From bd8c0e01d63578df9c079b0e5235f73b3fb6d97f Mon Sep 17 00:00:00 2001
From: Charles Beauville <charles@flower.dev>
Date: Thu, 23 Nov 2023 19:03:50 +0100
Subject: [PATCH] Add files for chinese translation (#2631)

---
 .../zh_Hans/LC_MESSAGES/framework-docs.po     | 13608 ++++++++++++++++
 doc/locales/zh_Hans/LC_MESSAGES/sphinx.po     |    37 +
 2 files changed, 13645 insertions(+)
 create mode 100644 doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
 create mode 100644 doc/locales/zh_Hans/LC_MESSAGES/sphinx.po

diff --git a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
new file mode 100644
index 000000000000..f077264b4aab
--- /dev/null
+++ b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
@@ -0,0 +1,13608 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2022 Flower Labs GmbH
+# This file is distributed under the same license as the Flower package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2023.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: Flower main\n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2023-11-23 18:31+0100\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language: zh_Hans\n"
+"Language-Team: zh_Hans <LL@li.org>\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"
+"Generated-By: Babel 2.13.1\n"
+
+#: ../../source/contributor-explanation-architecture.rst:2
+msgid "Flower Architecture"
+msgstr ""
+
+#: ../../source/contributor-explanation-architecture.rst:5
+msgid "Edge Client Engine"
+msgstr ""
+
+#: ../../source/contributor-explanation-architecture.rst:7
+msgid ""
+"`Flower <https://flower.dev>`_ core framework architecture with Edge "
+"Client Engine"
+msgstr ""
+
+#: ../../source/contributor-explanation-architecture.rst:13
+msgid "Virtual Client Engine"
+msgstr ""
+
+#: ../../source/contributor-explanation-architecture.rst:15
+msgid ""
+"`Flower <https://flower.dev>`_ core framework architecture with Virtual "
+"Client Engine"
+msgstr ""
+
+#: ../../source/contributor-explanation-architecture.rst:21
+msgid "Virtual Client Engine and Edge Client Engine in the same workload"
+msgstr ""
+
+#: ../../source/contributor-explanation-architecture.rst:23
+msgid ""
+"`Flower <https://flower.dev>`_ core framework architecture with both "
+"Virtual Client Engine and Edge Client Engine"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:2
+msgid "Creating New Messages"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:4
+msgid ""
+"This is a simple guide for creating a new type of message between the "
+"server and clients in Flower."
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:6
+msgid ""
+"Let's suppose we have the following example functions in "
+":code:`server.py` and :code:`numpy_client.py`..."
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:8
+msgid "Server's side:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:17
+msgid "Client's side:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:26
+msgid ""
+"Let's now see what we need to implement in order to get this simple "
+"function between the server and client to work!"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:30
+msgid "Message Types for Protocol Buffers"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:32
+msgid ""
+"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>`_."
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:35
+msgid "Within the :code:`ServerMessage` block:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:52
+msgid "Within the ClientMessage block:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:70
+msgid ""
+"Make sure to also add a field of the newly created message type in "
+":code:`oneof msg`."
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:72
+msgid "Once that is done, we will compile the file with:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:78
+msgid "If it compiles succesfully, you should see the following message:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:87
+msgid "Serialization and Deserialization Functions"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:89
+msgid ""
+"Our next step is to add functions to serialize and deserialize Python "
+"datatypes to or from our defined RPC message types. You should add these "
+"functions in :code:`serde.py`."
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:91
+msgid "The four functions:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:112
+msgid "Sending the Message from the Server"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:114
+msgid ""
+"Now write the request function in your Client Proxy class (e.g., "
+":code:`grpc_client_proxy.py`) using the serde functions you just created:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:128
+msgid "Receiving the Message by the Client"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:130
+msgid ""
+"Last step! Modify the code in :code:`message_handler.py` to check the "
+"field of your message and call the :code:`example_response` function. "
+"Remember to use the serde functions!"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:132
+msgid "Within the handle function:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:139
+msgid "And add a new function:"
+msgstr ""
+
+#: ../../source/contributor-how-create-new-messages.rst:149
+msgid "Hopefully, when you run your program you will get the intended result!"
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:2
+msgid "Contribute translations"
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:4
+msgid ""
+"Since `Flower 1.5 <https://flower.dev/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 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."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:13
+msgid ""
+"Our translation project is publicly available over on `Weblate "
+"<https://hosted.weblate.org/projects/flower-docs/framework/>`_, this "
+"where most of the work will happen."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:18
+msgid "Contribute to existing languages"
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:23
+msgid ""
+"The first thing you will need to do in order to contribute is to create a"
+" free Weblate account on this `page "
+"<https://hosted.weblate.org/accounts/register/>`_. More information about"
+" profile settings can be found `here "
+"<https://docs.weblate.org/en/latest/user/profile.html>`_."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:29
+msgid ""
+"Once you are signed in to Weblate, you can navigate to the `Flower "
+"Framework project <https://hosted.weblate.org/projects/flower-"
+"docs/framework/>`_. Here, you should see the different existing languages"
+" that can be found on the website."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:34
+msgid ""
+"Once you have selected the language you want to contribute to, you should"
+" see a similar interface to this:"
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:39
+msgid ""
+"The most straight forward option here is to click on the ``Translate`` "
+"button on the top right (in the ``Translation status`` section). This "
+"will automatically bring you to the translation interface for "
+"untranslated strings."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:43
+msgid "This is what the interface looks like:"
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:47
+msgid ""
+"You input your translation in the textbox 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 suggestions for other users to view), or ``Skip`` (to"
+" go to the next untranslated string without saving anything)."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:54
+msgid ""
+"In order to help with the translations, you can see on the bottom the "
+"``Nearby strings``, the ``Comments`` (from other contributors), the "
+"``Automatic suggestions`` (from machine translation engines), the "
+"translations in ``Other languages``, and the ``History`` of translations "
+"for this string."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:59
+msgid ""
+"On the right, under the ``String information`` section, you can also "
+"click the link under ``Source string location`` in order to view the "
+"source of the doc file containing the string."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:63
+msgid ""
+"For more information about translating using Weblate, you can check out "
+"this `in-depth guide "
+"<https://docs.weblate.org/en/latest/user/translating.html>`_."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:67
+msgid "Add new languages"
+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 "
+"issue on our `GitHub repo <https://github.com/adap/flower>`_."
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:2
+msgid "Develop in VSCode Dev Containers"
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:4
+msgid ""
+"When working on the Flower framework we want to ensure that all "
+"contributors use the same developer environment to format code or run "
+"tests. For this purpose we are using the VSCode Remote Containers "
+"extension. What is it? Read the following quote:"
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:7
+msgid ""
+"The Visual Studio Code Remote - Containers extension lets you use a "
+"Docker container as a fully-featured development environment. It allows "
+"you to open any folder inside (or mounted into) a container and take "
+"advantage of Visual Studio Code's full feature set. A "
+":code:`devcontainer.json` file in your project tells VS Code how to "
+"access (or create) a development container with a well-defined tool and "
+"runtime stack. This container can be used to run an application or to "
+"separate tools, libraries, or runtimes needed for working with a "
+"codebase."
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:9
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:11
+msgid ""
+"Source: `Official VSCode documentation "
+"<https://code.visualstudio.com/docs/remote/containers>`_"
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:15
+msgid "Getting started"
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:17
+msgid ""
+"Configuring and setting up the :code:`Dockerfile` as well the "
+"configuration for the devcontainer can be a bit more involved. The good "
+"thing is you want have to do it. Usually it should be enough to install "
+"Docker on your system and ensure its available on your command line. "
+"Additionally, install the `VSCode Containers Extension <vscode:extension"
+"/ms-vscode-remote.remote-containers>`_."
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:19
+msgid ""
+"Now you should be good to go. When starting VSCode, it will ask you to "
+"run in the container environment and - if you confirm - automatically "
+"build the container and use it. To manually instruct VSCode to use the "
+"devcontainer, you can, after installing the extension, click the green "
+"area in the bottom left corner of your VSCode window and select the "
+"option *(Re)Open Folder in Container*."
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:21
+msgid ""
+"In some cases your setup might be more involved. For those cases consult "
+"the following sources:"
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:23
+msgid ""
+"`Developing inside a Container "
+"<https://code.visualstudio.com/docs/remote/containers#_system-"
+"requirements>`_"
+msgstr ""
+
+#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:24
+msgid ""
+"`Remote development in Containers "
+"<https://code.visualstudio.com/docs/remote/containers-tutorial>`_"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:2
+msgid "Install development versions"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:5
+msgid "Install development versions of Flower"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:8
+msgid "Using Poetry (recommended)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:10
+msgid ""
+"Install a ``flwr`` pre-release from PyPI: update the ``flwr`` dependency "
+"in ``pyproject.toml`` and then reinstall (don't forget to delete "
+"``poetry.lock`` (``rm poetry.lock``) before running ``poetry install``)."
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:12
+msgid ""
+"``flwr = { version = \"1.0.0a0\", allow-prereleases = true }`` (without "
+"extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:13
+msgid ""
+"``flwr = { version = \"1.0.0a0\", allow-prereleases = true, extras = "
+"[\"simulation\"] }`` (with extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:15
+msgid ""
+"Install ``flwr`` from a local copy of the Flower source code via "
+"``pyproject.toml``:"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:17
+msgid "``flwr = { path = \"../../\", develop = true }`` (without extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:18
+msgid ""
+"``flwr = { path = \"../../\", develop = true, extras = [\"simulation\"] "
+"}`` (with extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:20
+msgid "Install ``flwr`` from a local wheel file via ``pyproject.toml``:"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:22
+msgid ""
+"``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\" }`` (without"
+" extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:23
+msgid ""
+"``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\", extras = "
+"[\"simulation\"] }`` (with extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:25
+msgid ""
+"Please refer to the Poetry documentation for further details: `Poetry "
+"Dependency Specification <https://python-poetry.org/docs/dependency-"
+"specification/>`_"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:28
+msgid "Using pip (recommended on Colab)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:30
+msgid "Install a ``flwr`` pre-release from PyPI:"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:32
+msgid "``pip install -U --pre flwr`` (without extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:33
+msgid "``pip install -U --pre flwr[simulation]`` (with extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:35
+msgid ""
+"Python packages can be installed from git repositories. Use one of the "
+"following commands to install the Flower directly from GitHub."
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:37
+msgid "Install ``flwr`` from the default GitHub branch (``main``):"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:39
+msgid ""
+"``pip install flwr@git+https://github.com/adap/flower.git`` (without "
+"extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:40
+msgid ""
+"``pip install flwr[simulation]@git+https://github.com/adap/flower.git`` "
+"(with extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:42
+msgid "Install ``flwr`` from a specific GitHub branch (``branch-name``):"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:44
+msgid ""
+"``pip install flwr@git+https://github.com/adap/flower.git@branch-name`` "
+"(without extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:45
+msgid ""
+"``pip install flwr[simulation]@git+https://github.com/adap/flower.git"
+"@branch-name`` (with extras)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:49
+msgid "Open Jupyter Notebooks on Google Colab"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:51
+msgid ""
+"Open the notebook ``doc/source/tutorial-get-started-with-flower-"
+"pytorch.ipynb``:"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:53
+msgid ""
+"https://colab.research.google.com/github/adap/flower/blob/main/doc/source"
+"/tutorial-get-started-with-flower-pytorch.ipynb"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:55
+msgid ""
+"Open a development version of the same notebook from branch `branch-name`"
+" by changing ``main`` to ``branch-name`` (right after ``blob``):"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:57
+msgid ""
+"https://colab.research.google.com/github/adap/flower/blob/branch-"
+"name/doc/source/tutorial-get-started-with-flower-pytorch.ipynb"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:59
+msgid "Install a `whl` on Google Colab:"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:61
+msgid ""
+"In the vertical icon grid on the left hand side, select ``Files`` > "
+"``Upload to session storage``"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:62
+msgid "Upload the whl (e.g., ``flwr-1.6.0-py3-none-any.whl``)"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:63
+msgid ""
+"Change ``!pip install -q 'flwr[simulation]' torch torchvision "
+"matplotlib`` to ``!pip install -q 'flwr-1.6.0-py3-none-"
+"any.whl[simulation]' torch torchvision matplotlib``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:2
+msgid "Release Flower"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:4
+msgid ""
+"This document describes the current release process. It may or may not "
+"change in the future."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:7
+msgid "Before the release"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:9
+msgid ""
+"Update the changelog (``changelog.md``) with all relevant changes that "
+"happened after the last release. If the last release was tagged "
+"``v1.2.0``, you can use the following URL to see all commits that got "
+"merged into ``main`` since then:"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:11
+msgid ""
+"`GitHub: Compare v1.2.0...main "
+"<https://github.com/adap/flower/compare/v1.2.0...main>`_"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:13
+msgid ""
+"Thank the authors who contributed since the last release. This can be "
+"done by running the ``./dev/add-shortlog.sh`` convenience script (it can "
+"be ran multiple times and will update the names in the list if new "
+"contributors were added in the meantime)."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:16
+msgid "During the release"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:18
+msgid ""
+"The version number of a release is stated in ``pyproject.toml``. To "
+"release a new version of Flower, the following things need to happen (in "
+"that order):"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:20
+msgid ""
+"Update the ``changelog.md`` section header ``Unreleased`` to contain the "
+"version number and date for the release you are building. Create a pull "
+"request with the change."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:21
+msgid ""
+"Tag the release commit with the version number as soon as the PR is "
+"merged: ``git tag v0.12.3``, then ``git push --tags``. This will create a"
+" draft release on GitHub containing the correct artifacts and the "
+"relevant part of the changelog."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:22
+msgid "Check the draft release on GitHub, and if everything is good, publish it."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:25
+msgid "After the release"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:27
+msgid "Create a pull request which contains the following changes:"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:29
+msgid "Increase the minor version in ``pyproject.toml`` by one."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:30
+msgid "Update all files which contain the current version number if necessary."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:31
+msgid "Add a new ``Unreleased`` section in ``changelog.md``."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:33
+msgid ""
+"Merge the pull request on the same day (i.e., before a new nighly release"
+" gets published to PyPI)."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:36
+msgid "Publishing a pre-release"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:39
+msgid "Pre-release naming"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:41
+msgid ""
+"PyPI supports pre-releases (alpha, beta, release candiate). Pre-releases "
+"MUST use one of the following naming patterns:"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:43
+msgid "Alpha: ``MAJOR.MINOR.PATCHaN``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:44
+msgid "Beta: ``MAJOR.MINOR.PATCHbN``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:45
+msgid "Release candiate (RC): ``MAJOR.MINOR.PATCHrcN``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:47
+msgid "Examples include:"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:49
+msgid "``1.0.0a0``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:50
+msgid "``1.0.0b0``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:51
+msgid "``1.0.0rc0``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:52
+msgid "``1.0.0rc1``"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:54
+msgid ""
+"This is in line with PEP-440 and the recommendations from the Python "
+"Packaging Authority (PyPA):"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:57
+msgid "`PEP-440 <https://peps.python.org/pep-0440/>`_"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:58
+msgid ""
+"`PyPA Choosing a versioning scheme "
+"<https://packaging.python.org/en/latest/guides/distributing-packages-"
+"using-setuptools/#choosing-a-versioning-scheme>`_"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:60
+msgid ""
+"Note that the approach defined by PyPA is not compatible with SemVer "
+"2.0.0 spec, for details consult the `Semantic Versioning Specification "
+"<https://semver.org/spec/v2.0.0.html#spec-item-11>`_ (specifically item "
+"11 on precedence)."
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:63
+msgid "Pre-release classification"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:65
+msgid "Should the next pre-release be called alpha, beta, or release candidate?"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:67
+msgid ""
+"RC: feature complete, no known issues (apart from issues that are "
+"classified as \"won't fix\" for the next stable release) - if no issues "
+"surface this will become the next stable release"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:68
+msgid "Beta: feature complete, allowed to have known issues"
+msgstr ""
+
+#: ../../source/contributor-how-to-release-flower.rst:69
+msgid "Alpha: not feature complete, allowed to have known issues"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:2
+msgid "Set up a virtual env"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:4
+msgid ""
+"It is recommended to run your Python setup within a virtual environment. "
+"This guide shows three different examples how to create a virtual "
+"environment with pyenv virtualenv, poetry, or Anaconda. You can follow "
+"the instructions or choose your preferred setup."
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:9
+msgid "Python Version"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:11
+#: ../../source/how-to-install-flower.rst:8
+msgid ""
+"Flower requires at least `Python 3.8 <https://docs.python.org/3.8/>`_, "
+"but `Python 3.10 <https://docs.python.org/3.10/>`_ or above is "
+"recommended."
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:14
+msgid "Virutualenv with Pyenv/Virtualenv"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:16
+msgid ""
+"One of the recommended virtual environment is `pyenv "
+"<https://github.com/pyenv/pyenv>`_/`virtualenv <https://github.com/pyenv"
+"/pyenv-virtualenv>`_. Please see `Flower examples "
+"<https://github.com/adap/flower/tree/main/examples/>`_ for details."
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:18
+msgid ""
+"Once Pyenv is set up, you can use it to install `Python Version 3.10 "
+"<https://docs.python.org/3.10/>`_ or above:"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:24
+msgid "Create the virtualenv with:"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:31
+msgid "Activate the virtualenv by running the following command:"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:39
+msgid "Virtualenv with Poetry"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:41
+msgid ""
+"The Flower examples are based on `Poetry <https://python-"
+"poetry.org/docs/>`_ to manage dependencies. After installing Poetry you "
+"simply create a virtual environment with:"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:47
+msgid ""
+"If you open a new terminal you can activate the previously created "
+"virtual environment with the following command:"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:55
+msgid "Virtualenv with Anaconda"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:57
+msgid ""
+"If you prefer to use Anaconda for your virtual environment then install "
+"and setup the `conda <https://docs.conda.io/projects/conda/en/latest"
+"/user-guide/install/index.html>`_  package. After setting it up you can "
+"create a virtual environment with:"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:63
+msgid "and activate the virtual environment with:"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:71
+msgid "And then?"
+msgstr ""
+
+#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:73
+msgid ""
+"As soon as you created your virtual environment you clone one of the "
+"`Flower examples <https://github.com/adap/flower/tree/main/examples/>`_."
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:2
+msgid "Write documentation"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:6
+msgid "Project layout"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:8
+msgid ""
+"The Flower documentation lives in the ``doc`` directory. The Sphinx-based"
+" documentation system supports both reStructuredText (``.rst`` files) and"
+" Markdown (``.md`` files)."
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:10
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:119
+msgid ""
+"Note that, in order to build the documentation locally (with ``poetry run"
+" make html``, like described below), `Pandoc "
+"<https://pandoc.org/installing.html>_` needs to be installed on the "
+"system."
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:14
+msgid "Edit an existing page"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:16
+msgid "Edit an existing ``.rst`` (or ``.md``) file under ``doc/source/``"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:17
+#: ../../source/contributor-how-to-write-documentation.rst:27
+msgid "Compile the docs: ``cd doc``, then ``poetry run make html``"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:18
+#: ../../source/contributor-how-to-write-documentation.rst:28
+msgid "Open ``doc/build/html/index.html`` in the browser to check the result"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:22
+msgid "Create a new page"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:24
+msgid "Add new ``.rst`` file under ``doc/source/``"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:25
+msgid "Add content to the new ``.rst`` file"
+msgstr ""
+
+#: ../../source/contributor-how-to-write-documentation.rst:26
+msgid "Link to the new rst from ``index.rst``"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:2
+msgid "Good first contributions"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:4
+msgid ""
+"We welcome contributions to Flower! However, it is not always easy to "
+"know where to start. We therefore put together a few recommendations on "
+"where to start to increase your chances of getting your PR accepted into "
+"the Flower codebase."
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:11
+msgid "Where to start"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:13
+msgid ""
+"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:"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:17
+msgid "Documentation: What's missing? What could be expressed more clearly?"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:18
+msgid "Baselines: See below."
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:19
+msgid "Examples: See below."
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:23
+msgid "Request for Flower Baselines"
+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"
+"/contributing-baselines.html>`_."
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:27
+msgid ""
+"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!"
+msgstr ""
+
+#: ../../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 ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:34
+msgid "Request for examples"
+msgstr ""
+
+#: ../../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 ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:40
+msgid "Llama 2 fine-tuning, with Hugging Face Transformers and PyTorch"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:41
+msgid "XGBoost"
+msgstr ""
+
+#: ../../source/contributor-ref-good-first-contributions.rst:42
+msgid "Android ONNX on-device training"
+msgstr ""
+
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:2
+msgid "Secure Aggregation Protocols"
+msgstr ""
+
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:4
+msgid ""
+"Include SecAgg, SecAgg+, and LightSecAgg protocol. The LightSecAgg "
+"protocol has not been implemented yet, so its diagram and abstraction may"
+" not be accurate in practice. The SecAgg protocol can be considered as a "
+"special case of the SecAgg+ protocol."
+msgstr ""
+
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:8
+msgid "The :code:`SecAgg+` abstraction"
+msgstr ""
+
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:10
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:161
+msgid ""
+"In this implementation, each client will be assigned with a unique index "
+"(int) for secure aggregation, and thus many python dictionaries used have"
+" keys of int type rather than ClientProxy type."
