request,
         )
         node_store[KEY_NODE] = create_node_response.node
@@ -127,7 +137,7 @@ def delete_node() -> None:
         node: Node = cast(Node, node_store[KEY_NODE])
 
         delete_node_request = DeleteNodeRequest(node=node)
-        stub.DeleteNode(request=delete_node_request)
+        retry_invoker.invoke(stub.DeleteNode, request=delete_node_request)
 
         del node_store[KEY_NODE]
 
@@ -141,7 +151,7 @@ def receive() -> Optional[Message]:
 
         # Request instructions (task) from server
         request = PullTaskInsRequest(node=node)
-        response = stub.PullTaskIns(request=request)
+        response = retry_invoker.invoke(stub.PullTaskIns, request=request)
 
         # Get the current TaskIns
         task_ins: Optional[TaskIns] = get_task_ins(response)
@@ -185,7 +195,7 @@ def send(message: Message) -> None:
 
         # Serialize ProtoBuf to bytes
         request = PushTaskResRequest(task_res_list=[task_res])
-        _ = stub.PushTaskRes(request)
+        _ = retry_invoker.invoke(stub.PushTaskRes, request)
 
         state[KEY_METADATA] = None
 
diff --git a/src/py/flwr/client/rest_client/connection.py b/src/py/flwr/client/rest_client/connection.py
index c637475551ed..d2cc71ba3b3f 100644
--- a/src/py/flwr/client/rest_client/connection.py
+++ b/src/py/flwr/client/rest_client/connection.py
@@ -27,6 +27,7 @@
 from flwr.common.constant import MISSING_EXTRA_REST
 from flwr.common.logger import log
 from flwr.common.message import Message, Metadata
+from flwr.common.retry_invoker import RetryInvoker
 from flwr.common.serde import message_from_taskins, message_to_taskres
 from flwr.proto.fleet_pb2 import (  # pylint: disable=E0611
     CreateNodeRequest,
@@ -61,6 +62,7 @@
 def http_request_response(
     server_address: str,
     insecure: bool,  # pylint: disable=unused-argument
+    retry_invoker: RetryInvoker,
     max_message_length: int = GRPC_MAX_MESSAGE_LENGTH,  # pylint: disable=W0613
     root_certificates: Optional[
         Union[bytes, str]
@@ -84,6 +86,12 @@ def http_request_response(
         The IPv6 address of the server with `http://` or `https://`.
         If the Flower server runs on the same machine
         on port 8080, then `server_address` would be `"http://[::]:8080"`.
+    insecure : bool
+        Unused argument present for compatibilty.
+    retry_invoker: RetryInvoker
+        `RetryInvoker` object that will try to reconnect the client to the server
+        after REST connection errors. If None, the client will only try to
+        reconnect once after a failure.
     max_message_length : int
         Ignored, only present to preserve API-compatibility.
     root_certificates : Optional[Union[bytes, str]] (default: None)
@@ -134,7 +142,8 @@ def create_node() -> None:
         create_node_req_proto = CreateNodeRequest()
         create_node_req_bytes: bytes = create_node_req_proto.SerializeToString()
 
-        res = requests.post(
+        res = retry_invoker.invoke(
+            requests.post,
             url=f"{base_url}/{PATH_CREATE_NODE}",
             headers={
                 "Accept": "application/protobuf",
@@ -177,7 +186,8 @@ def delete_node() -> None:
         node: Node = cast(Node, node_store[KEY_NODE])
         delete_node_req_proto = DeleteNodeRequest(node=node)
         delete_node_req_req_bytes: bytes = delete_node_req_proto.SerializeToString()
-        res = requests.post(
+        res = retry_invoker.invoke(
+            requests.post,
             url=f"{base_url}/{PATH_DELETE_NODE}",
             headers={
                 "Accept": "application/protobuf",
@@ -218,7 +228,8 @@ def receive() -> Optional[Message]:
         pull_task_ins_req_bytes: bytes = pull_task_ins_req_proto.SerializeToString()
 
         # Request instructions (task) from server
-        res = requests.post(
+        res = retry_invoker.invoke(
+            requests.post,
             url=f"{base_url}/{PATH_PULL_TASK_INS}",
             headers={
                 "Accept": "application/protobuf",
@@ -298,7 +309,8 @@ def send(message: Message) -> None:
         )
 
         # Send ClientMessage to server
-        res = requests.post(
+        res = retry_invoker.invoke(
+            requests.post,
             url=f"{base_url}/{PATH_PUSH_TASK_RES}",
             headers={
                 "Accept": "application/protobuf",