+msgstr ""
+
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:65
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:198
+msgid ""
+"The Flower server will execute and process received results in the "
+"following order:"
+msgstr ""
+
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:159
+msgid "The :code:`LightSecAgg` abstraction"
+msgstr ""
+
+#: ../../source/contributor-ref-secure-aggregation-protocols.rst:271
+msgid "Types"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:2
+msgid "Contribute on GitHub"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:4
+msgid ""
+"This guide is for people who want to get involved with Flower, but who "
+"are not used to contributing to GitHub projects."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:6
+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/getting-started-for-contributors.html>`_ and "
+"examples of `good first contributions <https://flower.dev/docs/good-"
+"first-contributions.html>`_."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:12
+msgid "Setting up the repository"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:23
+msgid "**Create a GitHub account and setup Git**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:15
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:18
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:20
+msgid ""
+"If you haven't already, you will need to create an account on `GitHub "
+"<https://github.com/signup>`_."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:22
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:34
+msgid "**Forking the Flower repository**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:26
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:31
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:49
+msgid "**Cloning your forked repository**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:37
+msgid ""
+"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, this will give you the "
+"ability to copy the HTTPS link of the repository."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:43
+msgid ""
+"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:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:49
+msgid ""
+"This will create a `flower/` (or the name of your fork if you renamed it)"
+" folder in the current working directory."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:68
+msgid "**Add origin**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:52
+msgid "You can then go into the repository folder:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:58
+msgid ""
+"And here we will need to add an origin to our repository. The origin is "
+"the \\<URL\\> of the remote fork repository. To obtain it, we can do as "
+"previously mentioned by going to our fork repository on our GitHub "
+"account and copying the link."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:63
+msgid ""
+"Once the \\<URL\\> is copied, we can type the following command in our "
+"terminal:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:92
+msgid "**Add upstream**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:71
+msgid ""
+"Now we will add an upstream address to our repository. Still in the same "
+"directroy, we must run the following command:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:78
+msgid "The following diagram visually explains what we did in the previous steps:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:82
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:86
+msgid ""
+"To make sure our local version of the fork is up-to-date with the latest "
+"changes from the Flower repository, we can execute the following command:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:95
+msgid "Setting up the coding environment"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:97
+msgid ""
+"This can be achieved by following this `getting started guide for "
+"contributors`_ (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!"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:102
+msgid "Making changes"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:104
+msgid ""
+"Before making any changes make sure you are up-to-date with your "
+"repository:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:110
+msgid "And with Flower's repository:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:124
+msgid "**Create a new branch**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:117
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:120
+msgid ""
+"To do so, just run the following command inside the repository's "
+"directory:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:127
+msgid "**Make changes**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:127
+msgid "Write great code and create wonderful changes using your favorite editor!"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:140
+msgid "**Test and format your code**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:130
+msgid ""
+"Don't forget to test and format your code! Otherwise your code won't be "
+"able to be merged into the Flower repository. This is done so the "
+"codebase stays consistent and easy to understand."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:133
+msgid "To do so, we have written a few scripts that you can execute:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:152
+msgid "**Stage changes**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:143
+msgid ""
+"Before creating a commit that will update your history, you must specify "
+"to Git which files it needs to take into account."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:145
+msgid "This can be done with:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:151
+msgid ""
+"To check which files have been modified compared to the last version "
+"(last commit) and to see which files are staged for commit, you can use "
+"the :code:`git status` command."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:162
+msgid "**Commit changes**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:155
+msgid ""
+"Once you have added all the files you wanted to commit using :code:`git "
+"add`, you can finally create your commit using this command:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:161
+msgid ""
+"The \\<commit_message\\> is there to explain to others what the commit "
+"does. It should be written in an imperative style and be concise. An "
+"example would be :code:`git commit -m \"Add images to README\"`."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:173
+msgid "**Push the changes to the fork**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:165
+msgid ""
+"Once we have committed our changes, we have effectively updated our local"
+" history, but GitHub has no way of knowing this unless we push our "
+"changes to our origin's remote address:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:172
+msgid ""
+"Once this is done, you will see on the GitHub that your forked repo was "
+"updated with the changes you have made."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:176
+msgid "Creating and merging a pull request (PR)"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:203
+msgid "**Create the PR**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:179
+msgid ""
+"Once you have pushed changes, on the GitHub webpage of your repository "
+"you should see the following message:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:183
+msgid "Otherwise you can always find this option in the `Branches` page."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:185
+msgid ""
+"Once you click the `Compare & pull request` button, you should see "
+"something similar to this:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:189
+msgid "At the top you have an explanation of which branch will be merged where:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:193
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:195
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:198
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:201
+msgid ""
+"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:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:206
+msgid "**Making new changes**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:206
+msgid ""
+"Once the PR has been opened (as draft or not), you can still push new "
+"commits to it the same way we did before, by making changes to the branch"
+" associated with the PR."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:228
+msgid "**Review the PR**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:209
+msgid ""
+"Once the PR has been opened or once the draft PR has been marked as "
+"ready, a review from code owners will be automatically requested:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:213
+msgid ""
+"Code owners will then look into the code, ask questions, request changes "
+"or validate the PR."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:215
+msgid "Merging will be blocked if there are ongoing requested changes."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:219
+msgid ""
+"To resolve them, just push the necessary changes to the branch associated"
+" with the PR:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:223
+msgid "And resolve the conversation:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:227
+msgid ""
+"Once all the conversations have been resolved, you can re-request a "
+"review."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:248
+msgid "**Once the PR is merged**"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:231
+msgid ""
+"If all the automatic tests have passed and reviewers have no more changes"
+" to request, they can approve the PR and merge it."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:235
+msgid ""
+"Once it is merged, you can delete the branch on GitHub (a button should "
+"appear to do so) and also delete it locally by doing:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:242
+msgid "Then you should update your forked repository by doing:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:251
+msgid "Example of first contribution"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:254
+msgid "Problem"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:256
+msgid ""
+"For our documentation, we’ve started to use the `Diàtaxis framework "
+"<https://diataxis.fr/>`_."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:258
+msgid ""
+"Our “How to” guides should have titles that continue the sencence “How to"
+" …”, for example, “How to upgrade to Flower 1.0”."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:260
+msgid ""
+"Most of our guides do not follow this new format yet, and changing their "
+"title is (unfortunately) more involved than one might think."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:262
+msgid ""
+"This issue is about changing the title of a doc from present continious "
+"to present simple."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:264
+msgid ""
+"Let's take the example of “Saving Progress” which we changed to “Save "
+"Progress”. Does this pass our check?"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:266
+msgid "Before: ”How to saving progress” ❌"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:268
+msgid "After: ”How to save progress” ✅"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:271
+msgid "Solution"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:273
+msgid ""
+"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:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:275
+msgid "Find the source file in `doc/source`"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:276
+msgid ""
+"Make the change in the `.rst` file (beware, the dashes under the title "
+"should be the same length as the title itself)"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:277
+msgid ""
+"Build the docs and check the result: `<https://flower.dev/docs/writing-"
+"documentation.html#edit-an-existing-page>`_"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:280
+msgid "Rename file"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:282
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:285
+msgid "Here’s how to change the file name:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:287
+msgid "Change the file name to `save-progress.rst`"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:288
+msgid "Add a redirect rule to `doc/source/conf.py`"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:290
+msgid ""
+"This will cause a redirect from `saving-progress.html` to `save-"
+"progress.html`, old links will continue to work."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:293
+msgid "Apply changes in the index file"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:295
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:298
+msgid "Find and modify the file name in `index.rst`"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:301
+msgid "Open PR"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:303
+msgid ""
+"Commit the changes (commit messages are always imperative: “Do "
+"something”, in this case “Change …”)"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:304
+msgid "Push the changes to your fork"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:305
+msgid "Open a PR (as shown above)"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:306
+msgid "Wait for it to be approved!"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:307
+msgid "Congrats! 🥳 You're now officially a Flower contributor!"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:311
+msgid "How to write a good PR title"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:313
+msgid ""
+"A well-crafted PR title helps team members quickly understand the purpose"
+" and scope of the changes being proposed. Here's a guide to help you "
+"write a good GitHub PR title:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:315
+msgid ""
+"1. Be Clear and Concise: Provide a clear summary of the changes in a "
+"concise manner. 1. Use Actionable Verbs: Start with verbs like \"Add,\" "
+"\"Update,\" or \"Fix\" to indicate the purpose. 1. Include Relevant "
+"Information: Mention the affected feature or module for context. 1. Keep "
+"it Short: Avoid lengthy titles for easy readability. 1. Use Proper "
+"Capitalization and Punctuation: Follow grammar rules for clarity."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:321
+msgid ""
+"Let's start with a few examples for titles that should be avoided because"
+" they do not provide meaningful information:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:323
+msgid "Implement Algorithm"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:324
+msgid "Database"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:325
+msgid "Add my_new_file.py to codebase"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:326
+msgid "Improve code in module"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:327
+msgid "Change SomeModule"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:329
+msgid ""
+"Here are a few positive examples which provide helpful information "
+"without repeating how they do it, as that is already visible in the "
+"\"Files changed\" section of the PR:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:331
+msgid "Update docs banner to mention Flower Summit 2023"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:332
+msgid "Remove unnecessary XGBoost dependency"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:333
+msgid "Remove redundant attributes in strategies subclassing FedAvg"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:334
+msgid "Add CI job to deploy the staging system when the `main` branch changes"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:335
+msgid ""
+"Add new amazing library which will be used to improve the simulation "
+"engine"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:339
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:548
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:946
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:747
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:713
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:367
+msgid "Next steps"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:341
+msgid ""
+"Once you have made your first PR, and want to contribute more, be sure to"
+" check out the following :"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:343
+msgid ""
+"`Good first contributions <https://flower.dev/docs/framework/contributor-"
+"ref-good-first-contributions.html>`_, where you should particularly look "
+"into the :code:`baselines` contributions."
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:2
+msgid "Get started as a contributor"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5
+msgid "Prerequisites"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:7
+msgid "`Python 3.7 <https://docs.python.org/3.7/>`_ or above"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:8
+msgid "`Poetry 1.3 <https://python-poetry.org/>`_ or above"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:9
+msgid "(Optional) `pyenv <https://github.com/pyenv/pyenv>`_"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:10
+msgid "(Optional) `pyenv-virtualenv <https://github.com/pyenv/pyenv-virtualenv>`_"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:12
+msgid ""
+"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/>`_."
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:18
+msgid "Developer Machine Setup"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:20
+msgid ""
+"First, clone the `Flower repository <https://github.com/adap/flower>`_ "
+"from GitHub::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:26
+msgid ""
+"Second, create a virtual environment (and activate it). If you chose to "
+"use :code:`pyenv` (with the :code:`pyenv-virtualenv` plugin) and already "
+"have it installed , you can use the following convenience script (by "
+"default it will use :code:`Python 3.8.17`, but you can change it by "
+"providing a specific :code:`<version>`)::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:33
+msgid ""
+"If you don't have :code:`pyenv` installed, you can use the following "
+"script that will install pyenv, set it up and create the virtual "
+"environment (with :code:`Python 3.8.17` by default)::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:39
+msgid ""
+"Third, install the Flower package in development mode (think :code:`pip "
+"install -e`) along with all necessary dependencies::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:46
+msgid "Convenience Scripts"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:48
+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:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:54
+msgid "Create/Delete Virtual Environment"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:62
+msgid "Compile ProtoBuf Definitions"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:69
+msgid "Auto-Format Code"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:76
+msgid "Run Linters and Tests"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:83
+msgid "Run Github Actions (CI) locally"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:85
+msgid ""
+"Developers could run the full set of Github Actions workflows under their"
+" local environment by using `Act <https://github.com/nektos/act>_`. "
+"Please refer to the installation instructions under the linked repository"
+" and run the next command under Flower main cloned repository folder::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:92
+msgid ""
+"The Flower default workflow would run by setting up the required Docker "
+"machines underneath."
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:97
+msgid "Build Release"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:99
+msgid ""
+"Flower uses Poetry to build releases. The necessary command is wrapped in"
+" a simple script::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:104
+msgid ""
+"The resulting :code:`.whl` and :code:`.tar.gz` releases will be stored in"
+" the :code:`/dist` subdirectory."
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:109
+msgid "Build Documentation"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:111
+msgid ""
+"Flower's documentation uses `Sphinx <https://www.sphinx-doc.org/>`_. "
+"There's no convenience script to re-build the documentation yet, but it's"
+" pretty easy::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:117
+msgid "This will generate HTML documentation in ``doc/build/html``."
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:2
+msgid "Example: FedBN in PyTorch - From Centralized To Federated"
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:4
+msgid ""
+"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>`_."
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:9
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:10
+msgid "Centralized Training"
+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.html>`_. The only thing to do is modifying the file called "
+":code:`cifar.py`, revised part is shown below:"
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:13
+msgid ""
+"The model architecture defined in class Net() is added with Batch "
+"Normalization layers accordingly."
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:41
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:157
+msgid "You can now run your machine learning workload:"
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:47
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:51
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:167
+msgid "Federated Training"
+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-"
+"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."
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:56
+msgid ""
+"Our example consists of one *server* and two *clients*. In FedBN, "
+":code:`server.py` keeps unchanged, we can start the server directly."
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:62
+msgid ""
+"Finally, we will revise our *client* logic by changing "
+":code:`get_parameters` and :code:`set_parameters` in :code:`client.py`, "
+"we will exclude batch normalization parameters from model parameter list "
+"when sending to or receiving from the server."
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:85
+msgid "Now, you can now open two additional terminal windows and run"
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:91
+msgid ""
+"in each window (make sure that the server is still running before you do "
+"so) and see your (previously centralized) PyTorch project run federated "
+"learning with FedBN strategy across two clients. Congratulations!"
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:94
+#: ../../source/example-jax-from-centralized-to-federated.rst:277
+#: ../../source/example-mxnet-walk-through.rst:356
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:310
+#: ../../source/tutorial-quickstart-jax.rst:283
+msgid "Next Steps"
+msgstr ""
+
+#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:96
+msgid ""
+"The full source code for this example can be found `here "
+"<https://github.com/adap/flower/blob/main/examples/pytorch-from-"
+"centralized-to-federated>`_. Our example is of course somewhat over-"
+"simplified because both clients load the exact same dataset, which isn't "
+"realistic. You're now prepared to explore this topic further. How about "
+"using different subsets of CIFAR-10 on each client? How about adding more"
+" clients?"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:2
+msgid "Example: JAX - Run JAX Federated"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:4
+#: ../../source/tutorial-quickstart-jax.rst:10
+msgid ""
+"This tutorial will show you how to use Flower to build a federated "
+"version of an existing JAX workload. We are using JAX to train a linear "
+"regression model on a scikit-learn dataset. We will structure the example"
+" similar to our `PyTorch - From Centralized To Federated "
+"<https://github.com/adap/flower/blob/main/examples/pytorch-from-"
+"centralized-to-federated>`_ walkthrough. First, we build a centralized "
+"training approach based on the `Linear Regression with JAX "
+"<https://coax.readthedocs.io/en/latest/examples/linear_regression/jax.html>`_"
+" tutorial`. Then, we build upon the centralized training code to run the "
+"training in a federated fashion."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:10
+#: ../../source/tutorial-quickstart-jax.rst:16
+msgid ""
+"Before we start building our JAX example, we need install the packages "
+":code:`jax`, :code:`jaxlib`, :code:`scikit-learn`, and :code:`flwr`:"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:18
+#: ../../source/tutorial-quickstart-jax.rst:24
+msgid "Linear Regression with JAX"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:20
+#: ../../source/tutorial-quickstart-jax.rst:26
+msgid ""
+"We begin with a brief description of the centralized training code based "
+"on a :code:`Linear Regression` model. If you want a more in-depth "
+"explanation of what's going on then have a look at the official `JAX "
+"documentation <https://jax.readthedocs.io/>`_."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:23
+#: ../../source/tutorial-quickstart-jax.rst:29
+msgid ""
+"Let's create a new file called :code:`jax_training.py` with all the "
+"components required for a traditional (centralized) linear regression "
+"training. First, the JAX packages :code:`jax` and :code:`jaxlib` need to "
+"be imported. In addition, we need to import :code:`sklearn` since we use "
+":code:`make_regression` for the dataset and :code:`train_test_split` to "
+"split the dataset into a training and test set. You can see that we do "
+"not yet import the :code:`flwr` package for federated learning. This will"
+" be done later."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:37
+#: ../../source/tutorial-quickstart-jax.rst:43
+msgid ""
+"The :code:`load_data()` function loads the mentioned training and test "
+"sets."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:47
+#: ../../source/tutorial-quickstart-jax.rst:53
+msgid ""
+"The model architecture (a very simple :code:`Linear Regression` model) is"
+" defined in :code:`load_model()`."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:59
+#: ../../source/tutorial-quickstart-jax.rst:65
+msgid ""
+"We now need to define the training (function :code:`train()`), which "
+"loops over the training set and measures the loss (function "
+":code:`loss_fn()`) for each batch of training examples. The loss function"
+" is separate since JAX takes derivatives with a :code:`grad()` function "
+"(defined in the :code:`main()` function and called in :code:`train()`)."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:77
+#: ../../source/tutorial-quickstart-jax.rst:83
+msgid ""
+"The evaluation of the model is defined in the function "
+":code:`evaluation()`. The function takes all test examples and measures "
+"the loss of the linear regression model."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:88
+#: ../../source/tutorial-quickstart-jax.rst:94
+msgid ""
+"Having defined the data loading, model architecture, training, and "
+"evaluation we can put everything together and train our model using JAX. "
+"As already mentioned, the :code:`jax.grad()` function is defined in "
+":code:`main()` and passed to :code:`train()`."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:105
+#: ../../source/tutorial-quickstart-jax.rst:111
+msgid "You can now run your (centralized) JAX linear regression workload:"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:111
+#: ../../source/tutorial-quickstart-jax.rst:117
+msgid ""
+"So far this should all look fairly familiar if you've used JAX before. "
+"Let's take the next step and use what we've built to create a simple "
+"federated learning system consisting of one server and two clients."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:115
+#: ../../source/tutorial-quickstart-jax.rst:121
+msgid "JAX meets Flower"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:117
+#: ../../source/tutorial-quickstart-jax.rst:123
+msgid ""
+"The concept of federating an existing workload is always the same and "
+"easy to understand. We have to start a *server* and then use the code in "
+":code:`jax_training.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 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."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:123
+#: ../../source/example-mxnet-walk-through.rst:204
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:181
+#: ../../source/tutorial-quickstart-jax.rst:129
+msgid ""
+"Our example consists of one *server* and two *clients*. Let's set up "
+":code:`server.py` first. The *server* needs to import the Flower package "
+":code:`flwr`. Next, we use the :code:`start_server` function to start a "
+"server and tell it to perform three rounds of federated learning."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:133
+#: ../../source/example-mxnet-walk-through.rst:214
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:191
+#: ../../source/tutorial-quickstart-jax.rst:139
+msgid "We can already start the *server*:"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:139
+#: ../../source/tutorial-quickstart-jax.rst:145
+msgid ""
+"Finally, we will define our *client* logic in :code:`client.py` and build"
+" upon the previously defined JAX training in :code:`jax_training.py`. Our"
+" *client* needs to import :code:`flwr`, but also :code:`jax` and "
+":code:`jaxlib` to update the parameters on our JAX model:"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:154
+#: ../../source/tutorial-quickstart-jax.rst:160
+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:`FlowerClient`. :code:`NumPyClient` is slightly "
+"easier to implement than :code:`Client` if you use a framework with good "
+"NumPy interoperability (like JAX) because it avoids some of the "
+"boilerplate that would otherwise be necessary. :code:`FlowerClient` 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:"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:161
+#: ../../source/example-mxnet-walk-through.rst:242
+#: ../../source/tutorial-quickstart-jax.rst:167
+msgid ":code:`set_parameters (optional)`"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:160
+#: ../../source/example-mxnet-walk-through.rst:241
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:219
+#: ../../source/tutorial-quickstart-jax.rst:166
+msgid ""
+"set the model parameters on the local model that are received from the "
+"server"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:161
+#: ../../source/tutorial-quickstart-jax.rst:167
+msgid "transform parameters to NumPy :code:`ndarray`'s"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:162
+#: ../../source/example-mxnet-walk-through.rst:243
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:220
+#: ../../source/tutorial-quickstart-jax.rst:168
+msgid ""
+"loop over the list of model parameters received as NumPy "
+":code:`ndarray`'s (think list of neural network layers)"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:163
+#: ../../source/example-mxnet-walk-through.rst:244
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:221
+#: ../../source/tutorial-quickstart-jax.rst:169
+#: ../../source/tutorial-quickstart-mxnet.rst:169
+#: ../../source/tutorial-quickstart-pytorch.rst:155
+#: ../../source/tutorial-quickstart-scikitlearn.rst:108
+msgid ":code:`get_parameters`"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:164
+#: ../../source/example-mxnet-walk-through.rst:245
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:222
+#: ../../source/tutorial-quickstart-jax.rst:170
+msgid ""
+"get the model parameters and return them as a list of NumPy "
+":code:`ndarray`'s (which is what :code:`flwr.client.NumPyClient` expects)"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:167
+#: ../../source/example-mxnet-walk-through.rst:248
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:225
+#: ../../source/tutorial-quickstart-jax.rst:173
+#: ../../source/tutorial-quickstart-mxnet.rst:175
+#: ../../source/tutorial-quickstart-pytorch.rst:161
+#: ../../source/tutorial-quickstart-scikitlearn.rst:115
+msgid ":code:`fit`"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:166
+#: ../../source/example-jax-from-centralized-to-federated.rst:170
+#: ../../source/example-mxnet-walk-through.rst:247
+#: ../../source/example-mxnet-walk-through.rst:251
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:224
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:228
+#: ../../source/tutorial-quickstart-jax.rst:172
+#: ../../source/tutorial-quickstart-jax.rst:176
+msgid ""
+"update the parameters of the local model with the parameters received "
+"from the server"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:167
+#: ../../source/example-mxnet-walk-through.rst:248
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:225
+#: ../../source/tutorial-quickstart-jax.rst:173
+msgid "train the model on the local training set"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:168
+#: ../../source/tutorial-quickstart-jax.rst:174
+msgid "get the updated local model parameters and return them to the server"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:172
+#: ../../source/example-mxnet-walk-through.rst:253
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:230
+#: ../../source/tutorial-quickstart-jax.rst:178
+#: ../../source/tutorial-quickstart-mxnet.rst:178
+#: ../../source/tutorial-quickstart-pytorch.rst:164
+#: ../../source/tutorial-quickstart-scikitlearn.rst:118
+msgid ":code:`evaluate`"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:171
+#: ../../source/example-mxnet-walk-through.rst:252
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:229
+#: ../../source/tutorial-quickstart-jax.rst:177
+msgid "evaluate the updated model on the local test set"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:172
+#: ../../source/tutorial-quickstart-jax.rst:178
+msgid "return the local loss to the server"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:174
+#: ../../source/tutorial-quickstart-jax.rst:180
+msgid ""
+"The challenging part is to transform the JAX model parameters from "
+":code:`DeviceArray` to :code:`NumPy ndarray` to make them compatible with"
+" `NumPyClient`."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:176
+#: ../../source/tutorial-quickstart-jax.rst:182
+msgid ""
+"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make"
+" use of the functions :code:`train()` and :code:`evaluate()` previously "
+"defined in :code:`jax_training.py`. So what we really do here is we tell "
+"Flower through our :code:`NumPyClient` subclass which of our already "
+"defined functions to call for training and evaluation. We included type "
+"annotations to give you a better understanding of the data types that get"
+" passed around."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:245
+#: ../../source/tutorial-quickstart-jax.rst:251
+msgid "Having defined the federation process, we can run it."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:268
+#: ../../source/example-mxnet-walk-through.rst:347
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:301
+#: ../../source/tutorial-quickstart-jax.rst:274
+msgid "And that's it. You can now open two additional terminal windows and run"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:274
+#: ../../source/tutorial-quickstart-jax.rst:280
+msgid ""
+"in each window (make sure that the server is still running before you do "
+"so) and see your JAX project run federated learning across two clients. "
+"Congratulations!"
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:279
+#: ../../source/tutorial-quickstart-jax.rst:285
+msgid ""
+"The source code of this example was improved over time and can be found "
+"here: `Quickstart JAX <https://github.com/adap/flower/blob/main/examples"
+"/quickstart-jax>`_. Our example is somewhat over-simplified because both "
+"clients load the same dataset."
+msgstr ""
+
+#: ../../source/example-jax-from-centralized-to-federated.rst:282
+#: ../../source/tutorial-quickstart-jax.rst:288
+msgid ""
+"You're now prepared to explore this topic further. How about using a more"
+" sophisticated model or using a different dataset? How about adding more "
+"clients?"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:2
+msgid "Example: MXNet - Run MXNet Federated"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:4
+msgid ""
+"This tutorial will show you how to use Flower to build a federated "
+"version of an existing MXNet workload. We are using MXNet to train a "
+"Sequential model on the MNIST dataset. We will structure the example "
+"similar to our `PyTorch - From Centralized To Federated "
+"<https://github.com/adap/flower/blob/main/examples/pytorch-from-"
+"centralized-to-federated>`_ walkthrough. MXNet and PyTorch are very "
+"similar and a very good comparison between MXNet and PyTorch is given "
+"`here <https://mxnet.apache.org/versions/1.7.0/api/python/docs/tutorials"
+"/getting-started/to-mxnet/pytorch.html>`_. First, we build a centralized "
+"training approach based on the `Handwritten Digit Recognition "
+"<https://mxnet.apache.org/versions/1.7.0/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_"
+" tutorial. Then, we build upon the centralized training code to run the "
+"training in a federated fashion."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:10
+msgid ""
+"Before we start setting up our MXNet example, we install the "
+":code:`mxnet` and :code:`flwr` packages:"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:19
+msgid "MNIST Training with MXNet"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:21
+msgid ""
+"We begin with a brief description of the centralized training code based "
+"on a :code:`Sequential` model. If you want a more in-depth explanation of"
+" what's going on then have a look at the official `MXNet tutorial "
+"<https://mxnet.apache.org/versions/1.7.0/api/python/docs/tutorials/>`_."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:24
+msgid ""
+"Let's create a new file called:code:`mxnet_mnist.py` with all the "
+"components required for a traditional (centralized) MNIST training. "
+"First, the MXNet package :code:`mxnet` needs to be imported. You can see "
+"that we do not yet import the :code:`flwr` package for federated "
+"learning. This will be done later."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:42
+msgid "The :code:`load_data()` function loads the MNIST training and test sets."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:57
+msgid ""
+"As already mentioned, we will use the MNIST dataset for this machine "
+"learning workload. The model architecture (a very simple "
+":code:`Sequential` model) is defined in :code:`model()`."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:70
+msgid ""
+"We now need to define the training (function :code:`train()`) which loops"
+" over the training set and measures the loss for each batch of training "
+"examples."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:123
+msgid ""
+"The evaluation of the model is defined in function :code:`test()`. The "
+"function loops over all test samples and measures the loss and accuracy "
+"of the model based on the test dataset."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:158
+msgid ""
+"Having defined the data loading, model architecture, training, and "
+"evaluation we can put everything together and train our model on MNIST. "
+"Note that the GPU/CPU device for the training and testing is defined "
+"within the :code:`ctx` (context)."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:184
+msgid "You can now run your (centralized) MXNet machine learning workload:"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:190
+msgid ""
+"So far this should all look fairly familiar if you've used MXNet (or even"
+" PyTorch) before. Let's take the next step and use what we've built to "
+"create a simple federated learning system consisting of one server and "
+"two clients."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:194
+msgid "MXNet meets Flower"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:196
+msgid ""
+"So far, it was not easily possible to use MXNet workloads for federated "
+"learning because federated learning is not supported in MXNet. Since "
+"Flower is fully agnostic towards the underlying machine learning "
+"framework, it can be used to federated arbitrary machine learning "
+"workloads. This section will show you how Flower can be used to federate "
+"our centralized MXNet workload."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:198
+msgid ""
+"The concept to federate an existing workload is always the same and easy "
+"to understand. We have to start a *server* and then use the code in "
+":code:`mxnet_mnist.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 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."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:220
+msgid ""
+"Finally, we will define our *client* logic in :code:`client.py` and build"
+" upon the previously defined MXNet training in :code:`mxnet_mnist.py`. "
+"Our *client* needs to import :code:`flwr`, but also :code:`mxnet` to "
+"update the parameters on our MXNet model:"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:235
+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 "
+"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:"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:242
+msgid "transform MXNet :code:`NDArray`'s to NumPy :code:`ndarray`'s"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:249
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:226
+msgid "get the updated local model weights and return them to the server"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:253
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:230
+msgid "return the local loss and accuracy to the server"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:255
+msgid ""
+"The challenging part is to transform the MXNet parameters from "
+":code:`NDArray` to :code:`NumPy Arrays` to make it readable for Flower."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:257
+msgid ""
+"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make"
+" use of the functions :code:`train()` and :code:`test()` previously "
+"defined in :code:`mxnet_mnist.py`. So what we really do here is we tell "
+"Flower through our :code:`NumPyClient` subclass which of our already "
+"defined functions to call for training and evaluation. We included type "
+"annotations to give you a better understanding of the data types that get"
+" passed around."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:319
+msgid ""
+"Having defined data loading, model architecture, training, and evaluation"
+" we can put everything together and train our :code:`Sequential` model on"
+" MNIST."
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:353
+msgid ""
+"in each window (make sure that the server is still running before you do "
+"so) and see your MXNet project run federated learning across two clients."
+" Congratulations!"
+msgstr ""
+
+#: ../../source/example-mxnet-walk-through.rst:358
+msgid ""
+"The full source code for this example: `MXNet: From Centralized To "
+"Federated (Code) <https://github.com/adap/flower/blob/main/examples"
+"/mxnet-from-centralized-to-federated>`_. Our example is of course "
+"somewhat over-simplified because both clients load the exact same "
+"dataset, which isn't realistic. You're now prepared to explore this topic"
+" further. How about using a CNN or using a different dataset? How about "
+"adding more clients?"
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:2
+msgid "Example: PyTorch - From Centralized To Federated"
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:4
+msgid ""
+"This tutorial will show you how to use Flower to build a federated "
+"version of an existing machine learning workload. We are using PyTorch to"
+" train a Convolutional Neural Network on the CIFAR-10 dataset. First, we "
+"introduce this machine learning task with a centralized training approach"
+" based on the `Deep Learning with PyTorch "
+"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_ "
+"tutorial. Then, we build upon the centralized training code to run the "
+"training in a federated fashion."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:12
+msgid ""
+"We begin with a brief description of the centralized CNN training code. "
+"If you want a more in-depth explanation of what's going on then have a "
+"look at the official `PyTorch tutorial "
+"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:15
+msgid ""
+"Let's create a new file called :code:`cifar.py` with all the components "
+"required for a traditional (centralized) training on CIFAR-10. First, all"
+" required packages (such as :code:`torch` and :code:`torchvision`) need "
+"to be imported. You can see that we do not import any package for "
+"federated learning. You can keep all these imports as they are even when "
+"we add the federated learning components at a later point."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:32
+msgid ""
+"As already mentioned we will use the CIFAR-10 dataset for this machine "
+"learning workload. The model architecture (a very simple Convolutional "
+"Neural Network) is defined in :code:`class Net()`."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:56
+msgid ""
+"The :code:`load_data()` function loads the CIFAR-10 training and test "
+"sets. The :code:`transform` normalized the data after loading."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:74
+msgid ""
+"We now need to define the training (function :code:`train()`) which loops"
+" over the training set, measures the loss, backpropagates it, and then "
+"takes one optimizer step for each batch of training examples."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:76
+msgid ""
+"The evaluation of the model is defined in the function :code:`test()`. "
+"The function loops over all test samples and measures the loss of the "
+"model based on the test dataset."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:136
+msgid ""
+"Having defined the data loading, model architecture, training, and "
+"evaluation we can put everything together and train our CNN on CIFAR-10."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:163
+msgid ""
+"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 "
+"simple federated learning system consisting of one server and two "
+"clients."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:169
+msgid ""
+"The simple machine learning project discussed in the previous section "
+"trains the model on a single dataset (CIFAR-10), we call this centralized"
+" learning. This concept of centralized learning, as shown in the previous"
+" section, is probably known to most of you, and many of you have used it "
+"previously. Normally, if you'd want to run machine learning workloads in "
+"a federated fashion, then you'd have to change most of your code and set "
+"everything up from scratch. This can be a considerable effort."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:173
+msgid ""
+"However, with Flower you can evolve your pre-existing code into a "
+"federated learning setup without the need for a major rewrite."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:175
+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 "
+"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."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:197
+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:"
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:213
+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 "
+"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:"
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:219
+msgid ":code:`set_parameters`"
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:232
+msgid ""
+"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make"
+" use of the functions :code:`train()` and :code:`test()` previously "
+"defined in :code:`cifar.py`. So what we really do here is we tell Flower "
+"through our :code:`NumPyClient` subclass which of our already defined "
+"functions to call for training and evaluation. We included type "
+"annotations to give you a better understanding of the data types that get"
+" passed around."
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:280
+msgid ""
+"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_numpy_client()` by pointing it "
+"at the same IP adress we used in :code:`server.py`:"
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:307
+msgid ""
+"in each window (make sure that the server is running before you do so) "
+"and see your (previously centralized) PyTorch project run federated "
+"learning across two clients. Congratulations!"
+msgstr ""
+
+#: ../../source/example-pytorch-from-centralized-to-federated.rst:312
+msgid ""
+"The full source code for this example: `PyTorch: From Centralized To "
+"Federated (Code) <https://github.com/adap/flower/blob/main/examples"
+"/pytorch-from-centralized-to-federated>`_. Our example is, of course, "
+"somewhat over-simplified because both clients load the exact same "
+"dataset, which isn't realistic. You're now prepared to explore this topic"
+" further. How about using different subsets of CIFAR-10 on each client? "
+"How about adding more clients?"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:2
+msgid "Example: Walk-Through PyTorch & MNIST"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:4
+msgid ""
+"In this tutorial we will learn, how to train a Convolutional Neural "
+"Network on MNIST using Flower and PyTorch."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:6
+#: ../../source/tutorial-quickstart-mxnet.rst:14
+#: ../../source/tutorial-quickstart-pytorch.rst:17
+#: ../../source/tutorial-quickstart-scikitlearn.rst:14
+msgid ""
+"Our example consists of one *server* and two *clients* all having the "
+"same model."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:8
+#: ../../source/tutorial-quickstart-pytorch.rst:19
+msgid ""
+"*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*."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:12
+#: ../../source/tutorial-quickstart-pytorch.rst:23
+msgid ""
+"Now that we have a rough idea of what is going on, let's get started. We "
+"first need to install Flower. You can do this by running :"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:18
+msgid ""
+"Since we want to use PyTorch to solve a computer vision task, let's go "
+"ahead an install PyTorch and the **torchvision** library:"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:26
+msgid "Ready... Set... Train!"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:28
+msgid ""
+"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 `Basic MNIST "
+"Example <https://github.com/pytorch/examples/tree/master/mnist>`_. This "
+"will allow you see how easy it is to wrap your code with Flower and begin"
+" training in a federated way. We provide you with two helper scripts, "
+"namely *run-server.sh*, and *run-clients.sh*. Don't be afraid to look "
+"inside, they are simple enough =)."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:31
+msgid ""
+"Go ahead and launch on a terminal the *run-server.sh* script first as "
+"follows:"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:38
+msgid "Now that the server is up and running, go ahead and launch the clients."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:45
+msgid ""
+"Et voilà! You should be seeing the training procedure and, after a few "
+"iterations, the test accuracy for each client."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:66
+msgid "Now, let's see what is really happening inside."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:69
+#: ../../source/tutorial-quickstart-ios.rst:129
+#: ../../source/tutorial-quickstart-mxnet.rst:224
+#: ../../source/tutorial-quickstart-pytorch.rst:203
+#: ../../source/tutorial-quickstart-scikitlearn.rst:157
+#: ../../source/tutorial-quickstart-tensorflow.rst:98
+#: ../../source/tutorial-quickstart-xgboost.rst:306
+msgid "Flower Server"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:71
+msgid ""
+"Inside the server helper script *run-server.sh* you will find the "
+"following code that basically runs the :code:`server.py`"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:78
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:89
+#: ../../source/tutorial-quickstart-ios.rst:34
+#: ../../source/tutorial-quickstart-mxnet.rst:34
+#: ../../source/tutorial-quickstart-pytorch.rst:37
+#: ../../source/tutorial-quickstart-scikitlearn.rst:40
+#: ../../source/tutorial-quickstart-tensorflow.rst:29
+#: ../../source/tutorial-quickstart-xgboost.rst:52
+msgid "Flower Client"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:91
+msgid ""
+"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*."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:100
+msgid ""
+"**cid**: is the client ID. It is an integer that uniquely identifies "
+"client identifier."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:101
+msgid "**sever_address**: String that identifies IP and port of the server."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:102
+msgid ""
+"**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."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:104
+msgid ""
+"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)."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:119
+msgid ""
+"The :code:`PytorchMNISTClient` object when finally passed to "
+":code:`fl.client.start_client` along with the server's address as the "
+"training process begins."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:123
+msgid "A Closer Look"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:125
+msgid ""
+"Now, let's look closely into the :code:`PytorchMNISTClient` inside "
+":code:`flwr_example.quickstart-pytorch.mnist` and see what it is doing:"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:226
+msgid ""
+"The first thing to notice is that :code:`PytorchMNISTClient` instantiates"
+" a CNN model inside its constructor"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:244
+msgid ""
+"The code for the CNN is available under :code:`quickstart-pytorch.mnist` "
+"and it is reproduced below. It is the same network found in `Basic MNIST "
+"Example <https://github.com/pytorch/examples/tree/master/mnist>`_."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:290
+msgid ""
+"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:"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:315
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:317
+msgid ""
+"These functions can both be found inside the same :code:`quickstart-"
+"pytorch.mnist` module:"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:437
+msgid ""
+"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?"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:444
+msgid "Give It a Try"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:445
+msgid ""
+"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:"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:448
+msgid ""
+"Try and change :code:`PytorchMNISTClient` so it can accept different "
+"architectures."
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:449
+msgid "Modify the :code:`train` function so that it accepts different optimizers"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:450
+msgid ""
+"Modify the :code:`test` function so that it proves not only the top-1 "
+"(regular accuracy) but also the top-5 accuracy?"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:451
+msgid ""
+"Go larger! Try to adapt the code to larger images and datasets. Why not "
+"try training on ImageNet with a ResNet-50?"
+msgstr ""
+
+#: ../../source/example-walkthrough-pytorch-mnist.rst:453
+msgid "You are ready now. Enjoy learning in a federated way!"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:2
+msgid "Differential privacy"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:4
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:7
+msgid ""
+"Please note that these components are still experimental, the correct "
+"configuration of DP for a specific task is still an unsolved problem."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:10
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:13
+msgid "DP-FedAvg"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:15
+msgid ""
+"DP-FedAvg, originally proposed by McMahan et al. [mcmahan]_ and extended "
+"by Andrew et al. [andrew]_, is essentially FedAvg with the following "
+"modifications."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:17
+msgid ""
+"**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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:18
+msgid ""
+"**Noising** :  Gaussian noise, calibrated to the clipping threshold, is "
+"added to the average computed at the server."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:20
+msgid ""
+"The distribution of the update norm has been shown to vary from task-to-"
+"task and to evolve as training progresses. Therefore, we use an adaptive "
+"approach [andrew]_ that continuously adjusts the clipping threshold to "
+"track a prespecified quantile of the update norm distribution."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:23
+msgid "Simplifying Assumptions"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:25
+msgid ""
+"We make (and attempt to enforce) a number of assumptions that must be "
+"satisfied to ensure that the training process actually realises the "
+":math:`(\\epsilon, \\delta)` guarantees the user has in mind when "
+"configuring the setup."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:27
+msgid ""
+"**Fixed-size subsampling** :Fixed-size subsamples of the clients must be "
+"taken at each round, as opposed to variable-sized Poisson subsamples."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:28
+msgid ""
+"**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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:29
+msgid ""
+"**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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:31
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:34
+msgid ""
+"These restrictions are in line with constraints imposed by Andrew et al. "
+"[andrew]_."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:37
+msgid "Customizable Responsibility for Noise injection"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:38
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:41
+msgid ""
+"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}`."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:44
+msgid "Wrapper-based approach"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:46
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:49
+msgid "Server-side logic"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:51
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:54
+msgid "DPFedAvgFixed"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:56
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:58
+msgid ""
+":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()`."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:59
+msgid ""
+":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()`."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:62
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:64
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:67
+msgid "DPFedAvgAdaptive"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:69
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:71
+msgid ""
+":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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:73
+msgid ""
+":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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:77
+msgid "Client-side logic"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:79
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:81
+msgid ""
+":code:`dpfedavg_noise_stddev` : Generate and add the specified amount of "
+"noise to the clipped update."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:82
+msgid ""
+":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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:86
+msgid "Performing the :math:`(\\epsilon, \\delta)` analysis"
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:88
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:98
+msgid ""
+"McMahan, H. Brendan, et al. \"Learning differentially private recurrent "
+"language models.\" arXiv preprint arXiv:1710.06963 (2017)."
+msgstr ""
+
+#: ../../source/explanation-differential-privacy.rst:100
+msgid ""
+"Andrew, Galen, et al. \"Differentially private learning with adaptive "
+"clipping.\" Advances in Neural Information Processing Systems 34 (2021): "
+"17455-17466."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:2
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:292
+msgid "Federated evaluation"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:4
+msgid ""
+"There are two main approaches to evaluating models in federated learning "
+"systems: centralized (or server-side) evaluation and federated (or "
+"client-side) evaluation."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:8
+msgid "Centralized Evaluation"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:11
+msgid "Built-In Strategies"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:13
+msgid ""
+"All built-in strategies support centralized evaluation by providing an "
+"evaluation function during initialization. An evaluation function is any "
+"function that can take the current global model parameters as input and "
+"return evaluation results:"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:58
+msgid "Custom Strategies"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:60
+msgid ""
+"The :code:`Strategy` abstraction provides a method called "
+":code:`evaluate` that can directly be used to evaluate the current global"
+" model parameters. The current server implementation calls "
+":code:`evaluate` after parameter aggregation and before federated "
+"evaluation (see next paragraph)."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:65
+msgid "Federated Evaluation"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:68
+msgid "Implementing Federated Evaluation"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:70
+msgid ""
+"Client-side evaluation happens in the :code:`Client.evaluate` method and "
+"can be configured from the server side."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:101
+msgid "Configuring Federated Evaluation"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:103
+msgid ""
+"Federated evaluation can be configured from the server side. Built-in "
+"strategies support the following arguments:"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:105
+msgid ""
+":code:`fraction_evaluate`: a :code:`float` defining the fraction of "
+"clients that will be selected for evaluation. If "
+":code:`fraction_evaluate` is set to :code:`0.1` and :code:`100` clients "
+"are connected to the server, then :code:`10` will be randomly selected "
+"for evaluation. If :code:`fraction_evaluate` is set to :code:`0.0`, "
+"federated evaluation will be disabled."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:106
+msgid ""
+":code:`min_evaluate_clients`: an :code:`int`: the minimum number of "
+"clients to be selected for evaluation. If :code:`fraction_evaluate` is "
+"set to :code:`0.1`, :code:`min_evaluate_clients` is set to 20, and "
+":code:`100` clients are connected to the server, then :code:`20` clients "
+"will be selected for evaluation."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:107
+msgid ""
+":code:`min_available_clients`: an :code:`int` that defines the minimum "
+"number of clients which need to be connected to the server before a round"
+" of federated evaluation can start. If fewer than "
+":code:`min_available_clients` are connected to the server, the server "
+"will wait until more clients are connected before it continues to sample "
+"clients for evaluation."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:108
+msgid ""
+":code:`on_evaluate_config_fn`: a function that returns a configuration "
+"dictionary which will be sent to the selected clients. The function will "
+"be called during each round and provides a convenient way to customize "
+"client-side evaluation from the server side, for example, to configure "
+"the number of validation steps performed."
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:135
+msgid "Evaluating Local Model Updates During Training"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:137
+msgid ""
+"Model parameters can also be evaluated during training. "
+":code:`Client.fit` can return arbitrary evaluation results as a "
+"dictionary:"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:177
+msgid "Full Code Example"
+msgstr ""
+
+#: ../../source/explanation-federated-evaluation.rst:179
+msgid ""
+"For a full code example that uses both centralized and federated "
+"evaluation, see the *Advanced TensorFlow Example* (the same approach can "
+"be applied to workloads implemented in any other framework): "
+"https://github.com/adap/flower/tree/main/examples/advanced-tensorflow"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:10
+msgid "FED Template"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:12
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:12
+msgid "Table of Contents"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:14
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:14
+msgid "[Table of Contents](#table-of-contents)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:15
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:15
+msgid "[Summary](#summary)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:16
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:16
+msgid "[Motivation](#motivation)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:17
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:17
+msgid "[Goals](#goals)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:18
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:18
+msgid "[Non-Goals](#non-goals)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:19
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:19
+msgid "[Proposal](#proposal)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:20
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:23
+msgid "[Drawbacks](#drawbacks)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:21
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:24
+msgid "[Alternatives Considered](#alternatives-considered)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:22
+msgid "[Appendix](#appendix)"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:24
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:28
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:76
+msgid "Summary"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:26
+msgid "\\[TODO - sentence 1: summary of the problem\\]"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:28
+msgid "\\[TODO - sentence 2: summary of the solution\\]"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:30
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:47
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:77
+msgid "Motivation"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:32
+#: ../../source/fed/0000-20200102-fed-template.md:36
+#: ../../source/fed/0000-20200102-fed-template.md:40
+#: ../../source/fed/0000-20200102-fed-template.md:44
+#: ../../source/fed/0000-20200102-fed-template.md:48
+#: ../../source/fed/0000-20200102-fed-template.md:54
+#: ../../source/fed/0000-20200102-fed-template.md:58
+msgid "\\[TODO\\]"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:34
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:53
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:78
+msgid "Goals"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:38
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:59
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:79
+msgid "Non-Goals"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:42
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:65
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:80
+msgid "Proposal"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:46
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:85
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:129
+msgid "Drawbacks"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:50
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:86
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:135
+msgid "Alternatives Considered"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:52
+msgid "\\[Alternative 1\\]"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:56
+msgid "\\[Alternative 2\\]"
+msgstr ""
+
+#: ../../source/fed/0000-20200102-fed-template.md:60
+msgid "Appendix"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:10
+msgid "Flower Enhancement Doc"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:20
+msgid "[Enhancement Doc Template](#enhancement-doc-template)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:21
+msgid "[Metadata](#metadata)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:22
+msgid "[Workflow](#workflow)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:25
+msgid "[GitHub Issues](#github-issues)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:26
+msgid "[Google Docs](#google-docs)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:30
+msgid "A Flower Enhancement is a standardized development process to"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:32
+msgid "provide a common structure for proposing larger changes"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:33
+msgid "ensure that the motivation for a change is clear"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:34
+msgid "persist project information in a version control system"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:35
+msgid "document the motivation for impactful user-facing changes"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:36
+msgid "reserve GitHub issues for tracking work in flight"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:37
+msgid ""
+"ensure community participants can successfully drive changes to "
+"completion across one or more releases while stakeholders are adequately "
+"represented throughout the process"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:39
+msgid "Hence, an Enhancement Doc combines aspects of"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:41
+msgid "a feature, and effort-tracking document"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:42
+msgid "a product requirements document"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:43
+msgid "a design document"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:45
+msgid ""
+"into one file, which is created incrementally in collaboration with the "
+"community."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:49
+msgid ""
+"For far-fetching changes or features proposed to Flower, an abstraction "
+"beyond a single GitHub issue or pull request is required to understand "
+"and communicate upcoming changes to the project."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:51
+msgid ""
+"The purpose of this process is to reduce the amount of \"tribal "
+"knowledge\" in our community. By moving decisions from Slack threads, "
+"video calls, and hallway conversations into a well-tracked artifact, this"
+" process aims to enhance communication and discoverability."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:55
+msgid ""
+"Roughly any larger, user-facing enhancement should follow the Enhancement"
+" process. If an enhancement would be described in either written or "
+"verbal communication to anyone besides the author or developer, then "
+"consider creating an Enhancement Doc."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:57
+msgid ""
+"Similarly, any technical effort (refactoring, major architectural change)"
+" that will impact a large section of the development community should "
+"also be communicated widely. The Enhancement process is suited for this "
+"even if it will have zero impact on the typical user or operator."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:61
+msgid ""
+"For small changes and additions, going through the Enhancement process "
+"would be time-consuming and unnecessary. This includes, for example, "
+"adding new Federated Learning algorithms, as these only add features "
+"without changing how Flower works or is used."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:63
+msgid ""
+"Enhancements are different from feature requests, as they are already "
+"providing a laid-out path for implementation and are championed by "
+"members of the community."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:67
+msgid ""
+"An Enhancement is captured in a Markdown file that follows a defined "
+"template and a workflow to review and store enhancement docs for "
+"reference — the Enhancement Doc."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:69
+msgid "Enhancement Doc Template"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:71
+msgid ""
+"Each enhancement doc is provided as a Markdown file having the following "
+"structure"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:73
+msgid "Metadata (as [described below](#metadata) in form of a YAML preamble)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:74
+msgid "Title (same as in metadata)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:75
+msgid "Table of Contents (if needed)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:81
+msgid "Notes/Constraints/Caveats (optional)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:82
+msgid "Design Details (optional)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:83
+msgid "Graduation Criteria"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:84
+msgid "Upgrade/Downgrade Strategy (if applicable)"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:88
+msgid "As a reference, this document follows the above structure."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:90
+msgid "Metadata"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:92
+msgid ""
+"**fed-number** (Required) The `fed-number` of the last Flower Enhancement"
+" Doc + 1. With this number, it becomes easy to reference other proposals."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:94
+msgid "**title** (Required) The title of the proposal in plain language."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:96
+msgid ""
+"**status** (Required) The current status of the proposal. See "
+"[workflow](#workflow) for the possible states."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:98
+msgid ""
+"**authors** (Required) A list of authors of the proposal. This is simply "
+"the GitHub ID."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:100
+msgid ""
+"**creation-date** (Required) The date that the proposal was first "
+"submitted in a PR."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:102
+msgid ""
+"**last-updated** (Optional) The date that the proposal was last changed "
+"significantly."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:104
+msgid ""
+"**see-also** (Optional) A list of other proposals that are relevant to "
+"this one."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:106
+msgid "**replaces** (Optional) A list of proposals that this one replaces."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:108
+msgid "**superseded-by** (Optional) A list of proposals that this one supersedes."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:111
+msgid "Workflow"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:113
+msgid ""
+"The idea forming the enhancement should already have been discussed or "
+"pitched in the community. As such, it needs a champion, usually the "
+"author, who shepherds the enhancement. This person also has to find "
+"committers to Flower willing to review the proposal."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:115
+msgid ""
+"New enhancements are checked in with a file name in the form of `NNNN-"
+"YYYYMMDD-enhancement-title.md`, with `NNNN` being the Flower Enhancement "
+"Doc number, to `enhancements`. All enhancements start in `provisional` "
+"state as part of a pull request. Discussions are done as part of the pull"
+" request review."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:117
+msgid ""
+"Once an enhancement has been reviewed and approved, its status is changed"
+" to `implementable`. The actual implementation is then done in separate "
+"pull requests. These pull requests should mention the respective "
+"enhancement as part of their description. After the implementation is "
+"done, the proposal status is changed to `implemented`."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:119
+msgid ""
+"Under certain conditions, other states are possible. An Enhancement has "
+"the following states:"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:121
+msgid ""
+"`provisional`: The enhancement has been proposed and is actively being "
+"defined. This is the starting state while the proposal is being fleshed "
+"out and actively defined and discussed."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:122
+msgid "`implementable`: The enhancement has been reviewed and approved."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:123
+msgid ""
+"`implemented`: The enhancement has been implemented and is no longer "
+"actively changed."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:124
+msgid "`deferred`: The enhancement is proposed but not actively being worked on."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:125
+msgid ""
+"`rejected`: The authors and reviewers have decided that this enhancement "
+"is not moving forward."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:126
+msgid "`withdrawn`: The authors have withdrawn the enhancement."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:127
+msgid "`replaced`: The enhancement has been replaced by a new enhancement."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:131
+msgid ""
+"Adding an additional process to the ones already provided by GitHub "
+"(Issues and Pull Requests) adds more complexity and can be a barrier for "
+"potential first-time contributors."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:133
+msgid ""
+"Expanding the proposal template beyond the single-sentence description "
+"currently required in the features issue template may be a heavy burden "
+"for non-native English speakers."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:137
+msgid "GitHub Issues"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:139
+msgid ""
+"Using GitHub Issues for these kinds of enhancements is doable. One could "
+"use, for example, tags, to differentiate and filter them from other "
+"issues. The main issue is in discussing and reviewing an enhancement: "
+"GitHub issues only have a single thread for comments. Enhancements "
+"usually have multiple threads of discussion at the same time for various "
+"parts of the doc. Managing these multiple discussions can be confusing "
+"when using GitHub Issues."