From 5cd6dc8f2e99152759aadf196f5120ba07f94018 Mon Sep 17 00:00:00 2001
From: Heng Pan <pan@flower.ai>
Date: Tue, 5 Mar 2024 23:01:33 +0100
Subject: [PATCH 101/102] Add `MessageType` (#3005)

Co-authored-by: Daniel J. Beutel <daniel@flower.ai>
---
 src/py/flwr/common/__init__.py |  2 ++
 src/py/flwr/common/constant.py | 11 +++++++++++
 2 files changed, 13 insertions(+)

diff --git a/src/py/flwr/common/__init__.py b/src/py/flwr/common/__init__.py
index 0b2d3f17c47c..d9d0861d949f 100644
--- a/src/py/flwr/common/__init__.py
+++ b/src/py/flwr/common/__init__.py
@@ -15,6 +15,7 @@
 """Common components shared between server and client."""
 
 
+from .constant import MessageType as MessageType
 from .context import Context as Context
 from .date import now as now
 from .grpc import GRPC_MAX_MESSAGE_LENGTH
@@ -83,6 +84,7 @@
     "GRPC_MAX_MESSAGE_LENGTH",
     "log",
     "Message",
+    "MessageType",
     "Metadata",
     "Metrics",
     "MetricsAggregationFn",
diff --git a/src/py/flwr/common/constant.py b/src/py/flwr/common/constant.py
index 2946a594e68c..020610d17d35 100644
--- a/src/py/flwr/common/constant.py
+++ b/src/py/flwr/common/constant.py
@@ -42,6 +42,17 @@
 MESSAGE_TYPE_EVALUATE = "evaluate"
 
 
+class MessageType:
+    """Message type."""
+
+    TRAIN = "train"
+    EVALUATE = "evaluate"
+
+    def __new__(cls) -> MessageType:
+        """Prevent instantiation."""
+        raise TypeError(f"{cls.__name__} cannot be instantiated.")
+
+
 class SType:
     """Serialisation type."""
 

From 6da8079af87262478be743dd48299227fadad5c0 Mon Sep 17 00:00:00 2001
From: Heng Pan <pan@flower.ai>
Date: Wed, 6 Mar 2024 10:23:09 +0100
Subject: [PATCH 102/102] Add `LegacyMessageType` (#3064)

---
 src/py/flwr/common/__init__.py |  2 ++
 src/py/flwr/common/constant.py | 13 ++++++++++++-
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/src/py/flwr/common/__init__.py b/src/py/flwr/common/__init__.py
index d9d0861d949f..9f9ff7ebc68a 100644
--- a/src/py/flwr/common/__init__.py
+++ b/src/py/flwr/common/__init__.py
@@ -16,6 +16,7 @@
 
 
 from .constant import MessageType as MessageType
+from .constant import MessageTypeLegacy as MessageTypeLegacy
 from .context import Context as Context
 from .date import now as now
 from .grpc import GRPC_MAX_MESSAGE_LENGTH
@@ -85,6 +86,7 @@
     "log",
     "Message",
     "MessageType",
+    "MessageTypeLegacy",
     "Metadata",
     "Metrics",
     "MetricsAggregationFn",
diff --git a/src/py/flwr/common/constant.py b/src/py/flwr/common/constant.py
index 020610d17d35..d3f429586a07 100644
--- a/src/py/flwr/common/constant.py
+++ b/src/py/flwr/common/constant.py
@@ -38,7 +38,7 @@
 
 MESSAGE_TYPE_GET_PROPERTIES = "get_properties"
 MESSAGE_TYPE_GET_PARAMETERS = "get_parameters"
-MESSAGE_TYPE_FIT = "fit"
+MESSAGE_TYPE_FIT = "train"
 MESSAGE_TYPE_EVALUATE = "evaluate"
 
 
@@ -53,6 +53,17 @@ def __new__(cls) -> MessageType:
         raise TypeError(f"{cls.__name__} cannot be instantiated.")
 
 
+class MessageTypeLegacy:
+    """Legacy message type."""
+
+    GET_PROPERTIES = "get_properties"
+    GET_PARAMETERS = "get_parameters"
+
+    def __new__(cls) -> MessageTypeLegacy:
+        """Prevent instantiation."""
+        raise TypeError(f"{cls.__name__} cannot be instantiated.")
+
+
 class SType:
     """Serialisation type."""