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:141
+msgid "Google Docs"
+msgstr ""
+
+#: ../../source/fed/0001-20220311-flower-enhancement-doc.md:143
+msgid ""
+"Google Docs allow for multiple threads of discussions. But as Google Docs"
+" are hosted outside the project, their discoverability by the community "
+"needs to be taken care of. A list of links to all proposals has to be "
+"managed and made available for the community. Compared to shipping "
+"proposals as part of Flower's repository, the potential for missing links"
+" is much higher."
+msgstr ""
+
+#: ../../source/fed/index.md:1
+msgid "FED - Flower Enhancement Doc"
+msgstr ""
+
+#: ../../source/how-to-aggregate-evaluation-results.rst:2
+msgid "Aggregate evaluation results"
+msgstr ""
+
+#: ../../source/how-to-aggregate-evaluation-results.rst:4
+msgid ""
+"The Flower server does not prescribe a way to aggregate evaluation "
+"results, but it enables the user to fully customize result aggregation."
+msgstr ""
+
+#: ../../source/how-to-aggregate-evaluation-results.rst:8
+msgid "Aggregate Custom Evaluation Results"
+msgstr ""
+
+#: ../../source/how-to-aggregate-evaluation-results.rst:10
+msgid ""
+"The same :code:`Strategy`-customization approach can be used to aggregate"
+" custom evaluation results coming from individual clients. Clients can "
+"return custom metrics to the server by returning a dictionary:"
+msgstr ""
+
+#: ../../source/how-to-aggregate-evaluation-results.rst:36
+msgid ""
+"The server can then use a customized strategy to aggregate the metrics "
+"provided in these dictionaries:"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:2
+msgid "Configure clients"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:4
+msgid ""
+"Along with model parameters, Flower can send configuration values to "
+"clients. Configuration values can be used for various purposes. They are,"
+" for example, a popular way to control client-side hyperparameters from "
+"the server."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:7
+msgid "Configuration values"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:9
+msgid ""
+"Configuration values are represented as a dictionary with ``str`` keys "
+"and values of type ``bool``, ``bytes``, ``double`` (64-bit precision "
+"float), ``int``, or ``str`` (or equivalent types in different languages)."
+" Here is an example of a configuration dictionary in Python:"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:20
+msgid ""
+"Flower serializes these configuration dictionaries (or *config dict* for "
+"short) to their ProtoBuf representation, transports them to the client "
+"using gRPC, and then deserializes them back to Python dictionaries."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:24
+msgid ""
+"Currently, there is no support for directly sending collection types "
+"(e.g., ``Set``, ``List``, ``Map``) as values in configuration "
+"dictionaries. There are several workarounds to send collections as values"
+" by converting them to one of the supported value types (and converting "
+"them back on the client-side)."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:26
+msgid ""
+"One can, for example, convert a list of floating-point numbers to a JSON "
+"string, then send the JSON string using the configuration dictionary, and"
+" then convert the JSON string back to a list of floating-point numbers on"
+" the client."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:30
+msgid "Configuration through built-in strategies"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:32
+msgid ""
+"The easiest way to send configuration values to clients is to use a "
+"built-in strategy like :code:`FedAvg`. Built-in strategies support so-"
+"called configuration functions. A configuration function is a function "
+"that the built-in strategy calls to get the configuration dictionary for "
+"the current round. It then forwards the configuration dictionary to all "
+"the clients selected during that round."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:34
+msgid ""
+"Let's start with a simple example. Imagine we want to send (a) the batch "
+"size that the client should use, (b) the current global round of "
+"federated learning, and (c) the number of epochs to train on the client-"
+"side. Our configuration function could look like this:"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:47
+msgid ""
+"To make the built-in strategies use this function, we can pass it to "
+"``FedAvg`` during initialization using the parameter "
+":code:`on_fit_config_fn`:"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:56
+msgid "One the client side, we receive the configuration dictionary in ``fit``:"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:67
+msgid ""
+"There is also an `on_evaluate_config_fn` to configure evaluation, which "
+"works the same way. They are separate functions because one might want to"
+" send different configuration values to `evaluate` (for example, to use a"
+" different batch size)."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:69
+msgid ""
+"The built-in strategies call this function every round (that is, every "
+"time `Strategy.configure_fit` or `Strategy.configure_evaluate` runs). "
+"Calling `on_evaluate_config_fn` every round allows us to vary/change the "
+"config dict over consecutive rounds. If we wanted to implement a "
+"hyperparameter schedule, for example, to increase the number of local "
+"epochs during later rounds, we could do the following:"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:82
+msgid "The :code:`FedAvg` strategy will call this function *every round*."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:85
+msgid "Configuring individual clients"
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:87
+msgid ""
+"In some cases, it is necessary to send different configuration values to "
+"different clients."
+msgstr ""
+
+#: ../../source/how-to-configure-clients.rst:89
+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>`_. "
+"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 ""
+
+#: ../../source/how-to-configure-logging.rst:2
+msgid "Configure logging"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:4
+msgid ""
+"The Flower logger keeps track of all core events that take place in "
+"federated learning workloads. It presents information by default "
+"following a standard message format:"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:13
+msgid ""
+"containing relevant information including: log message level (e.g. "
+":code:`INFO`, :code:`DEBUG`), a timestamp, the line where the logging "
+"took place from, as well as the log message itself. In this way, the "
+"logger would typically display information on your terminal as follows:"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:34
+msgid "Saving log to file"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:36
+msgid ""
+"By default, the Flower log is outputted to the terminal where you launch "
+"your Federated Learning workload from. This applies for both gRPC-based "
+"federation (i.e. when you do :code:`fl.server.start_server`) and when "
+"using the :code:`VirtualClientEngine` (i.e. when you do "
+":code:`fl.simulation.start_simulation`). In some situations you might "
+"want to save this log to disk. You can do so by calling the "
+"`fl.common.logger.configure() "
+"<https://github.com/adap/flower/blob/main/src/py/flwr/common/logger.py>`_"
+" function. For example:"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:53
+msgid ""
+"With the above, Flower will record the log you see on your terminal to "
+":code:`log.txt`. This file will be created in the same directory as were "
+"you are running the code from. If we inspect we see the log above is also"
+" recorded but prefixing with :code:`identifier` each line:"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:74
+msgid "Log your own messages"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:76
+msgid ""
+"You might expand the information shown by default with the Flower logger "
+"by adding more messages relevant to your application. You can achieve "
+"this easily as follows."
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:102
+msgid ""
+"In this way your logger will show, in addition to the default messages, "
+"the ones introduced by the clients as specified above."
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:128
+msgid "Log to a remote service"
+msgstr ""
+
+#: ../../source/how-to-configure-logging.rst:130
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:2
+msgid "Enable SSL connections"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:4
+msgid ""
+"This guide describes how to a SSL-enabled secure Flower server can be "
+"started and how a Flower client can establish a secure connections to it."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:7
+msgid ""
+"A complete code example demonstrating a secure connection can be found "
+"`here <https://github.com/adap/flower/tree/main/examples/advanced-"
+"tensorflow>`_."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:10
+msgid ""
+"The code example comes with a README.md file which will explain how to "
+"start it. Although it is already SSL-enabled, it might be less "
+"descriptive on how. Stick to this guide for a deeper introduction to the "
+"topic."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:16
+msgid "Certificates"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:18
+msgid ""
+"Using SSL-enabled connections requires certificates to be passed to the "
+"server and client. For the purpose of this guide we are going to generate"
+" self-signed certificates. As this can become quite complex we are going "
+"to ask you to run the script in :code:`examples/advanced-"
+"tensorflow/certificates/generate.sh`"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:23
+msgid "with the following command sequence:"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:30
+msgid ""
+"This will generate the certificates in :code:`examples/advanced-"
+"tensorflow/.cache/certificates`."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:32
+msgid ""
+"The approach how the SSL certificates are generated in this example can "
+"serve as an inspiration and starting point but should not be taken as "
+"complete for production environments. Please refer to other sources "
+"regarding the issue of correctly generating certificates for production "
+"environments."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:36
+msgid ""
+"In case you are a researcher you might be just fine using the self-signed"
+" certificates generated using the scripts which are part of this guide."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:41
+msgid "Server"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:43
+msgid ""
+"We are now going to show how to write a sever which uses the previously "
+"generated scripts."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:61
+msgid ""
+"When providing certificates, the server expects a tuple of three "
+"certificates. :code:`Path` can be used to easily read the contents of "
+"those files into byte strings, which is the data type "
+":code:`start_server` expects."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:65
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:37
+#: ../../source/ref-api-flwr.rst:15
+msgid "Client"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:67
+msgid ""
+"We are now going to show how to write a client which uses the previously "
+"generated scripts:"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:84
+msgid ""
+"When setting :code:`root_certificates`, the client expects the PEM-"
+"encoded root certificates as a byte string. We are again using "
+":code:`Path` to simplify reading those as byte strings."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:89
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:287
+msgid "Conclusion"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:91
+msgid ""
+"You should now have learned how to generate self-signed certificates "
+"using the given script, start a SSL-enabled server, and have a client "
+"establish a secure connection to it."
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:96
+msgid "Additional resources"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:98
+msgid ""
+"These additional sources might be relevant if you would like to dive "
+"deeper into the topic of certificates:"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:100
+msgid "`Let's Encrypt <https://letsencrypt.org/docs/>`_"
+msgstr ""
+
+#: ../../source/how-to-enable-ssl-connections.rst:101
+msgid "`certbot <https://certbot.eff.org/>`_"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:2
+msgid "Implement strategies"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:4
+msgid ""
+"The strategy abstraction enables implementation of fully custom "
+"strategies. A strategy is basically the federated learning algorithm that"
+" runs on the server. Strategies decide how to sample clients, how to "
+"configure clients for training, how to aggregate updates, and how to "
+"evaluate models. Flower provides a few built-in strategies which are "
+"based on the same API described below."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:11
+msgid "The :code:`Strategy` abstraction"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:13
+msgid ""
+"All strategy implementation are derived from the abstract base class "
+":code:`flwr.server.strategy.Strategy`, both built-in implementations and "
+"third party implementations. This means that custom strategy "
+"implementations have the exact same capabilities at their disposal as "
+"built-in ones."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:18
+msgid ""
+"The strategy abstraction defines a few abstract methods that need to be "
+"implemented:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:75
+msgid ""
+"Creating a new strategy means implementing a new :code:`class` (derived "
+"from the abstract base class :code:`Strategy`) that implements for the "
+"previously shown abstract methods:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:100
+msgid "The Flower server calls these methods in the following order:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:177
+msgid "The following sections describe each of those methods in more detail."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:180
+msgid "The :code:`initialize_parameters` method"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:182
+msgid ""
+":code:`initialize_parameters` is called only once, at the very beginning "
+"of an execution. It is responsible for providing the initial global model"
+" parameters in a serialized form (i.e., as a :code:`Parameters` object)."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:184
+msgid ""
+"Built-in strategies return user-provided initial parameters. The "
+"following example shows how initial parameters can be passed to "
+":code:`FedAvg`:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:209
+msgid ""
+"The Flower server will call :code:`initialize_parameters`, which either "
+"returns the parameters that were passed to :code:`initial_parameters`, or"
+" :code:`None`. If no parameters are returned from "
+":code:`initialize_parameters` (i.e., :code:`None`), the server will "
+"randomly select one client and ask it to provide its parameters. This is "
+"a convenience feature and not recommended in practice, but it can be "
+"useful for prototyping. In practice, it is recommended to always use "
+"server-side parameter initialization."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:213
+msgid ""
+"Server-side parameter initialization is a powerful mechanism. It can be "
+"used, for example, to resume training from a previously saved checkpoint."
+" It is also the fundamental capability needed to implement hybrid "
+"approaches, for example, to fine-tune a pre-trained model using federated"
+" learning."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:216
+msgid "The :code:`configure_fit` method"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:218
+msgid ""
+":code:`configure_fit` is responsible for configuring the upcoming round "
+"of training. What does *configure* mean in this context? Configuring a "
+"round means selecting clients and deciding what instructions to send to "
+"these clients. The signature of :code:`configure_fit` makes this clear:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:231
+msgid ""
+"The return value is a list of tuples, each representing the instructions "
+"that will be sent to a particular client. Strategy implementations "
+"usually perform the following steps in :code:`configure_fit`:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:233
+#: ../../source/how-to-implement-strategies.rst:280
+msgid ""
+"Use the :code:`client_manager` to randomly sample all (or a subset of) "
+"available clients (each represented as a :code:`ClientProxy` object)"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:234
+msgid ""
+"Pair each :code:`ClientProxy` with the same :code:`FitIns` holding the "
+"current global model :code:`parameters` and :code:`config` dict"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:236
+msgid ""
+"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`."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:240
+msgid ""
+"The structure of this return value provides a lot of flexibility to the "
+"user. Since instructions are defined on a per-client basis, different "
+"instructions can be sent to each client. This enables custom strategies "
+"to train, for example, different models on different clients, or use "
+"different hyperparameters on different clients (via the :code:`config` "
+"dict)."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:243
+msgid "The :code:`aggregate_fit` method"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:245
+msgid ""
+":code:`aggregate_fit` is responsible for aggregating the results returned"
+" by the clients that were selected and asked to train in "
+":code:`configure_fit`."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:258
+msgid ""
+"Of course, failures can happen, so there is no guarantee that the server "
+"will get results from all the clients it sent instructions to (via "
+":code:`configure_fit`). :code:`aggregate_fit` therefore receives a list "
+"of :code:`results`, but also a list of :code:`failures`."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:260
+msgid ""
+":code:`aggregate_fit` returns an optional :code:`Parameters` object and a"
+" dictionary of aggregated metrics. The :code:`Parameters` return value is"
+" optional because :code:`aggregate_fit` might decide that the results "
+"provided are not sufficient for aggregation (e.g., too many failures)."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:263
+msgid "The :code:`configure_evaluate` method"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:265
+msgid ""
+":code:`configure_evaluate` is responsible for configuring the upcoming "
+"round of evaluation. What does *configure* mean in this context? "
+"Configuring a round means selecting clients and deciding what "
+"instructions to send to these clients. The signature of "
+":code:`configure_evaluate` makes this clear:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:278
+msgid ""
+"The return value is a list of tuples, each representing the instructions "
+"that will be sent to a particular client. Strategy implementations "
+"usually perform the following steps in :code:`configure_evaluate`:"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:281
+msgid ""
+"Pair each :code:`ClientProxy` with the same :code:`EvaluateIns` holding "
+"the current global model :code:`parameters` and :code:`config` dict"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:283
+msgid ""
+"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`."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:287
+msgid ""
+"The structure of this return value provides a lot of flexibility to the "
+"user. Since instructions are defined on a per-client basis, different "
+"instructions can be sent to each client. This enables custom strategies "
+"to evaluate, for example, different models on different clients, or use "
+"different hyperparameters on different clients (via the :code:`config` "
+"dict)."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:291
+msgid "The :code:`aggregate_evaluate` method"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:293
+msgid ""
+":code:`aggregate_evaluate` is responsible for aggregating the results "
+"returned by the clients that were selected and asked to evaluate in "
+":code:`configure_evaluate`."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:306
+msgid ""
+"Of course, failures can happen, so there is no guarantee that the server "
+"will get results from all the clients it sent instructions to (via "
+":code:`configure_evaluate`). :code:`aggregate_evaluate` therefore "
+"receives a list of :code:`results`, but also a list of :code:`failures`."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:308
+msgid ""
+":code:`aggregate_evaluate` returns an optional :code:`float` (loss) and a"
+" dictionary of aggregated metrics. The :code:`float` return value is "
+"optional because :code:`aggregate_evaluate` might decide that the results"
+" provided are not sufficient for aggregation (e.g., too many failures)."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:311
+msgid "The :code:`evaluate` method"
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:313
+msgid ""
+":code:`evaluate` is responsible for evaluating model parameters on the "
+"server-side. Having :code:`evaluate` in addition to "
+":code:`configure_evaluate`/:code:`aggregate_evaluate` enables strategies "
+"to perform both servers-side and client-side (federated) evaluation."
+msgstr ""
+
+#: ../../source/how-to-implement-strategies.rst:323
+msgid ""
+"The return value is again optional because the strategy might not need to"
+" implement server-side evaluation or because the user-defined "
+":code:`evaluate` method might not complete successfully (e.g., it might "
+"fail to load the server-side evaluation data)."
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:2
+msgid "Install Flower"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:6
+msgid "Python version"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:12
+msgid "Install stable release"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:14
+msgid ""
+"Stable releases are available on `PyPI "
+"<https://pypi.org/project/flwr/>`_::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:18
+msgid ""
+"For simulations that use the Virtual Client Engine, ``flwr`` should be "
+"installed with the ``simulation`` extra::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:24
+msgid "Verify installation"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:26
+msgid ""
+"The following command can be used to verfiy if Flower was successfully "
+"installed. If everything worked, it should print the version of Flower to"
+" the command line::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:33
+msgid "Advanced installation options"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:36
+msgid "Install pre-release"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:38
+msgid ""
+"New (possibly unstable) versions of Flower are sometimes available as "
+"pre-release versions (alpha, beta, release candidate) before the stable "
+"release happens::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:42
+msgid ""
+"For simulations that use the Virtual Client Engine, ``flwr`` pre-releases"
+" should be installed with the ``simulation`` extra::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:47
+msgid "Install nightly release"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:49
+msgid ""
+"The latest (potentially unstable) changes in Flower are available as "
+"nightly releases::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:53
+msgid ""
+"For simulations that use the Virtual Client Engine, ``flwr-nightly`` "
+"should be installed with the ``simulation`` extra::"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:2
+msgid "Monitor simulation"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:4
+msgid ""
+"Flower allows you to monitor system resources while running your "
+"simulation. Moreover, the Flower simulation engine is powerful and "
+"enables you to decide how to allocate resources per client manner and "
+"constrain the total usage. Insights from resource consumption can help "
+"you make smarter decisions and speed up the execution time."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:6
+msgid ""
+"The specific instructions assume you are using macOS and have the "
+"`Homebrew <https://brew.sh/>`_ package manager installed."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:10
+msgid "Downloads"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:16
+msgid ""
+"`Prometheus <https://prometheus.io/>`_ is used for data collection, while"
+" `Grafana <https://grafana.com/>`_ will enable you to visualize the "
+"collected data. They are both well integrated with `Ray "
+"<https://www.ray.io/>`_ which Flower uses under the hood."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:18
+msgid ""
+"Overwrite the configuration files (depending on your device, it might be "
+"installed on a different path)."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:20
+msgid "If you are on an M1 Mac, it should be:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:27
+msgid "On the previous generation Intel Mac devices, it should be:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:34
+msgid ""
+"Open the respective configuration files and change them. Depending on "
+"your device, use one of the two following commands:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:44
+msgid ""
+"and then delete all the text in the file and paste a new Prometheus "
+"config you see below. You may adjust the time intervals to your "
+"requirements:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:59
+msgid ""
+"Now after you have edited the Prometheus configuration, do the same with "
+"the Grafana configuration files. Open those using one of the following "
+"commands as before:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:69
+msgid ""
+"Your terminal editor should open and allow you to apply the following "
+"configuration as before."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:84
+msgid ""
+"Congratulations, you just downloaded all the necessary software needed "
+"for metrics tracking. Now, let’s start it."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:88
+msgid "Tracking metrics"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:90
+msgid ""
+"Before running your Flower simulation, you have to start the monitoring "
+"tools you have just installed and configured."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:97
+msgid ""
+"Please include the following argument in your Python code when starting a"
+" simulation."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:108
+msgid "Now, you are ready to start your workload."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:110
+msgid ""
+"Shortly after the simulation starts, you should see the following logs in"
+" your terminal:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:117
+msgid "You can look at everything at `<http://127.0.0.1:8265>`_ ."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:119
+msgid ""
+"It's a Ray Dashboard. You can navigate to Metrics (on the left panel, the"
+" lowest option)."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:121
+msgid ""
+"Or alternatively, you can just see them in Grafana by clicking on the "
+"right-up corner, “View in Grafana”. Please note that the Ray dashboard is"
+" only accessible during the simulation. After the simulation ends, you "
+"can only use Grafana to explore the metrics. You can start Grafana by "
+"going to ``http://localhost:3000/``."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:123
+msgid ""
+"After you finish the visualization, stop Prometheus and Grafana. This is "
+"important as they will otherwise block, for example port :code:`3000` on "
+"your machine as long as they are running."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:132
+msgid "Resource allocation"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:134
+msgid ""
+"You must understand how the Ray library works to efficiently allocate "
+"system resources to simulation clients on your own."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:136
+msgid ""
+"Initially, the simulation (which Ray handles under the hood) starts by "
+"default with all the available resources on the system, which it shares "
+"among the clients. It doesn't mean it divides it equally among all of "
+"them, nor that the model training happens at all of them simultaneously. "
+"You will learn more about that in the later part of this blog. You can "
+"check the system resources by running the following:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:143
+msgid "In Google Colab, the result you see might be similar to this:"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:155
+msgid ""
+"However, you can overwrite the defaults. When starting a simulation, do "
+"the following (you don't need to overwrite all of them):"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:175
+msgid "Let’s also specify the resource for a single client."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:205
+msgid ""
+"Now comes the crucial part. Ray will start a new client only when it has "
+"all the required resources (such that they run in parallel) when the "
+"resources allow."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:207
+msgid ""
+"In the example above, only one client will be run, so your clients won't "
+"run concurrently. Setting :code:`client_num_gpus = 0.5` would allow "
+"running two clients and therefore enable them to run concurrently. Be "
+"careful not to require more resources than available. If you specified "
+":code:`client_num_gpus = 2`, the simulation wouldn't start (even if you "
+"had 2 GPUs but decided to set 1 in :code:`ray_init_args`)."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:212 ../../source/ref-faq.rst:2
+msgid "FAQ"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:214
+msgid "Q: I don't see any metrics logged."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:216
+msgid ""
+"A: The timeframe might not be properly set. The setting is in the top "
+"right corner (\"Last 30 minutes\" by default). Please change the "
+"timeframe to reflect the period when the simulation was running."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:218
+msgid ""
+"Q: I see “Grafana server not detected. Please make sure the Grafana "
+"server is running and refresh this page” after going to the Metrics tab "
+"in Ray Dashboard."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:220
+msgid ""
+"A: You probably don't have Grafana running. Please check the running "
+"services"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:226
+msgid ""
+"Q: I see \"This site can't be reached\" when going to "
+"`<http://127.0.0.1:8265>`_."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:228
+msgid ""
+"A: Either the simulation has already finished, or you still need to start"
+" Prometheus."
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:232
+msgid "Resources"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:234
+msgid ""
+"Ray Dashboard: `<https://docs.ray.io/en/latest/ray-core/ray-"
+"dashboard.html>`_"
+msgstr ""
+
+#: ../../source/how-to-monitor-simulation.rst:236
+msgid ""
+"Ray Metrics: `<https://docs.ray.io/en/latest/ray-observability/ray-"
+"metrics.html>`_"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:2
+msgid "Run simulations"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:8
+msgid ""
+"Simulating Federated Learning workloads is useful for a multitude of use-"
+"cases: you might want to run your workload on a large cohort of clients "
+"but without having to source, configure and mange a large number of "
+"physical devices; you might want to run your FL workloads as fast as "
+"possible on the compute systems you have access to without having to go "
+"through a complex setup process; you might want to validate your "
+"algorithm on different scenarios at varying levels of data and system "
+"heterogeneity, client availability, privacy budgets, etc. These are among"
+" some of the use-cases where simulating FL workloads makes sense. Flower "
+"can accommodate these scenarios by means of its `VirtualClientEngine "
+"<contributor-explanation-architecture.html#virtual-client-engine>`_ or "
+"VCE."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:10
+msgid ""
+"The :code:`VirtualClientEngine` schedules, launches and manages `virtual`"
+" clients. These clients are identical to `non-virtual` clients (i.e. the "
+"ones you launch via the command `flwr.client.start_numpy_client <ref-api-"
+"flwr.html#start-numpy-client>`_) in the sense that they can be configure "
+"by creating a class inheriting, for example, from "
+"`flwr.client.NumPyClient <ref-api-flwr.html#flwr.client.NumPyClient>`_ "
+"and therefore behave in an identical way. In addition to that, clients "
+"managed by the :code:`VirtualClientEngine` are:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:12
+msgid ""
+"resource-aware: this means that each client gets assigned a portion of "
+"the compute and memory on your system. You as a user can control this at "
+"the beginning of the simulation and allows you to control the degree of "
+"parallelism of your Flower FL simulation. The fewer the resources per "
+"client, the more clients can run concurrently on the same hardware."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:13
+msgid ""
+"self-managed: this means that you as a user do not need to launch clients"
+" manually, instead this gets delegated to :code:`VirtualClientEngine`'s "
+"internals."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:14
+msgid ""
+"ephemeral: this means that a client is only materialized when it is "
+"required in the FL process (e.g. to do `fit() <ref-api-"
+"flwr.html#flwr.client.Client.fit>`_). The object is destroyed afterwards,"
+" releasing the resources it was assigned and allowing in this way other "
+"clients to participate."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:16
+msgid ""
+"The :code:`VirtualClientEngine` implements `virtual` clients using `Ray "
+"<https://www.ray.io/>`_, an open-source framework for scalable Python "
+"workloads. In particular, Flower's :code:`VirtualClientEngine` makes use "
+"of `Actors <https://docs.ray.io/en/latest/ray-core/actors.html>`_ to "
+"spawn `virtual` clients and run their workload."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:20
+msgid "Launch your Flower simulation"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:22
+msgid ""
+"Running Flower simulations still require you to define your client class,"
+" a strategy, and utility functions to download and load (and potentially "
+"partition) your dataset. With that out of the way, launching your "
+"simulation is done with `start_simulation <ref-api-"
+"flwr.html#flwr.simulation.start_simulation>`_ and a minimal example looks"
+" as follows:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:44
+msgid "VirtualClientEngine resources"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:45
+msgid ""
+"By default the VCE has access to all system resources (i.e. all CPUs, all"
+" GPUs, etc) since that is also the default behavior when starting Ray. "
+"However, in some settings you might want to limit how many of your system"
+" resources are used for simulation. You can do this via the "
+":code:`ray_init_args` input argument to :code:`start_simulation` which "
+"the VCE internally passes to Ray's :code:`ray.init` command. For a "
+"complete list of settings you can configure check the `ray.init "
+"<https://docs.ray.io/en/latest/ray-core/api/doc/ray.init.html#ray-init>`_"
+" documentation. Do not set :code:`ray_init_args` if you want the VCE to "
+"use all your system's CPUs and GPUs."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:62
+msgid "Assigning client resources"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:63
+msgid ""
+"By default the :code:`VirtualClientEngine` assigns a single CPU core (and"
+" nothing else) to each virtual client. This means that if your system has"
+" 10 cores, that many virtual clients can be concurrently running."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:65
+msgid ""
+"More often than not, you would probably like to adjust the resources your"
+" clients get assigned based on the complexity (i.e. compute and memory "
+"footprint) of your FL workload. You can do so when starting your "
+"simulation by setting the argument `client_resources` to "
+"`start_simulation <ref-api-flwr.html#flwr.simulation.start_simulation>`_."
+" Two keys are internally used by Ray to schedule and spawn workloads (in "
+"our case Flower clients):"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:67
+msgid ":code:`num_cpus` indicates the number of CPU cores a client would get."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:68
+msgid ""
+":code:`num_gpus` indicates the **ratio** of GPU memory a client gets "
+"assigned."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:70
+msgid "Let's see a few examples:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:89
+msgid ""
+"While the :code:`client_resources` can be used to control the degree of "
+"concurrency in your FL simulation, this does not stop you from running "
+"dozens, hundreds or even thousands of clients in the same round and "
+"having orders of magnitude more `dormant` (i.e. not participating in a "
+"round) clients. Let's say you want to have 100 clients per round but your"
+" system can only accommodate 8 clients concurrently. The "
+":code:`VirtualClientEngine` will schedule 100 jobs to run (each "
+"simulating a client sampled by the strategy) and then will execute them "
+"in a resource-aware manner in batches of 8."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:91
+msgid ""
+"To understand all the intricate details on how resources are used to "
+"schedule FL clients and how to define custom resources, please take a "
+"look at the `Ray documentation <https://docs.ray.io/en/latest/ray-"
+"core/scheduling/resources.html>`_."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:94
+msgid "Simulation examples"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:96
+msgid ""
+"A few ready-to-run complete examples for Flower simulation in "
+"Tensorflow/Keras and PyTorch are provided in the `Flower repository "
+"<https://github.com/adap/flower>`_. You can run them on Google Colab too:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:98
+msgid ""
+"`Tensorflow/Keras Simulation "
+"<https://github.com/adap/flower/tree/main/examples/simulation-"
+"tensorflow>`_: 100 clients collaboratively train a MLP model on MNIST."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:99
+msgid ""
+"`PyTorch Simulation <https://github.com/adap/flower/tree/main/examples"
+"/simulation-pytorch>`_: 100 clients collaboratively train a CNN model on "
+"MNIST."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:104
+msgid "Multi-node Flower simulations"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:106
+msgid ""
+"Flower's :code:`VirtualClientEngine` allows you to run FL simulations "
+"across multiple compute nodes. Before starting your multi-node simulation"
+" ensure that you:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:108
+msgid "Have the same Python environment in all nodes."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:109
+msgid "Have a copy of your code (e.g. your entire repo) in all nodes."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:110
+msgid ""
+"Have a copy of your dataset in all nodes (more about this in "
+":ref:`simulation considerations <considerations-for-simulations>`)"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:111
+msgid ""
+"Pass :code:`ray_init_args={\"address\"=\"auto\"}` to `start_simulation "
+"<ref-api-flwr.html#flwr.simulation.start_simulation>`_ so the "
+":code:`VirtualClientEngine` attaches to a running Ray instance."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:112
+msgid ""
+"Start Ray on you head node: on the terminal type :code:`ray start "
+"--head`. This command will print a few lines, one of which indicates how "
+"to attach other nodes to the head node."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:113
+msgid ""
+"Attach other nodes to the head node: copy the command shown after "
+"starting the head and execute it on terminal of a new node: for example "
+":code:`ray start --address='192.168.1.132:6379'`"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:115
+msgid ""
+"With all the above done, you can run your code from the head node as you "
+"would if the simulation was running on a single node."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:117
+msgid ""
+"Once your simulation is finished, if you'd like to dismantle your cluster"
+" you simply need to run the command :code:`ray stop` in each node's "
+"terminal (including the head node)."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:120
+msgid "Multi-node simulation good-to-know"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:122
+msgid ""
+"Here we list a few interesting functionality when running multi-node FL "
+"simulations:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:124
+msgid ""
+"User :code:`ray status` to check all nodes connected to your head node as"
+" well as the total resources available to the "
+":code:`VirtualClientEngine`."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:126
+msgid ""
+"When attaching a new node to the head, all its resources (i.e. all CPUs, "
+"all GPUs) will be visible by the head node. This means that the "
+":code:`VirtualClientEngine` can schedule as many `virtual` clients as "
+"that node can possible run. In some settings you might want to exclude "
+"certain resources from the simulation. You can do this by appending "
+"`--num-cpus=<NUM_CPUS_FROM_NODE>` and/or `--num-"
+"gpus=<NUM_GPUS_FROM_NODE>` in any :code:`ray start` command (including "
+"when starting the head)"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:132
+msgid "Considerations for simulations"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:135
+msgid ""
+"We are actively working on these fronts so to make it trivial to run any "
+"FL workload with Flower simulation."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:138
+msgid ""
+"The current VCE allows you to run Federated Learning workloads in "
+"simulation mode whether you are prototyping simple scenarios on your "
+"personal laptop or you want to train a complex FL pipeline across "
+"multiple high-performance GPU nodes. While we add more capabilities to "
+"the VCE, the points below highlight some of the considerations to keep in"
+" mind when designing your FL pipeline with Flower. We also highlight a "
+"couple of current limitations in our implementation."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:141
+msgid "GPU resources"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:143
+msgid ""
+"The VCE assigns a share of GPU memory to a client that specifies the key "
+":code:`num_gpus` in :code:`client_resources`. This being said, Ray (used "
+"internally by the VCE) is by default:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:146
+msgid ""
+"not aware of the total VRAM available on the GPUs. This means that if you"
+" set :code:`num_gpus=0.5` and you have two GPUs in your system with "
+"different (e.g. 32GB and 8GB) VRAM amounts, they both would run 2 clients"
+" concurrently."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:147
+msgid ""
+"not aware of other unrelated (i.e. not created by the VCE) workloads are "
+"running on the GPU. Two takeaways from this are:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:149
+msgid ""
+"Your Flower server might need a GPU to evaluate the `global model` after "
+"aggregation (by instance when making use of the `evaluate method <how-to-"
+"implement-strategies.html#the-evaluate-method>`_)"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:150
+msgid ""
+"If you want to run several independent Flower simulations on the same "
+"machine you need to mask-out your GPUs with "
+":code:`CUDA_VISIBLE_DEVICES=\"<GPU_IDs>\"` when launching your "
+"experiment."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:153
+msgid ""
+"In addition, the GPU resource limits passed to :code:`client_resources` "
+"are not `enforced` (i.e. they can be exceeded) which can result in the "
+"situation of client using more VRAM than the ratio specified when "
+"starting the simulation."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:156
+msgid "TensorFlow with GPUs"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:158
+msgid ""
+"When `using a GPU with TensorFlow "
+"<https://www.tensorflow.org/guide/gpu>`_ nearly your entire GPU memory of"
+" all your GPUs visible to the process will be mapped. This is done by "
+"TensorFlow for optimization purposes. However, in settings such as FL "
+"simulations where we want to split the GPU into multiple `virtual` "
+"clients, this is not a desirable mechanism. Luckily we can disable this "
+"default behavior by `enabling memory growth "
+"<https://www.tensorflow.org/guide/gpu#limiting_gpu_memory_growth>`_."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:160
+msgid ""
+"This would need to be done in the main process (which is where the server"
+" would run) and in each Actor created by the VCE. By means of "
+":code:`actor_kwargs` we can pass the reserved key `\"on_actor_init_fn\"` "
+"in order to specify a function to be executed upon actor initialization. "
+"In this case, to enable GPU growth for TF workloads. It would look as "
+"follows:"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:179
+msgid ""
+"This is precisely the mechanism used in `Tensorflow/Keras Simulation "
+"<https://github.com/adap/flower/tree/main/examples/simulation-"
+"tensorflow>`_ example."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:183
+msgid "Multi-node setups"
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:185
+msgid ""
+"The VCE does not currently offer a way to control on which node a "
+"particular `virtual` client is executed. In other words, if more than a "
+"single node have the resources needed by a client to run, then any of "
+"those nodes could get the client workload scheduled onto. Later in the FL"
+" process (i.e. in a different round) the same client could be executed by"
+" a different node. Depending on how your clients access their datasets, "
+"this might require either having a copy of all dataset partitions on all "
+"nodes or a dataset serving mechanism (e.g. using nfs, a database) to "
+"circumvent data duplication."
+msgstr ""
+
+#: ../../source/how-to-run-simulations.rst:187
+msgid ""
+"By definition virtual clients are `stateless` due to their ephemeral "
+"nature. A client state can be implemented as part of the Flower client "
+"class but users need to ensure this saved to persistent storage (e.g. a "
+"database, disk) and that can be retrieve later by the same client "
+"regardless on which node it is running from. This is related to the point"
+" above also since, in some way, the client's dataset could be seen as a "
+"type of `state`."
+msgstr ""
+
+#: ../../source/how-to-save-and-load-model-checkpoints.rst:2
+msgid "Save and load model checkpoints"
+msgstr ""
+
+#: ../../source/how-to-save-and-load-model-checkpoints.rst:4
+msgid ""
+"Flower does not automatically save model updates on the server-side. This"
+" how-to guide describes the steps to save (and load) model checkpoints in"
+" Flower."
+msgstr ""
+
+#: ../../source/how-to-save-and-load-model-checkpoints.rst:8
+msgid "Model checkpointing"
+msgstr ""
+
+#: ../../source/how-to-save-and-load-model-checkpoints.rst:10
+msgid ""
+"Model updates can be persisted on the server-side by customizing "
+":code:`Strategy` methods. Implementing custom strategies is always an "
+"option, but for many cases it may be more convenient to simply customize "
+"an existing strategy. The following code example defines a new "
+":code:`SaveModelStrategy` which customized the existing built-in "
+":code:`FedAvg` strategy. In particular, it customizes "
+":code:`aggregate_fit` by calling :code:`aggregate_fit` in the base class "
+"(:code:`FedAvg`). It then continues to save returned (aggregated) weights"
+" before it returns those aggregated weights to the caller (i.e., the "
+"server):"
+msgstr ""
+
+#: ../../source/how-to-save-and-load-model-checkpoints.rst:47
+msgid "Save and load PyTorch checkpoints"
+msgstr ""
+
+#: ../../source/how-to-save-and-load-model-checkpoints.rst:49
+msgid ""
+"Similar to the previous example but with a few extra steps, we'll show "
+"how to store a PyTorch checkpoint we'll use the ``torch.save`` function. "
+"Firstly, ``aggregate_fit`` returns a ``Parameters`` object that has to be"
+" transformed into a list of NumPy ``ndarray``'s, then those are "
+"transformed into the PyTorch ``state_dict`` following the ``OrderedDict``"
+" class structure."
+msgstr ""
+
+#: ../../source/how-to-save-and-load-model-checkpoints.rst:85
+msgid ""
+"To load your progress, you simply append the following lines to your "
+"code. Note that this will iterate over all saved checkpoints and load the"
+" latest one:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:2
+msgid "Upgrade to Flower 1.0"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:4
+msgid ""
+"Flower 1.0 is here. Along with new features, Flower 1.0 provides a stable"
+" foundation for future growth. Compared to Flower 0.19 (and other 0.x "
+"series releases), there are a few breaking changes that make it necessary"
+" to change the code of existing 0.x-series projects."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:8
+msgid "Install update"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:10
+msgid ""
+"Here's how to update an existing installation to Flower 1.0 using either "
+"pip or Poetry:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:12
+msgid "pip: add ``-U`` when installing."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:14
+msgid ""
+"``python -m pip install -U flwr`` (when using ``start_server`` and "
+"``start_client``)"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:15
+msgid ""
+"``python -m pip install -U flwr[simulation]`` (when using "
+"``start_simulation``)"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:17
+msgid ""
+"Poetry: update the ``flwr`` dependency in ``pyproject.toml`` and then "
+"reinstall (don't forget to delete ``poetry.lock`` via ``rm poetry.lock`` "
+"before running ``poetry install``)."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:19
+msgid "``flwr = \"^1.0.0\"`` (when using ``start_server`` and ``start_client``)"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:20
+msgid ""
+"``flwr = { version = \"^1.0.0\", extras = [\"simulation\"] }`` (when "
+"using ``start_simulation``)"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:24
+msgid "Required changes"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:26
+msgid "The following breaking changes require manual updates."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:29
+msgid "General"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:31
+msgid ""
+"Pass all arguments as keyword arguments (not as positional arguments). "
+"Here's an example:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:33
+msgid ""
+"Flower 0.19 (positional arguments): ``start_client(\"127.0.0.1:8080\", "
+"FlowerClient())``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:34
+msgid ""
+"Flower 1.0 (keyword arguments): "
+"``start_client(server_address=\"127.0.0.1:8080\", "
+"client=FlowerClient())``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:39
+msgid ""
+"Subclasses of ``NumPyClient``: change ``def get_parameters(self):``` to "
+"``def get_parameters(self, config):``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:40
+msgid ""
+"Subclasses of ``Client``: change ``def get_parameters(self):``` to ``def "
+"get_parameters(self, ins: GetParametersIns):``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:43
+msgid "Strategies / ``start_server`` / ``start_simulation``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:45
+msgid ""
+"Pass ``ServerConfig`` (instead of a dictionary) to ``start_server`` and "
+"``start_simulation``. Here's an example:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:47
+msgid ""
+"Flower 0.19: ``start_server(..., config={\"num_rounds\": 3, "
+"\"round_timeout\": 600.0}, ...)``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:48
+msgid ""
+"Flower 1.0: ``start_server(..., "
+"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), "
+"...)``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:50
+msgid ""
+"Replace ``num_rounds=1`` in ``start_simulation`` with the new "
+"``config=ServerConfig(...)`` (see previous item)"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:51
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:52
+msgid "Rename parameter/ndarray conversion functions:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:54
+msgid "``parameters_to_weights`` --> ``parameters_to_ndarrays``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:55
+msgid "``weights_to_parameters`` --> ``ndarrays_to_parameters``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:57
+msgid ""
+"Strategy initialization: if the strategy relies on the default values for"
+" ``fraction_fit`` and ``fraction_evaluate``, set ``fraction_fit`` and "
+"``fraction_evaluate`` manually to ``0.1``. Projects that do not manually "
+"create a strategy (by calling ``start_server`` or ``start_simulation`` "
+"without passing a strategy instance) should now manually initialize "
+"FedAvg with ``fraction_fit`` and ``fraction_evaluate`` set to ``0.1``."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:58
+msgid "Rename built-in strategy parameters (e.g., ``FedAvg``):"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:60
+msgid "``fraction_eval`` --> ``fraction_evaluate``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:61
+msgid "``min_eval_clients`` --> ``min_evaluate_clients``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:62
+msgid "``eval_fn`` --> ``evaluate_fn``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:64
+msgid ""
+"Rename ``rnd`` to ``server_round``. This impacts multiple methods and "
+"functions, for example, ``configure_fit``, ``aggregate_fit``, "
+"``configure_evaluate``, ``aggregate_evaluate``, and ``evaluate_fn``."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:65
+msgid "Add ``server_round`` and ``config`` to ``evaluate_fn``:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:67
+msgid ""
+"Flower 0.19: ``def evaluate(parameters: NDArrays) -> "
+"Optional[Tuple[float, Dict[str, Scalar]]]:``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:68
+msgid ""
+"Flower 1.0: ``def evaluate(server_round: int, parameters: NDArrays, "
+"config: Dict[str, Scalar]) -> Optional[Tuple[float, Dict[str, "
+"Scalar]]]:``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:71
+msgid "Custom strategies"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:73
+msgid ""
+"The type of parameter ``failures`` has changed from "
+"``List[BaseException]`` to ``List[Union[Tuple[ClientProxy, FitRes], "
+"BaseException]]`` (in ``aggregate_fit``) and "
+"``List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]]`` (in "
+"``aggregate_evaluate``)"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:74
+msgid ""
+"The ``Strategy`` method ``evaluate`` now receives the current round of "
+"federated learning/evaluation as the first parameter:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:76
+msgid ""
+"Flower 0.19: ``def evaluate(self, parameters: Parameters) -> "
+"Optional[Tuple[float, Dict[str, Scalar]]]:``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:77
+msgid ""
+"Flower 1.0: ``def evaluate(self, server_round: int, parameters: "
+"Parameters) -> Optional[Tuple[float, Dict[str, Scalar]]]:``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:80
+msgid "Optional improvements"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:82
+msgid ""
+"Along with the necessary changes above, there are a number of potential "
+"improvements that just became possible:"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:84
+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."
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:85
+msgid ""
+"Configure the round timeout via ``start_simulation``: "
+"``start_simulation(..., config=flwr.server.ServerConfig(num_rounds=3, "
+"round_timeout=600.0), ...)``"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:89
+msgid "Further help"
+msgstr ""
+
+#: ../../source/how-to-upgrade-to-flower-1.0.rst:91
+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.dev/join-slack/>`_ and use the channgel ``#questions``."
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:2
+msgid "Use strategies"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:4
+msgid ""
+"Flower allows full customization of the learning process through the "
+":code:`Strategy` abstraction. A number of built-in strategies are "
+"provided in the core framework."
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:6
+msgid ""
+"There are three ways to customize the way Flower orchestrates the "
+"learning process on the server side:"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:8
+msgid "Use an existing strategy, for example, :code:`FedAvg`"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:9
+#: ../../source/how-to-use-strategies.rst:40
+msgid "Customize an existing strategy with callback functions"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:10
+#: ../../source/how-to-use-strategies.rst:87
+msgid "Implement a novel strategy"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:14
+msgid "Use an existing strategy"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:16
+msgid ""
+"Flower comes with a number of popular federated learning strategies "
+"built-in. A built-in strategy can be instantiated as follows:"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:25
+msgid ""
+"This creates a strategy with all parameters left at their default values "
+"and passes it to the :code:`start_server` function. It is usually "
+"recommended to adjust a few parameters during instantiation:"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:42
+msgid ""
+"Existing strategies provide several ways to customize their behaviour. "
+"Callback functions allow strategies to call user-provided code during "
+"execution."
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:45
+msgid "Configuring client fit and client evaluate"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:47
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:75
+msgid ""
+"The :code:`on_fit_config_fn` can be used to pass arbitrary configuration "
+"values from server to client, and poetentially change these values each "
+"round, for example, to adjust the learning rate. The client will receive "
+"the dictionary returned by the :code:`on_fit_config_fn` in its own "
+":code:`client.fit()` function."
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:78
+msgid ""
+"Similar to :code:`on_fit_config_fn`, there is also "
+":code:`on_evaluate_config_fn` to customize the configuration sent to "
+":code:`client.evaluate()`"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:81
+msgid "Configuring server-side evaluation"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:83
+msgid ""
+"Server-side evaluation can be enabled by passing an evaluation function "
+"to :code:`evaluate_fn`."
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:89
+msgid ""
+"Writing a fully custom strategy is a bit more involved, but it provides "
+"the most flexibility. Read the `Implementing Strategies <how-to-"
+"implement-strategies.html>`_ guide to learn more."
+msgstr ""
+
+#: ../../source/index.rst:34
+msgid "Tutorial"
+msgstr ""
+
+#: ../../source/index.rst:44
+msgid "Quickstart tutorials"
+msgstr ""
+
+#: ../../source/index.rst:75 ../../source/index.rst:79
+msgid "How-to guides"
+msgstr ""
+
+#: ../../source/index.rst:95
+msgid "Legacy example guides"
+msgstr ""
+
+#: ../../source/index.rst:106 ../../source/index.rst:110
+msgid "Explanations"
+msgstr ""
+
+#: ../../source/index.rst:122
+msgid "API reference"
+msgstr ""
+
+#: ../../source/index.rst:129
+msgid "Reference docs"
+msgstr ""
+
+#: ../../source/index.rst:145
+msgid "Contributor tutorials"
+msgstr ""
+
+#: ../../source/index.rst:152
+msgid "Contributor how-to guides"
+msgstr ""
+
+#: ../../source/index.rst:164
+msgid "Contributor explanations"
+msgstr ""
+
+#: ../../source/index.rst:170
+msgid "Contributor references"
+msgstr ""
+
+#: ../../source/index.rst:-1
+msgid ""
+"Check out the documentation of the main Flower Framework enabling easy "
+"Python development for Federated Learning."
+msgstr ""
+
+#: ../../source/index.rst:2
+msgid "Flower Framework Documentation"
+msgstr ""
+
+#: ../../source/index.rst:7
+msgid ""
+"Welcome to Flower's documentation. `Flower <https://flower.dev>`_ is a "
+"friendly federated learning framework."
+msgstr ""
+
+#: ../../source/index.rst:11
+msgid "Join the Flower Community"
+msgstr ""
+
+#: ../../source/index.rst:13
+msgid ""
+"The Flower Community is growing quickly - we're a friendly group of "
+"researchers, engineers, students, professionals, academics, and other "
+"enthusiasts."
+msgstr ""
+
+#: ../../source/index.rst:15
+msgid "Join us on Slack"
+msgstr ""
+
+#: ../../source/index.rst:23
+msgid "Flower Framework"
+msgstr ""
+
+#: ../../source/index.rst:25
+msgid ""
+"The user guide is targeted at researchers and developers who want to use "
+"Flower to bring existing machine learning workloads into a federated "
+"setting. One of Flower's design goals was to make this simple. Read on to"
+" learn more."
+msgstr ""
+
+#: ../../source/index.rst:30
+msgid "Tutorials"
+msgstr ""
+
+#: ../../source/index.rst:32
+msgid ""
+"A learning-oriented series of federated learning tutorials, the best "
+"place to start."
+msgstr ""
+
+#: ../../source/index.rst:62
+msgid ""
+"QUICKSTART TUTORIALS: :doc:`PyTorch <tutorial-quickstart-pytorch>` | "
+":doc:`TensorFlow <tutorial-quickstart-tensorflow>` | :doc:`🤗 Transformers"
+" <tutorial-quickstart-huggingface>` | :doc:`JAX <tutorial-quickstart-"
+"jax>` | :doc:`Pandas <tutorial-quickstart-pandas>` | :doc:`fastai "
+"<tutorial-quickstart-fastai>` | :doc:`PyTorch Lightning <tutorial-"
+"quickstart-pytorch-lightning>` | :doc:`MXNet <tutorial-quickstart-mxnet>`"
+" | :doc:`scikit-learn <tutorial-quickstart-scikitlearn>` | :doc:`XGBoost "
+"<tutorial-quickstart-xgboost>` | :doc:`Android <tutorial-quickstart-"
+"android>` | :doc:`iOS <tutorial-quickstart-ios>`"
+msgstr ""
+
+#: ../../source/index.rst:64
+msgid "We also made video tutorials for PyTorch:"
+msgstr ""
+
+#: ../../source/index.rst:69
+msgid "And TensorFlow:"
+msgstr ""
+
+#: ../../source/index.rst:77
+msgid ""
+"Problem-oriented how-to guides show step-by-step how to achieve a "
+"specific goal."
+msgstr ""
+
+#: ../../source/index.rst:108
+msgid ""
+"Understanding-oriented concept guides explain and discuss key topics and "
+"underlying ideas behind Flower and collaborative AI."
+msgstr ""
+
+#: ../../source/index.rst:118
+msgid "References"
+msgstr ""
+
+#: ../../source/index.rst:120
+msgid "Information-oriented API reference and other reference material."
+msgstr ""
+
+#: ../../source/index.rst:140
+msgid "Contributor docs"
+msgstr ""
+
+#: ../../source/index.rst:142
+msgid ""
+"The Flower community welcomes contributions. The following docs are "
+"intended to help along the way."
+msgstr ""
+
+#: ../../source/ref-api-cli.rst:2
+msgid "Flower CLI reference"
+msgstr ""
+
+#: ../../source/ref-api-cli.rst:7
+msgid "flower-server"
+msgstr ""
+
+#: ../../source/ref-api-cli.rst:17
+msgid "flower-driver-api"
+msgstr ""
+
+#: ../../source/ref-api-cli.rst:27
+msgid "flower-fleet-api"
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:2
+msgid "flwr (Python API reference)"
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:8
+msgid "client"
+msgstr ""
+
+#: flwr.client:1 of
+msgid "Flower client."
+msgstr ""
+
+#: flwr.client.client.Client:1 of
+msgid "Abstract base class for Flower clients."
+msgstr ""
+
+#: flwr.client.client.Client.evaluate:1
+#: flwr.client.numpy_client.NumPyClient.evaluate:1 of
+msgid "Evaluate the provided parameters using the locally held dataset."
+msgstr ""
+
+#: flwr.client.app.start_client flwr.client.app.start_numpy_client
+#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit
+#: flwr.client.client.Client.get_parameters
+#: flwr.client.client.Client.get_properties
+#: flwr.client.numpy_client.NumPyClient.evaluate
+#: flwr.client.numpy_client.NumPyClient.fit
+#: flwr.client.numpy_client.NumPyClient.get_parameters
+#: flwr.client.numpy_client.NumPyClient.get_properties
+#: flwr.server.app.start_server flwr.server.strategy.bulyan.Bulyan.__init__
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__
+#: flwr.server.strategy.fedadam.FedAdam.__init__
+#: flwr.server.strategy.fedavg.FedAvg.__init__
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__
+#: flwr.server.strategy.fedopt.FedOpt.__init__
+#: flwr.server.strategy.fedprox.FedProx.__init__
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__
+#: flwr.server.strategy.fedyogi.FedYogi.__init__
+#: flwr.server.strategy.krum.Krum.__init__
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate
+#: flwr.server.strategy.strategy.Strategy.configure_fit
+#: flwr.server.strategy.strategy.Strategy.evaluate
+#: flwr.server.strategy.strategy.Strategy.initialize_parameters
+#: flwr.simulation.app.start_simulation of
+msgid "Parameters"
+msgstr ""
+
+#: flwr.client.client.Client.evaluate:3 of
+msgid ""
+"The evaluation instructions containing (global) model parameters received"
+" from the server and a dictionary of configuration values used to "
+"customize the local evaluation process."
+msgstr ""
+
+#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit
+#: flwr.client.client.Client.get_parameters
+#: flwr.client.client.Client.get_properties
+#: flwr.client.numpy_client.NumPyClient.evaluate
+#: flwr.client.numpy_client.NumPyClient.fit
+#: flwr.client.numpy_client.NumPyClient.get_parameters
+#: flwr.client.numpy_client.NumPyClient.get_properties
+#: flwr.server.app.start_server
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate
+#: flwr.server.strategy.strategy.Strategy.configure_fit
+#: flwr.server.strategy.strategy.Strategy.evaluate
+#: flwr.server.strategy.strategy.Strategy.initialize_parameters
+#: flwr.simulation.app.start_simulation of
+msgid "Returns"
+msgstr ""
+
+#: flwr.client.client.Client.evaluate:8 of
+msgid ""
+"The evaluation result containing the loss on the local dataset and other "
+"details such as the number of local data examples used for evaluation."
+msgstr ""
+
+#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit
+#: flwr.client.client.Client.get_parameters
+#: flwr.client.client.Client.get_properties
+#: flwr.client.numpy_client.NumPyClient.get_parameters
+#: flwr.client.numpy_client.NumPyClient.get_properties
+#: flwr.server.app.start_server
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate
+#: flwr.server.strategy.strategy.Strategy.configure_fit
+#: flwr.server.strategy.strategy.Strategy.evaluate
+#: flwr.server.strategy.strategy.Strategy.initialize_parameters
+#: flwr.simulation.app.start_simulation of
+msgid "Return type"
+msgstr ""
+
+#: flwr.client.client.Client.fit:1 of
+msgid "Refine the provided parameters using the locally held dataset."
+msgstr ""
+
+#: flwr.client.client.Client.fit:3 of
+msgid ""
+"The training instructions containing (global) model parameters received "
+"from the server and a dictionary of configuration values used to "
+"customize the local training process."
+msgstr ""
+
+#: flwr.client.client.Client.fit:8 of
+msgid ""
+"The training result containing updated parameters and other details such "
+"as the number of local training examples used for training."
+msgstr ""
+
+#: flwr.client.client.Client.get_parameters:1
+#: flwr.client.numpy_client.NumPyClient.get_parameters:1 of
+msgid "Return the current local model parameters."
+msgstr ""
+
+#: flwr.client.client.Client.get_parameters:3 of
+msgid ""
+"The get parameters instructions received from the server containing a "
+"dictionary of configuration values."
+msgstr ""
+
+#: flwr.client.client.Client.get_parameters:7 of
+msgid "The current local model parameters."
+msgstr ""
+
+#: flwr.client.client.Client.get_properties:1 of
+msgid "Return set of client's properties."
+msgstr ""
+
+#: flwr.client.client.Client.get_properties:3 of
+msgid ""
+"The get properties instructions received from the server containing a "
+"dictionary of configuration values."
+msgstr ""
+
+#: flwr.client.client.Client.get_properties:7 of
+msgid "The current client properties."
+msgstr ""
+
+#: flwr.client.client.Client.to_client:1 of
+msgid "Return client (itself)."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:24
+msgid "start_client"
+msgstr ""
+
+#: flwr.client.app.start_client:1 of
+msgid "Start a Flower client node which connects to a Flower server."
+msgstr ""
+
+#: flwr.client.app.start_client:3 flwr.client.app.start_numpy_client:3 of
+msgid ""
+"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 "
+"`\"[::]:8080\"`."
+msgstr ""
+
+#: flwr.client.app.start_client:7 of
+msgid "..."
+msgstr ""
+
+#: flwr.client.app.start_client:9 of
+msgid "A callable that instantiates a Client. (default: None)"
+msgstr ""
+
+#: flwr.client.app.start_client:11 of
+msgid ""
+"An implementation of the abstract base class `flwr.client.Client` "
+"(default: None)"
+msgstr ""
+
+#: flwr.client.app.start_client:14 flwr.client.app.start_numpy_client:9 of
+msgid ""
+"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 very large models might need to increase this value. Note that the "
+"Flower server needs to be started with the same value (see "
+"`flwr.server.start_server`), otherwise it will not know about the "
+"increased limit and block larger messages."
+msgstr ""
+
+#: flwr.client.app.start_client:21 flwr.client.app.start_numpy_client:16 of
+msgid ""
+"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."
+msgstr ""
+
+#: flwr.client.app.start_client:25 flwr.client.app.start_numpy_client:20 of
+msgid ""
+"Configure the transport layer. Allowed values: - 'grpc-bidi': gRPC, "
+"bidirectional streaming - 'grpc-rere': gRPC, request-response "
+"(experimental) - 'rest': HTTP (experimental)"
+msgstr ""
+
+#: flwr.client.app.start_client:32 flwr.client.app.start_numpy_client:27
+#: flwr.server.app.start_server:41 of
+msgid "Examples"
+msgstr ""
+
+#: flwr.client.app.start_client:33 of
+msgid "Starting a gRPC client with an insecure server connection:"
+msgstr ""
+
+#: flwr.client.app.start_client:43 flwr.client.app.start_numpy_client:35 of
+msgid "Starting an SSL-enabled gRPC client:"
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:32
+msgid "NumPyClient"
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient:1 of
+msgid "Abstract base class for Flower clients using NumPy."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:3
+#: flwr.client.numpy_client.NumPyClient.fit:3
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:5
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:8
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate:5
+#: flwr.server.strategy.strategy.Strategy.configure_fit:5
+#: flwr.server.strategy.strategy.Strategy.evaluate:8 of
+msgid "The current (global) model parameters."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:5 of
+msgid ""
+"Configuration parameters which allow the server to influence evaluation "
+"on the client. It can be used to communicate arbitrary values from the "
+"server to the client, for example, to influence the number of examples "
+"used for evaluation."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:11 of
+msgid ""
+"* **loss** (*float*) -- The evaluation loss of the model on the local "
+"dataset. * **num_examples** (*int*) -- The number of examples used for "
+"evaluation. * **metrics** (*Dict[str, Scalar]*) -- A dictionary mapping "
+"arbitrary string keys to values of   type bool, bytes, float, int, or "
+"str. It can be used to   communicate arbitrary values back to the server."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:11 of
+msgid ""
+"**loss** (*float*) -- The evaluation loss of the model on the local "
+"dataset."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:12 of
+msgid "**num_examples** (*int*) -- The number of examples used for evaluation."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:13
+#: flwr.client.numpy_client.NumPyClient.fit:13 of
+msgid ""
+"**metrics** (*Dict[str, Scalar]*) -- A dictionary mapping arbitrary "
+"string keys to values of type bool, bytes, float, int, or str. It can be "
+"used to communicate arbitrary values back to the server."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:19 of
+msgid ""
+"The previous return type format (int, float, float) and the extended "
+"format (int, float, float, Dict[str, Scalar]) have been deprecated and "
+"removed since Flower 0.19."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.fit:1 of
+msgid "Train the provided parameters using the locally held dataset."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.fit:5 of
+msgid ""
+"Configuration parameters which allow the server to influence training on "
+"the client. It can be used to communicate arbitrary values from the "
+"server to the client, for example, to set the number of (local) training "
+"epochs."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.fit:11 of
+msgid ""
+"* **parameters** (*NDArrays*) -- The locally updated model parameters. * "
+"**num_examples** (*int*) -- The number of examples used for training. * "
+"**metrics** (*Dict[str, Scalar]*) -- A dictionary mapping arbitrary "
+"string keys to values of type   bool, bytes, float, int, or str. It can "
+"be used to communicate   arbitrary values back to the server."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.fit:11 of
+msgid "**parameters** (*NDArrays*) -- The locally updated model parameters."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.fit:12 of
+msgid "**num_examples** (*int*) -- The number of examples used for training."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.get_parameters:3 of
+msgid ""
+"Configuration parameters requested by the server. This can be used to "
+"tell the client which parameters are needed along with some Scalar "
+"attributes."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.get_parameters:8 of
+msgid "**parameters** -- The local model parameters as a list of NumPy ndarrays."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.get_properties:1 of
+msgid "Return a client's set of properties."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.get_properties:3 of
+msgid ""
+"Configuration parameters requested by the server. This can be used to "
+"tell the client which properties are needed along with some Scalar "
+"attributes."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.get_properties:8 of
+msgid ""
+"**properties** -- A dictionary mapping arbitrary string keys to values of"
+" type bool, bytes, float, int, or str. It can be used to communicate "
+"arbitrary property values back to the server."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.to_client:1 of
+msgid "Convert to object to Client type and return it."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:41
+msgid "start_numpy_client"
+msgstr ""
+
+#: flwr.client.app.start_numpy_client:1 of
+msgid "Start a Flower NumPyClient which connects to a gRPC server."
+msgstr ""
+
+#: flwr.client.app.start_numpy_client:7 of
+msgid "An implementation of the abstract base class `flwr.client.NumPyClient`."
+msgstr ""
+
+#: flwr.client.app.start_numpy_client:28 of
+msgid "Starting a client with an insecure server connection:"
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:49
+msgid "start_simulation"
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:1 of
+msgid "Start a Ray-based Flower simulation server."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:3 of
+msgid ""
+"A function creating client instances. The function must take a single "
+"`str` argument called `cid`. It should return a single client instance of"
+" type Client. Note that the created client instances are ephemeral and "
+"will often be destroyed after a single method invocation. Since client "
+"instances are not long-lived, they should not attempt to carry state over"
+" method invocations. Any state required by the instance (model, dataset, "
+"hyperparameters, ...) should be (re-)created in either the call to "
+"`client_fn` or the call to any of the client methods (e.g., load "
+"evaluation data in the `evaluate` method itself)."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:13 of
+msgid ""
+"The total number of clients in this simulation. This must be set if "
+"`clients_ids` is not set and vice-versa."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:16 of
+msgid ""
+"List `client_id`s for each client. This is only required if `num_clients`"
+" is not set. Setting both `num_clients` and `clients_ids` with "
+"`len(clients_ids)` not equal to `num_clients` generates an error."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:20 of
+msgid ""
+"\"num_gpus\": 0.0}` CPU and GPU resources for a single client. Supported "
+"keys are `num_cpus` and `num_gpus`. To understand the GPU utilization "
+"caused by `num_gpus`, as well as using custom resources, please consult "
+"the Ray documentation."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:25 of
+msgid ""
+"An implementation of the abstract base class `flwr.server.Server`. If no "
+"instance is provided, then `start_server` will create one."
+msgstr ""
+
+#: flwr.server.app.start_server:9 flwr.simulation.app.start_simulation:28 of
+msgid ""
+"Currently supported values are `num_rounds` (int, default: 1) and "
+"`round_timeout` in seconds (float, default: None)."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:31 of
+msgid ""
+"An implementation of the abstract base class `flwr.server.Strategy`. If "
+"no strategy is provided, then `start_server` will use "
+"`flwr.server.strategy.FedAvg`."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:35 of
+msgid ""
+"An implementation of the abstract base class `flwr.server.ClientManager`."
+" If no implementation is provided, then `start_simulation` will use "
+"`flwr.server.client_manager.SimpleClientManager`."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:39 of
+msgid ""
+"Optional dictionary containing arguments for the call to `ray.init`. If "
+"ray_init_args is None (the default), Ray will be initialized with the "
+"following default args:  { \"ignore_reinit_error\": True, "
+"\"include_dashboard\": False }  An empty dictionary can be used "
+"(ray_init_args={}) to prevent any arguments from being passed to "
+"ray.init."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:39 of
+msgid ""
+"Optional dictionary containing arguments for the call to `ray.init`. If "
+"ray_init_args is None (the default), Ray will be initialized with the "
+"following default args:"
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:43 of
+msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }"
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:45 of
+msgid ""
+"An empty dictionary can be used (ray_init_args={}) to prevent any "
+"arguments from being passed to ray.init."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:48 of
+msgid ""
+"Set to True to prevent `ray.shutdown()` in case "
+"`ray.is_initialized()=True`."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:50 of
+msgid ""
+"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)."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:54 of
+msgid ""
+"If you want to create your own Actor classes, you might need to pass some"
+" input argument. You can use this dictionary for such purpose."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:57 of
+msgid ""
+"(default: \"DEFAULT\") Optional string (\"DEFAULT\" or \"SPREAD\") for "
+"the VCE to choose in which node the actor is placed. If you are an "
+"advanced user needed more control you can use lower-level scheduling "
+"strategies to pin actors to specific compute nodes (e.g. via "
+"NodeAffinitySchedulingStrategy). Please note this is an advanced feature."
+" For all details, please refer to the Ray documentation: "
+"https://docs.ray.io/en/latest/ray-core/scheduling/index.html"
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:66 of
+msgid "**hist** -- Object containing metrics from training."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:57
+msgid "server"
+msgstr ""
+
+#: flwr.server:1 of
+msgid "Flower server."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:65
+msgid "server.start_server"
+msgstr ""
+
+#: flwr.server.app.start_server:1 of
+msgid "Start a Flower server using the gRPC transport layer."
+msgstr ""
+
+#: flwr.server.app.start_server:3 of
+msgid "The IPv4 or IPv6 address of the server. Defaults to `\"[::]:8080\"`."
+msgstr ""
+
+#: flwr.server.app.start_server:5 of
+msgid ""
+"A server implementation, either `flwr.server.Server` or a subclass "
+"thereof. If no instance is provided, then `start_server` will create one."
+msgstr ""
+
+#: flwr.server.app.start_server:12 of
+msgid ""
+"An implementation of the abstract base class "
+"`flwr.server.strategy.Strategy`. If no strategy is provided, then "
+"`start_server` will use `flwr.server.strategy.FedAvg`."
+msgstr ""
+
+#: flwr.server.app.start_server:16 of
+msgid ""
+"An implementation of the abstract base class `flwr.server.ClientManager`."
+" If no implementation is provided, then `start_server` will use "
+"`flwr.server.client_manager.SimpleClientManager`."
+msgstr ""
+
+#: flwr.server.app.start_server:21 of
+msgid ""
+"The maximum length of gRPC messages that can be exchanged with the Flower"
+" clients. The default should be sufficient for most models. Users who "
+"train very large models might need to increase this value. Note that the "
+"Flower clients need to be started with the same value (see "
+"`flwr.client.start_client`), otherwise clients will not know about the "
+"increased limit and block larger messages."
+msgstr ""
+
+#: flwr.server.app.start_server:28 of
+msgid ""
+"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."
+msgstr ""
+
+#: flwr.server.app.start_server:28 of
+msgid ""
+"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:"
+msgstr ""
+
+#: flwr.server.app.start_server:32 of
+msgid "CA certificate."
+msgstr ""
+
+#: flwr.server.app.start_server:33 of
+msgid "server certificate."
+msgstr ""
+
+#: flwr.server.app.start_server:34 of
+msgid "server private key."
+msgstr ""
+
+#: flwr.server.app.start_server:37 of
+msgid "**hist** -- Object containing training and evaluation metrics."
+msgstr ""
+
+#: flwr.server.app.start_server:42 of
+msgid "Starting an insecure server:"
+msgstr ""
+
+#: flwr.server.app.start_server:46 of
+msgid "Starting an SSL-enabled server:"
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:73
+msgid "server.strategy"
+msgstr ""
+
+#: flwr.server.strategy:1 of
+msgid "Contains the strategy abstraction and different implementations."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:81
+msgid "server.strategy.Strategy"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy:1 of
+msgid "Abstract base class for server strategy implementations."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1 of
+msgid "Aggregate evaluation results."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:3
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:6
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:3
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit:3
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate:3
+#: flwr.server.strategy.strategy.Strategy.configure_fit:3
+#: flwr.server.strategy.strategy.Strategy.evaluate:6 of
+msgid "The current round of federated learning."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:5 of
+msgid ""
+"Successful updates from the previously selected and configured clients. "
+"Each pair of `(ClientProxy, FitRes` constitutes a successful update from "
+"one of the previously selected clients. Not that not all previously "
+"selected clients are necessarily included in this list: a client might "
+"drop out and not submit a result. For each client that did not submit an "
+"update, there should be an `Exception` in `failures`."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:13
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit:13 of
+msgid "Exceptions that occurred while the server was waiting for client updates."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:16 of
+msgid ""
+"**aggregation_result** -- The aggregated evaluation result. Aggregation "
+"typically uses some variant of a weighted average."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit:1 of
+msgid "Aggregate training results."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit:5 of
+msgid ""
+"Successful updates from the previously selected and configured clients. "
+"Each pair of `(ClientProxy, FitRes)` constitutes a successful update from"
+" one of the previously selected clients. Not that not all previously "
+"selected clients are necessarily included in this list: a client might "
+"drop out and not submit a result. For each client that did not submit an "
+"update, there should be an `Exception` in `failures`."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit:17 of
+msgid ""
+"**parameters** -- If parameters are returned, then the server will treat "
+"these as the new global model parameters (i.e., it will replace the "
+"previous parameters with the ones returned from this method). If `None` "
+"is returned (e.g., because there were only failures and no viable "
+"results) then the server will no update the previous model parameters, "
+"the updates received in this round are discarded, and the global model "
+"parameters remain the same."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.configure_evaluate:1
+#: flwr.server.strategy.qfedavg.QFedAvg.configure_evaluate:1
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate:1 of
+msgid "Configure the next round of evaluation."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:7
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:10
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate:7
+#: flwr.server.strategy.strategy.Strategy.configure_fit:7
+#: flwr.server.strategy.strategy.Strategy.initialize_parameters:3 of
+msgid "The client manager which holds all currently connected clients."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:10
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate:10 of
+msgid ""
+"**evaluate_configuration** -- A list of tuples. Each tuple in the list "
+"identifies a `ClientProxy` and the `EvaluateIns` for this particular "
+"`ClientProxy`. If a particular `ClientProxy` is not included in this "
+"list, it means that this `ClientProxy` will not participate in the next "
+"round of federated evaluation."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.configure_fit:1
+#: flwr.server.strategy.fedavg.FedAvg.configure_fit:1
+#: flwr.server.strategy.fedprox.FedProx.configure_fit:1
+#: flwr.server.strategy.qfedavg.QFedAvg.configure_fit:1
+#: flwr.server.strategy.strategy.Strategy.configure_fit:1 of
+msgid "Configure the next round of training."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:13
+#: flwr.server.strategy.strategy.Strategy.configure_fit:10 of
+msgid ""
+"**fit_configuration** -- A list of tuples. Each tuple in the list "
+"identifies a `ClientProxy` and the `FitIns` for this particular "
+"`ClientProxy`. If a particular `ClientProxy` is not included in this "
+"list, it means that this `ClientProxy` will not participate in the next "
+"round of federated learning."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.evaluate:1 of
+msgid "Evaluate the current model parameters."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.evaluate:3 of
+msgid ""
+"This function can be used to perform centralized (i.e., server-side) "
+"evaluation of model parameters."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.evaluate:11 of
+msgid ""
+"**evaluation_result** -- The evaluation result, usually a Tuple "
+"containing loss and a dictionary containing task-specific metrics (e.g., "
+"accuracy)."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.initialize_parameters:1 of
+msgid "Initialize the (global) model parameters."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.initialize_parameters:6 of
+msgid ""
+"**parameters** -- If parameters are returned, then the server will treat "
+"these as the initial global model parameters."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:90
+msgid "server.strategy.FedAvg"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg:1 of
+msgid "Configurable FedAvg strategy implementation."
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:1
+#: flwr.server.strategy.fedavg.FedAvg.__init__:1
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:1 of
+msgid "Federated Averaging strategy."
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:3
+#: flwr.server.strategy.fedavg.FedAvg.__init__:3
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:3 of
+msgid "Implementation based on https://arxiv.org/abs/1602.05629"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:5
+#: flwr.server.strategy.fedavg.FedAvg.__init__:5
+#: flwr.server.strategy.fedprox.FedProx.__init__:37
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:5 of
+msgid ""
+"Fraction of clients used during training. In case `min_fit_clients` is "
+"larger than `fraction_fit * available_clients`, `min_fit_clients` will "
+"still be sampled. Defaults to 1.0."
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:9
+#: flwr.server.strategy.fedavg.FedAvg.__init__:9
+#: flwr.server.strategy.fedprox.FedProx.__init__:41
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:9 of
+msgid ""
+"Fraction of clients used during validation. In case "
+"`min_evaluate_clients` is larger than `fraction_evaluate * "
+"available_clients`, `min_evaluate_clients` will still be sampled. "
+"Defaults to 1.0."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:9
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:13
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:9
+#: flwr.server.strategy.fedadam.FedAdam.__init__:9
+#: flwr.server.strategy.fedavg.FedAvg.__init__:13
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:9
+#: flwr.server.strategy.fedopt.FedOpt.__init__:9
+#: flwr.server.strategy.fedprox.FedProx.__init__:45
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:7
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:9
+#: flwr.server.strategy.krum.Krum.__init__:7
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:13 of
+msgid "Minimum number of clients used during training. Defaults to 2."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:11
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:15
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:11
+#: flwr.server.strategy.fedadam.FedAdam.__init__:11
+#: flwr.server.strategy.fedavg.FedAvg.__init__:15
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:11
+#: flwr.server.strategy.fedopt.FedOpt.__init__:11
+#: flwr.server.strategy.fedprox.FedProx.__init__:47
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:9
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:11
+#: flwr.server.strategy.krum.Krum.__init__:9
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:15 of
+msgid "Minimum number of clients used during validation. Defaults to 2."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:13
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:17
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:13
+#: flwr.server.strategy.fedadam.FedAdam.__init__:13
+#: flwr.server.strategy.fedavg.FedAvg.__init__:17
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:13
+#: flwr.server.strategy.fedopt.FedOpt.__init__:13
+#: flwr.server.strategy.fedprox.FedProx.__init__:49
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:11
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:13
+#: flwr.server.strategy.krum.Krum.__init__:11
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:17 of
+msgid "Minimum number of total clients in the system. Defaults to 2."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:17
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:19
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:15
+#: flwr.server.strategy.fedadam.FedAdam.__init__:15
+#: flwr.server.strategy.fedavg.FedAvg.__init__:19
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:15
+#: flwr.server.strategy.fedopt.FedOpt.__init__:15
+#: flwr.server.strategy.fedprox.FedProx.__init__:51
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:13
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:15
+#: flwr.server.strategy.krum.Krum.__init__:18
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:19 of
+msgid "Optional function used for validation. Defaults to None."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:19
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:21
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:17
+#: flwr.server.strategy.fedadam.FedAdam.__init__:17
+#: flwr.server.strategy.fedavg.FedAvg.__init__:21
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:17
+#: flwr.server.strategy.fedopt.FedOpt.__init__:17
+#: flwr.server.strategy.fedprox.FedProx.__init__:53
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:15
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:17
+#: flwr.server.strategy.krum.Krum.__init__:20
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:21 of
+msgid "Function used to configure training. Defaults to None."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:21
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:23
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:19
+#: flwr.server.strategy.fedadam.FedAdam.__init__:19
+#: flwr.server.strategy.fedavg.FedAvg.__init__:23
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:19
+#: flwr.server.strategy.fedopt.FedOpt.__init__:19
+#: flwr.server.strategy.fedprox.FedProx.__init__:55
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:17
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:19
+#: flwr.server.strategy.krum.Krum.__init__:22
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:23 of
+msgid "Function used to configure validation. Defaults to None."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:23
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:25
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:25
+#: flwr.server.strategy.fedadam.FedAdam.__init__:21
+#: flwr.server.strategy.fedavg.FedAvg.__init__:25
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:21
+#: flwr.server.strategy.fedopt.FedOpt.__init__:21
+#: flwr.server.strategy.fedprox.FedProx.__init__:57
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:19
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:21
+#: flwr.server.strategy.krum.Krum.__init__:24
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:25 of
+msgid "Whether or not accept rounds containing failures. Defaults to True."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:25
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:27
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:27
+#: flwr.server.strategy.fedadam.FedAdam.__init__:23
+#: flwr.server.strategy.fedavg.FedAvg.__init__:27
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:23
+#: flwr.server.strategy.fedopt.FedOpt.__init__:23
+#: flwr.server.strategy.fedprox.FedProx.__init__:59
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:21
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:23
+#: flwr.server.strategy.krum.Krum.__init__:26
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:27 of
+msgid "Initial global model parameters."
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:29
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:31
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:21
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:23
+#: flwr.server.strategy.fedadam.FedAdam.__init__:25
+#: flwr.server.strategy.fedadam.FedAdam.__init__:27
+#: flwr.server.strategy.fedavg.FedAvg.__init__:29
+#: flwr.server.strategy.fedavg.FedAvg.__init__:31
+#: flwr.server.strategy.fedopt.FedOpt.__init__:25
+#: flwr.server.strategy.fedopt.FedOpt.__init__:27
+#: flwr.server.strategy.fedprox.FedProx.__init__:61
+#: flwr.server.strategy.fedprox.FedProx.__init__:63
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:25
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:27
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:29
+#: flwr.server.strategy.qfedavg.QFedAvg.__init__:31 of
+msgid "Metrics aggregation function, optional."
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1 of
+msgid "Aggregate evaluation losses using weighted average."
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_fit:1
+#: flwr.server.strategy.fedadagrad.FedAdagrad.aggregate_fit:1
+#: flwr.server.strategy.fedadam.FedAdam.aggregate_fit:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_fit:1
+#: flwr.server.strategy.fedavgm.FedAvgM.aggregate_fit:1
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.aggregate_fit:1
+#: flwr.server.strategy.fedyogi.FedYogi.aggregate_fit:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_fit:1 of
+msgid "Aggregate fit results using weighted average."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.evaluate:1
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.evaluate:1 of
+msgid "Evaluate model parameters using an evaluation function."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.initialize_parameters:1
+#: flwr.server.strategy.fedavgm.FedAvgM.initialize_parameters:1 of
+msgid "Initialize global model parameters."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.num_evaluation_clients:1
+#: flwr.server.strategy.qfedavg.QFedAvg.num_evaluation_clients:1 of
+msgid "Use a fraction of available clients for evaluation."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.num_fit_clients:1
+#: flwr.server.strategy.qfedavg.QFedAvg.num_fit_clients:1 of
+msgid "Return the sample size and the required number of available clients."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:101
+msgid "server.strategy.FedAvgM"
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM:1
+#: flwr.server.strategy.fedmedian.FedMedian:1 of
+msgid "Configurable FedAvg with Momentum strategy implementation."
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:1 of
+msgid "Federated Averaging with Momentum strategy."
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:3 of
+msgid "Implementation based on https://arxiv.org/pdf/1909.06335.pdf"
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:5
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:3
+#: flwr.server.strategy.krum.Krum.__init__:3 of
+msgid "Fraction of clients used during training. Defaults to 0.1."
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:7
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:5
+#: flwr.server.strategy.krum.Krum.__init__:5 of
+msgid "Fraction of clients used during validation. Defaults to 0.1."
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:25 of
+msgid ""
+"Server-side learning rate used in server-side optimization. Defaults to "
+"1.0."
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM.__init__:28 of
+msgid "Server-side momentum factor used for FedAvgM. Defaults to 0.0."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:112
+msgid "server.strategy.FedMedian"
+msgstr ""
+
+#: flwr.server.strategy.fedmedian.FedMedian.aggregate_fit:1 of
+msgid "Aggregate fit results using median."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:122
+msgid "server.strategy.QFedAvg"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg:1 of
+msgid "Configurable QFedAvg strategy implementation."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:133
+msgid "server.strategy.FaultTolerantFedAvg"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 of
+msgid "Configurable fault-tolerant FedAvg strategy implementation."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:144
+msgid "server.strategy.FedOpt"
+msgstr ""
+
+#: flwr.server.strategy.fedopt.FedOpt:1 of
+msgid "Configurable FedAdagrad strategy implementation."
+msgstr ""
+
+#: flwr.server.strategy.fedopt.FedOpt.__init__:1 of
+msgid "Federated Optim strategy interface."
+msgstr ""
+
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:3
+#: flwr.server.strategy.fedadam.FedAdam.__init__:3
+#: flwr.server.strategy.fedopt.FedOpt.__init__:3
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:3 of
+msgid "Implementation based on https://arxiv.org/abs/2003.00295v5"
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:5
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:5
+#: flwr.server.strategy.fedadam.FedAdam.__init__:5
+#: flwr.server.strategy.fedopt.FedOpt.__init__:5
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:5 of
+msgid "Fraction of clients used during training. Defaults to 1.0."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:7
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:7
+#: flwr.server.strategy.fedadam.FedAdam.__init__:7
+#: flwr.server.strategy.fedopt.FedOpt.__init__:7
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:7 of
+msgid "Fraction of clients used during validation. Defaults to 1.0."
+msgstr ""
+
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:29
+#: flwr.server.strategy.fedadam.FedAdam.__init__:29
+#: flwr.server.strategy.fedopt.FedOpt.__init__:29
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:29 of
+msgid "Server-side learning rate. Defaults to 1e-1."
+msgstr ""
+
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:31
+#: flwr.server.strategy.fedadam.FedAdam.__init__:31
+#: flwr.server.strategy.fedopt.FedOpt.__init__:31
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:31 of
+msgid "Client-side learning rate. Defaults to 1e-1."
+msgstr ""
+
+#: flwr.server.strategy.fedopt.FedOpt.__init__:33 of
+msgid "Momentum parameter. Defaults to 0.0."
+msgstr ""
+
+#: flwr.server.strategy.fedopt.FedOpt.__init__:35 of
+msgid "Second moment parameter. Defaults to 0.0."
+msgstr ""
+
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:33
+#: flwr.server.strategy.fedadam.FedAdam.__init__:37
+#: flwr.server.strategy.fedopt.FedOpt.__init__:37
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:37 of
+msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-9."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:155
+msgid "server.strategy.FedProx"
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx:1 of
+msgid "Configurable FedProx strategy implementation."
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:1 of
+msgid "Federated Optimization strategy."
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:3 of
+msgid "Implementation based on https://arxiv.org/abs/1812.06127"
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:5 of
+msgid ""
+"The strategy in itself will not be different than FedAvg, the client "
+"needs to be adjusted. A proximal term needs to be added to the loss "
+"function during the training:"
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:9 of
+msgid ""
+"\\\\frac{\\\\mu}{2} || w - w^t ||^2\n"
+"\n"
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:12 of
+msgid ""
+"Where $w^t$ are the global parameters and $w$ are the local weights the "
+"function will be optimized with."
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:15 of
+msgid "In PyTorch, for example, the loss would go from:"
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:21 of
+msgid "To:"
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:30 of
+msgid ""
+"With `global_params` being a copy of the parameters before the training "
+"takes place."
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.__init__:65 of
+msgid ""
+"The weight of the proximal term used in the optimization. 0.0 makes this "
+"strategy equivalent to FedAvg, and the higher the coefficient, the more "
+"regularization will be used (that is, the client parameters will need to "
+"be closer to the server parameters during training)."
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx.configure_fit:3 of
+msgid "Sends the proximal factor mu to the clients"
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:166
+msgid "server.strategy.FedAdagrad"
+msgstr ""
+
+#: flwr.server.strategy.fedadagrad.FedAdagrad:1 of
+msgid "FedAdagrad strategy - Adaptive Federated Optimization using Adagrad."
+msgstr ""
+
+#: flwr.server.strategy.fedadagrad.FedAdagrad:3
+#: flwr.server.strategy.fedadam.FedAdam:3
+#: flwr.server.strategy.fedyogi.FedYogi:5 of
+msgid "Paper: https://arxiv.org/abs/2003.00295"
+msgstr ""
+
+#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:1
+#: flwr.server.strategy.fedadam.FedAdam.__init__:1 of
+msgid "Federated learning strategy using Adagrad on server-side."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:177
+msgid "server.strategy.FedAdam"
+msgstr ""
+
+#: flwr.server.strategy.fedadam.FedAdam:1 of
+msgid "FedAdam - Adaptive Federated Optimization using Adam."
+msgstr ""
+
+#: flwr.server.strategy.fedadam.FedAdam.__init__:33
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:33 of
+msgid "Momentum parameter. Defaults to 0.9."
+msgstr ""
+
+#: flwr.server.strategy.fedadam.FedAdam.__init__:35
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:35 of
+msgid "Second moment parameter. Defaults to 0.99."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:188
+msgid "server.strategy.FedYogi"
+msgstr ""
+
+#: flwr.server.strategy.fedyogi.FedYogi:1 of
+msgid "FedYogi [Reddi et al., 2020] strategy."
+msgstr ""
+
+#: flwr.server.strategy.fedyogi.FedYogi:3 of
+msgid "Adaptive Federated Optimization using Yogi."
+msgstr ""
+
+#: flwr.server.strategy.fedyogi.FedYogi.__init__:1 of
+msgid "Federated learning strategy using Yogi on server-side."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:199
+msgid "server.strategy.FedTrimmedAvg"
+msgstr ""
+
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:1 of
+msgid "Federated Averaging with Trimmed Mean [Dong Yin, et al., 2021]."
+msgstr ""
+
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:3 of
+msgid "Paper: https://arxiv.org/abs/1803.01498"
+msgstr ""
+
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:23 of
+msgid "Fraction to cut off of both tails of the distribution. Defaults to 0.2."
+msgstr ""
+
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.aggregate_fit:1 of
+msgid "Aggregate fit results using trimmed average."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:210
+msgid "server.strategy.Krum"
+msgstr ""
+
+#: flwr.server.strategy.krum.Krum:1 of
+msgid "Configurable Krum strategy implementation."
+msgstr ""
+
+#: flwr.server.strategy.krum.Krum.__init__:1 of
+msgid "Krum strategy."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:15
+#: flwr.server.strategy.krum.Krum.__init__:13 of
+msgid "Number of malicious clients in the system. Defaults to 0."
+msgstr ""
+
+#: flwr.server.strategy.krum.Krum.__init__:15 of
+msgid ""
+"Number of clients to keep before averaging (MultiKrum). Defaults to 0, in"
+" that case classical Krum is applied."
+msgstr ""
+
+#: flwr.server.strategy.krum.Krum.aggregate_fit:1 of
+msgid "Aggregate fit results using Krum."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:220
+msgid "server.strategy.Bulyan"
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan:1 of
+msgid "Bulyan strategy implementation."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:1 of
+msgid "Bulyan strategy."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:3 of
+msgid "Implementation based on https://arxiv.org/abs/1802.07927."
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:27 of
+msgid ""
+"Byzantine resilient aggregation rule that is used as the first step of "
+"the Bulyan (e.g., Krum)"
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.__init__:29 of
+msgid "arguments to the first_aggregation rule"
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.aggregate_fit:1 of
+msgid "Aggregate fit results using Bulyan."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:231
+msgid "server.strategy.FedXgbNnAvg"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 of
+msgid "Configurable FedXgbNnAvg strategy implementation."
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.__init__:1 of
+msgid "Federated XGBoost [Ma et al., 2023] strategy."
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.__init__:3 of
+msgid "Implementation based on https://arxiv.org/abs/2304.07537."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:242
+msgid "server.strategy.DPFedAvgAdaptive"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of
+msgid "Wrapper for configuring a Strategy for DP with Adaptive Clipping."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.aggregate_fit:1 of
+msgid "Aggregate training results as in DPFedAvgFixed and update clip norms."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:253
+msgid "server.strategy.DPFedAvgFixed"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 of
+msgid "Wrapper for configuring a Strategy for DP with Fixed Clipping."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1 of
+msgid "Aggregate evaluation losses using the given strategy."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_fit:1 of
+msgid "Aggregate training results using unweighted aggregation."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:1 of
+msgid "Configure the next round of evaluation using the specified strategy."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:1 of
+msgid ""
+"Configure the next round of training incorporating Differential Privacy "
+"(DP)."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:3 of
+msgid ""
+"Configuration of the next training round includes information related to "
+"DP, such as clip norm and noise stddev."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.evaluate:1 of
+msgid "Evaluate model parameters using an evaluation function from the strategy."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.initialize_parameters:1 of
+msgid "Initialize global model parameters using given strategy."
+msgstr ""
+
+#: ../../source/ref-api-flwr.rst:261
+msgid "common"
+msgstr ""
+
+#: flwr.common:1 of
+msgid "Common components shared between server and client."
+msgstr ""
+
+#: flwr.common.typing.ClientMessage:1 of
+msgid "ClientMessage is a container used to hold one result message."
+msgstr ""
+
+#: flwr.common.typing.Code:1 of
+msgid "Client status codes."
+msgstr ""
+
+#: flwr.common.typing.DisconnectRes:1 of
+msgid "DisconnectRes message from client to server."
+msgstr ""
+
+#: flwr.common.typing.EvaluateIns:1 of
+msgid "Evaluate instructions for a client."
+msgstr ""
+
+#: flwr.common.typing.EvaluateRes:1 of
+msgid "Evaluate response from a client."
+msgstr ""
+
+#: flwr.common.telemetry.EventType:1 of
+msgid "Types of telemetry events."
+msgstr ""
+
+#: flwr.common.typing.FitIns:1 of
+msgid "Fit instructions for a client."
+msgstr ""
+
+#: flwr.common.typing.FitRes:1 of
+msgid "Fit response from a client."
+msgstr ""
+
+#: flwr.common.typing.GetParametersIns:1 of
+msgid "Parameters request for a client."
+msgstr ""
+
+#: flwr.common.typing.GetParametersRes:1 of
+msgid "Response when asked to return parameters."
+msgstr ""
+
+#: flwr.common.typing.GetPropertiesIns:1 of
+msgid "Properties request for a client."
+msgstr ""
+
+#: flwr.common.typing.GetPropertiesRes:1 of
+msgid "Properties response from a client."
+msgstr ""
+
+#: flwr.common.typing.Parameters:1 of
+msgid "Model parameters."
+msgstr ""
+
+#: flwr.common.typing.ReconnectIns:1 of
+msgid "ReconnectIns message from server to client."
+msgstr ""
+
+#: flwr.common.typing.ServerMessage:1 of
+msgid "ServerMessage is a container used to hold one instruction message."
+msgstr ""
+
+#: flwr.common.typing.Status:1 of
+msgid "Client status."
+msgstr ""
+
+#: flwr.common.parameter.bytes_to_ndarray:1 of
+msgid "Deserialize NumPy ndarray from bytes."
+msgstr ""
+
+#: flwr.common.logger.configure:1 of
+msgid "Configure logging to file and/or remote log server."
+msgstr ""
+
+#: logging.Logger.log:1 of
+msgid "Log 'msg % args' with the integer severity 'level'."
+msgstr ""
+
+#: logging.Logger.log:3 of
+msgid ""
+"To pass exception information, use the keyword argument exc_info with a "
+"true value, e.g."
+msgstr ""
+
+#: logging.Logger.log:6 of
+#, python-format
+msgid "logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)"
+msgstr ""
+
+#: flwr.common.parameter.ndarray_to_bytes:1 of
+msgid "Serialize NumPy ndarray to bytes."
+msgstr ""
+
+#: flwr.common.parameter.ndarrays_to_parameters:1 of
+msgid "Convert NumPy ndarrays to parameters object."
+msgstr ""
+
+#: flwr.common.date.now:1 of
+msgid "Construct a datetime from time.time() with time zone set to UTC."
+msgstr ""
+
+#: flwr.common.parameter.parameters_to_ndarrays:1 of
+msgid "Convert parameters object to NumPy ndarrays."
+msgstr ""
+
+#: ../../source/ref-changelog.md:1
+msgid "Changelog"
+msgstr ""
+
+#: ../../source/ref-changelog.md:3
+msgid "Unreleased"
+msgstr ""
+
+#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:83
+#: ../../source/ref-changelog.md:167 ../../source/ref-changelog.md:231
+#: ../../source/ref-changelog.md:289 ../../source/ref-changelog.md:358
+#: ../../source/ref-changelog.md:487 ../../source/ref-changelog.md:529
+#: ../../source/ref-changelog.md:596 ../../source/ref-changelog.md:662
+#: ../../source/ref-changelog.md:707 ../../source/ref-changelog.md:746
+#: ../../source/ref-changelog.md:779 ../../source/ref-changelog.md:829
+msgid "What's new?"
+msgstr ""
+
+#: ../../source/ref-changelog.md:7
+msgid ""
+"**Add experimental support for Python 3.12** "
+"([#2565](https://github.com/adap/flower/pull/2565))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:9
+msgid ""
+"**Support custom** `ClientManager` **in** `start_driver()` "
+"([#2292](https://github.com/adap/flower/pull/2292))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:11
+msgid ""
+"**Update REST API to support create and delete nodes** "
+"([#2283](https://github.com/adap/flower/pull/2283))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:13
+msgid ""
+"**Update the C++ SDK** "
+"([#2537](https://github/com/adap/flower/pull/2537), "
+"[#2528](https://github/com/adap/flower/pull/2528), "
+"[#2523](https://github.com/adap/flower/pull/2523), "
+"[#2522](https://github.com/adap/flower/pull/2522))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:15
+msgid "Add gRPC request-response capability to the C++ SDK."
+msgstr ""
+
+#: ../../source/ref-changelog.md:17
+msgid ""
+"**Fix the incorrect return types of Strategy** "
+"([#2432](https://github.com/adap/flower/pull/2432/files))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:19
+msgid ""
+"The types of the return values in the docstrings in two methods "
+"(`aggregate_fit` and `aggregate_evaluate`) now match the hint types in "
+"the code."
+msgstr ""
+
+#: ../../source/ref-changelog.md:21
+msgid ""
+"**Unify client API** ([#2303](https://github.com/adap/flower/pull/2303), "
+"[#2390](https://github.com/adap/flower/pull/2390), "
+"[#2493](https://github.com/adap/flower/pull/2493))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:23
+msgid ""
+"Using the `client_fn`, Flower clients can interchangeably run as "
+"standalone processes (i.e. via `start_client`) or in simulation (i.e. via"
+" `start_simulation`) without requiring changes to how the client class is"
+" defined and instantiated. Calling `start_numpy_client` is now "
+"deprecated."
+msgstr ""
+
+#: ../../source/ref-changelog.md:25
+msgid "**Update Flower Baselines**"
+msgstr ""
+
+#: ../../source/ref-changelog.md:27
+msgid ""
+"FedProx ([#2210](https://github.com/adap/flower/pull/2210), "
+"[#2286](https://github.com/adap/flower/pull/2286), "
+"[#2509](https://github.com/adap/flower/pull/2509))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:29
+msgid ""
+"Baselines Docs ([#2290](https://github.com/adap/flower/pull/2290), "
+"[#2400](https://github.com/adap/flower/pull/2400))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:31
+msgid ""
+"FedMLB ([#2340](https://github.com/adap/flower/pull/2340), "
+"[#2507](https://github.com/adap/flower/pull/2507))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:33
+msgid ""
+"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), "
+"[#2508](https://github.com/adap/flower/pull/2508))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:35
+msgid "FedMeta [#2438](https://github.com/adap/flower/pull/2438)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:37
+msgid "FjORD [#2431](https://github.com/adap/flower/pull/2431)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:39
+msgid "MOON [#2421](https://github.com/adap/flower/pull/2421)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:41
+msgid "DepthFL [#2295](https://github.com/adap/flower/pull/2295)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:43
+msgid "FedPer [#2266](https://github.com/adap/flower/pull/2266)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:45
+msgid "FedWav2vec [#2551](https://github.com/adap/flower/pull/2551)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:47
+msgid "niid-Bench [#2428](https://github.com/adap/flower/pull/2428)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:49
+msgid ""
+"FedBN ([#2608](https://github.com/adap/flower/pull/2608), "
+"[#2615](https://github.com/adap/flower/pull/2615))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:51
+msgid ""
+"**Update Flower Examples** "
+"([#2384](https://github.com/adap/flower/pull/2384),[#2425](https://github.com/adap/flower/pull/2425),"
+" [#2526](https://github.com/adap/flower/pull/2526))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:53
+msgid ""
+"**General updates to baselines** "
+"([#2301](https://github.com/adap/flower/pull/2301), "
+"[#2305](https://github.com/adap/flower/pull/2305), "
+"[#2307](https://github.com/adap/flower/pull/2307), "
+"[#2327](https://github.com/adap/flower/pull/2327), "
+"[#2435](https://github.com/adap/flower/pull/2435))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:55
+msgid ""
+"**General updates to the simulation engine** "
+"([#2331](https://github.com/adap/flower/pull/2331), "
+"[#2447](https://github.com/adap/flower/pull/2447), "
+"[#2448](https://github.com/adap/flower/pull/2448))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:57
+msgid ""
+"**General improvements** "
+"([#2309](https://github.com/adap/flower/pull/2309), "
+"[#2310](https://github.com/adap/flower/pull/2310), "
+"[2313](https://github.com/adap/flower/pull/2313), "
+"[#2316](https://github.com/adap/flower/pull/2316), "
+"[2317](https://github.com/adap/flower/pull/2317),[#2349](https://github.com/adap/flower/pull/2349),"
+" [#2360](https://github.com/adap/flower/pull/2360), "
+"[#2402](https://github.com/adap/flower/pull/2402), "
+"[#2446](https://github.com/adap/flower/pull/2446) "
+"[#2561](https://github.com/adap/flower/pull/2561))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:59 ../../source/ref-changelog.md:153
+#: ../../source/ref-changelog.md:217 ../../source/ref-changelog.md:271
+#: ../../source/ref-changelog.md:338
+msgid "Flower received many improvements under the hood, too many to list here."
+msgstr ""
+
+#: ../../source/ref-changelog.md:61
+msgid ""
+"**Add new** `Bulyan` **strategy** "
+"([#1817](https://github.com/adap/flower/pull/1817), "
+"[#1891](https://github.com/adap/flower/pull/1891))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:63
+msgid ""
+"The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., "
+"2018](https://arxiv.org/abs/1802.07927)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:65 ../../source/ref-changelog.md:155
+#: ../../source/ref-changelog.md:219 ../../source/ref-changelog.md:277
+#: ../../source/ref-changelog.md:346 ../../source/ref-changelog.md:408
+#: ../../source/ref-changelog.md:427 ../../source/ref-changelog.md:583
+#: ../../source/ref-changelog.md:654 ../../source/ref-changelog.md:691
+#: ../../source/ref-changelog.md:734
+msgid "Incompatible changes"
+msgstr ""
+
+#: ../../source/ref-changelog.md:67
+msgid ""
+"**Remove support for Python 3.7** "
+"([#2280](https://github.com/adap/flower/pull/2280), "
+"[#2299](https://github.com/adap/flower/pull/2299), "
+"[#2304](https://github.com/adap/flower/pull/2304), "
+"[#2306](https://github.com/adap/flower/pull/2306), "
+"[#2355](https://github.com/adap/flower/pull/2355), "
+"[#2356](https://github.com/adap/flower/pull/2356))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:69
+msgid ""
+"Python 3.7 support was deprecated in Flower 1.5, and this release removes"
+" support. Flower now requires Python 3.8."
+msgstr ""
+
+#: ../../source/ref-changelog.md:71
+msgid ""
+"**Remove experimental argument** `rest` **from** `start_client` "
+"([#2324](https://github.com/adap/flower/pull/2324))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:73
+msgid ""
+"The (still experimental) argument `rest` was removed from `start_client` "
+"and `start_numpy_client`. Use `transport=\"rest\"` to opt into the "
+"experimental REST API instead."
+msgstr ""
+
+#: ../../source/ref-changelog.md:75
+msgid "v1.5.0 (2023-08-31)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:77 ../../source/ref-changelog.md:161
+#: ../../source/ref-changelog.md:225 ../../source/ref-changelog.md:283
+#: ../../source/ref-changelog.md:352 ../../source/ref-changelog.md:421
+msgid "Thanks to our contributors"
+msgstr ""
+
+#: ../../source/ref-changelog.md:79 ../../source/ref-changelog.md:163
+#: ../../source/ref-changelog.md:227 ../../source/ref-changelog.md:285
+msgid ""
+"We would like to give our special thanks to all the contributors who made"
+" the new version of Flower possible (in `git shortlog` order):"
+msgstr ""
+
+#: ../../source/ref-changelog.md:81
+msgid ""
+"`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, "
+"`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo "
+"Bertoli`, `Heng Pan`, `Javier`, `Mahdi`, `Steven Hé (Sīchàng)`, `Taner "
+"Topal`, `achiverram28`, `danielnugraha`, `eunchung`, `ruthgal` <!---"
+"TOKEN_v1.5.0-->"
+msgstr ""
+
+#: ../../source/ref-changelog.md:85
+msgid ""
+"**Introduce new simulation engine** "
+"([#1969](https://github.com/adap/flower/pull/1969), "
+"[#2221](https://github.com/adap/flower/pull/2221), "
+"[#2248](https://github.com/adap/flower/pull/2248))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:87
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:89
+msgid ""
+"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)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:91
+msgid ""
+"**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))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:93
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:95
+msgid ""
+"**Introduce Flower Swift SDK** "
+"([#1858](https://github.com/adap/flower/pull/1858), "
+"[#1897](https://github.com/adap/flower/pull/1897))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:97
+msgid ""
+"This is the first preview release of the Flower Swift SDK. Flower support"
+" on iOS is improving, and alongside the Swift SDK and code example, there"
+" is now also an iOS quickstart tutorial."
+msgstr ""
+
+#: ../../source/ref-changelog.md:99
+msgid ""
+"**Introduce Flower Android SDK** "
+"([#2131](https://github.com/adap/flower/pull/2131))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:101
+msgid ""
+"This is the first preview release of the Flower Kotlin SDK. Flower "
+"support on Android is improving, and alongside the Kotlin SDK and code "
+"example, there is now also an Android quickstart tutorial."
+msgstr ""
+
+#: ../../source/ref-changelog.md:103
+msgid ""
+"**Introduce new end-to-end testing infrastructure** "
+"([#1842](https://github.com/adap/flower/pull/1842), "
+"[#2071](https://github.com/adap/flower/pull/2071), "
+"[#2072](https://github.com/adap/flower/pull/2072), "
+"[#2068](https://github.com/adap/flower/pull/2068), "
+"[#2067](https://github.com/adap/flower/pull/2067), "
+"[#2069](https://github.com/adap/flower/pull/2069), "
+"[#2073](https://github.com/adap/flower/pull/2073), "
+"[#2070](https://github.com/adap/flower/pull/2070), "
+"[#2074](https://github.com/adap/flower/pull/2074), "
+"[#2082](https://github.com/adap/flower/pull/2082), "
+"[#2084](https://github.com/adap/flower/pull/2084), "
+"[#2093](https://github.com/adap/flower/pull/2093), "
+"[#2109](https://github.com/adap/flower/pull/2109), "
+"[#2095](https://github.com/adap/flower/pull/2095), "
+"[#2140](https://github.com/adap/flower/pull/2140), "
+"[#2137](https://github.com/adap/flower/pull/2137), "
+"[#2165](https://github.com/adap/flower/pull/2165))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:105
+msgid ""
+"A new testing infrastructure ensures that new changes stay compatible "
+"with existing framework integrations or strategies."
+msgstr ""
+
+#: ../../source/ref-changelog.md:107
+msgid "**Deprecate Python 3.7**"
+msgstr ""
+
+#: ../../source/ref-changelog.md:109
+msgid ""
+"Since Python 3.7 reached its end of life (EOL) on 2023-06-27, support for"
+" Python 3.7 is now deprecated and will be removed in an upcoming release."
+msgstr ""
+
+#: ../../source/ref-changelog.md:111
+msgid ""
+"**Add new** `FedTrimmedAvg` **strategy** "
+"([#1769](https://github.com/adap/flower/pull/1769), "
+"[#1853](https://github.com/adap/flower/pull/1853))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:113
+msgid ""
+"The new `FedTrimmedAvg` strategy implements Trimmed Mean by [Dong Yin, "
+"2018](https://arxiv.org/abs/1803.01498)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:115
+msgid ""
+"**Introduce start_driver** "
+"([#1697](https://github.com/adap/flower/pull/1697))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:117
+msgid ""
+"In addition to `start_server` and using the raw Driver API, there is a "
+"new `start_driver` function that allows for running `start_server` "
+"scripts as a Flower driver with only a single-line code change. Check out"
+" the `mt-pytorch` code example to see a working example using "
+"`start_driver`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:119
+msgid ""
+"**Add parameter aggregation to** `mt-pytorch` **code example** "
+"([#1785](https://github.com/adap/flower/pull/1785))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:121
+msgid ""
+"The `mt-pytorch` example shows how to aggregate parameters when writing a"
+" driver script. The included `driver.py` and `server.py` have been "
+"aligned to demonstrate both the low-level way and the high-level way of "
+"building server-side logic."
+msgstr ""
+
+#: ../../source/ref-changelog.md:123
+msgid ""
+"**Migrate experimental REST API to Starlette** "
+"([2171](https://github.com/adap/flower/pull/2171))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:125
+msgid ""
+"The (experimental) REST API used to be implemented in "
+"[FastAPI](https://fastapi.tiangolo.com/), but it has now been migrated to"
+" use [Starlette](https://www.starlette.io/) directly."
+msgstr ""
+
+#: ../../source/ref-changelog.md:127
+msgid ""
+"Please note: The REST request-response API is still experimental and will"
+" likely change significantly over time."
+msgstr ""
+
+#: ../../source/ref-changelog.md:129
+msgid ""
+"**Introduce experimental gRPC request-response API** "
+"([#1867](https://github.com/adap/flower/pull/1867), "
+"[#1901](https://github.com/adap/flower/pull/1901))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:131
+msgid ""
+"In addition to the existing gRPC API (based on bidirectional streaming) "
+"and the experimental REST API, there is now a new gRPC API that uses a "
+"request-response model to communicate with client nodes."
+msgstr ""
+
+#: ../../source/ref-changelog.md:133
+msgid ""
+"Please note: The gRPC request-response API is still experimental and will"
+" likely change significantly over time."
+msgstr ""
+
+#: ../../source/ref-changelog.md:135
+msgid ""
+"**Replace the experimental** `start_client(rest=True)` **with the new** "
+"`start_client(transport=\"rest\")` "
+"([#1880](https://github.com/adap/flower/pull/1880))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:137
+msgid ""
+"The (experimental) `start_client` argument `rest` was deprecated in "
+"favour of a new argument `transport`. `start_client(transport=\"rest\")` "
+"will yield the same behaviour as `start_client(rest=True)` did before. "
+"All code should migrate to the new argument `transport`. The deprecated "
+"argument `rest` will be removed in a future release."
+msgstr ""
+
+#: ../../source/ref-changelog.md:139
+msgid ""
+"**Add a new gRPC option** "
+"([#2197](https://github.com/adap/flower/pull/2197))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:141
+msgid ""
+"We now start a gRPC server with the `grpc.keepalive_permit_without_calls`"
+" option set to 0 by default. This prevents the clients from sending "
+"keepalive pings when there is no outstanding stream."
+msgstr ""
+
+#: ../../source/ref-changelog.md:143
+msgid ""
+"**Improve example notebooks** "
+"([#2005](https://github.com/adap/flower/pull/2005))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:145
+msgid "There's a new 30min Federated Learning PyTorch tutorial!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:147
+msgid ""
+"**Example updates** ([#1772](https://github.com/adap/flower/pull/1772), "
+"[#1873](https://github.com/adap/flower/pull/1873), "
+"[#1981](https://github.com/adap/flower/pull/1981), "
+"[#1988](https://github.com/adap/flower/pull/1988), "
+"[#1984](https://github.com/adap/flower/pull/1984), "
+"[#1982](https://github.com/adap/flower/pull/1982), "
+"[#2112](https://github.com/adap/flower/pull/2112), "
+"[#2144](https://github.com/adap/flower/pull/2144), "
+"[#2174](https://github.com/adap/flower/pull/2174), "
+"[#2225](https://github.com/adap/flower/pull/2225), "
+"[#2183](https://github.com/adap/flower/pull/2183))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:149
+msgid ""
+"Many examples have received significant updates, including simplified "
+"advanced-tensorflow and advanced-pytorch examples, improved macOS "
+"compatibility of TensorFlow examples, and code examples for simulation. A"
+" major upgrade is that all code examples now have a `requirements.txt` "
+"(in addition to `pyproject.toml`)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:151
+msgid ""
+"**General improvements** "
+"([#1872](https://github.com/adap/flower/pull/1872), "
+"[#1866](https://github.com/adap/flower/pull/1866), "
+"[#1884](https://github.com/adap/flower/pull/1884), "
+"[#1837](https://github.com/adap/flower/pull/1837), "
+"[#1477](https://github.com/adap/flower/pull/1477), "
+"[#2171](https://github.com/adap/flower/pull/2171))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:157 ../../source/ref-changelog.md:221
+#: ../../source/ref-changelog.md:279 ../../source/ref-changelog.md:348
+#: ../../source/ref-changelog.md:410
+msgid "None"
+msgstr ""
+
+#: ../../source/ref-changelog.md:159
+msgid "v1.4.0 (2023-04-21)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:165
+msgid ""
+"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, "
+"`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, "
+"`Iacob-Alexandru-Andrei`, `JDRanpariya`, `Jean Charle Yaacoub`, `Kunal "
+"Sarkhel`, `L. Jiang`, `Lennart Behme`, `Max Kapsecker`, `Michał`, `Nic "
+"Lane`, `Nikolaos Episkopos`, `Ragy`, `Saurav Maheshkar`, `Semo Yang`, "
+"`Steve Laskaridis`, `Steven Hé (Sīchàng)`, `Taner Topal`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:169
+msgid ""
+"**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))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:171
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:173
+msgid ""
+"**Introduce iOS SDK (preview)** "
+"([#1621](https://github.com/adap/flower/pull/1621), "
+"[#1764](https://github.com/adap/flower/pull/1764))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:175
+msgid ""
+"This is a major update for anyone wanting to implement Federated Learning"
+" on iOS mobile devices. We now have a swift iOS SDK present under "
+"[src/swift/flwr](https://github.com/adap/flower/tree/main/src/swift/flwr)"
+" that will facilitate greatly the app creating process. To showcase its "
+"use, the [iOS "
+"example](https://github.com/adap/flower/tree/main/examples/ios) has also "
+"been updated!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:177
+msgid ""
+"**Introduce new \"What is Federated Learning?\" tutorial** "
+"([#1657](https://github.com/adap/flower/pull/1657), "
+"[#1721](https://github.com/adap/flower/pull/1721))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:179
+msgid ""
+"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!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:181
+msgid ""
+"**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))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:183
+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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:185
+msgid ""
+"**Introduce new Flower Baseline: FedAvg FEMNIST** "
+"([#1655](https://github.com/adap/flower/pull/1655))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:187
+msgid ""
+"This new baseline replicates an experiment evaluating the performance of "
+"the FedAvg algorithm on the FEMNIST dataset from the paper [LEAF: A "
+"Benchmark for Federated Settings (Caldas et al., "
+"2018)](https://arxiv.org/abs/1812.01097)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:189
+msgid ""
+"**Introduce (experimental) REST API** "
+"([#1594](https://github.com/adap/flower/pull/1594), "
+"[#1690](https://github.com/adap/flower/pull/1690), "
+"[#1695](https://github.com/adap/flower/pull/1695), "
+"[#1712](https://github.com/adap/flower/pull/1712), "
+"[#1802](https://github.com/adap/flower/pull/1802), "
+"[#1770](https://github.com/adap/flower/pull/1770), "
+"[#1733](https://github.com/adap/flower/pull/1733))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:191
+msgid ""
+"A new REST API has been introduced as an alternative to the gRPC-based "
+"communication stack. In this initial version, the REST API only supports "
+"anonymous clients."
+msgstr ""
+
+#: ../../source/ref-changelog.md:193
+msgid ""
+"Please note: The REST API is still experimental and will likely change "
+"significantly over time."
+msgstr ""
+
+#: ../../source/ref-changelog.md:195
+msgid ""
+"**Improve the (experimental) Driver API** "
+"([#1663](https://github.com/adap/flower/pull/1663), "
+"[#1666](https://github.com/adap/flower/pull/1666), "
+"[#1667](https://github.com/adap/flower/pull/1667), "
+"[#1664](https://github.com/adap/flower/pull/1664), "
+"[#1675](https://github.com/adap/flower/pull/1675), "
+"[#1676](https://github.com/adap/flower/pull/1676), "
+"[#1693](https://github.com/adap/flower/pull/1693), "
+"[#1662](https://github.com/adap/flower/pull/1662), "
+"[#1794](https://github.com/adap/flower/pull/1794))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:197
+msgid ""
+"The Driver API is still an experimental feature, but this release "
+"introduces some major upgrades. One of the main improvements is the "
+"introduction of an SQLite database to store server state on disk (instead"
+" of in-memory). Another improvement is that tasks (instructions or "
+"results) that have been delivered will now be deleted. This greatly "
+"improves the memory efficiency of a long-running Flower server."
+msgstr ""
+
+#: ../../source/ref-changelog.md:199
+msgid ""
+"**Fix spilling issues related to Ray during simulations** "
+"([#1698](https://github.com/adap/flower/pull/1698))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:201
+msgid ""
+"While running long simulations, `ray` was sometimes spilling huge amounts"
+" of data that would make the training unable to continue. This is now "
+"fixed! 🎉"
+msgstr ""
+
+#: ../../source/ref-changelog.md:203
+msgid ""
+"**Add new example using** `TabNet` **and Flower** "
+"([#1725](https://github.com/adap/flower/pull/1725))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:205
+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)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:207
+msgid ""
+"**Add new how-to guide for monitoring simulations** "
+"([#1649](https://github.com/adap/flower/pull/1649))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:209
+msgid ""
+"We now have a documentation guide to help users monitor their performance"
+" during simulations."
+msgstr ""
+
+#: ../../source/ref-changelog.md:211
+msgid ""
+"**Add training metrics to** `History` **object during simulations** "
+"([#1696](https://github.com/adap/flower/pull/1696))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:213
+msgid ""
+"The `fit_metrics_aggregation_fn` can be used to aggregate training "
+"metrics, but previous releases did not save the results in the `History` "
+"object. This is now the case!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:215
+msgid ""
+"**General improvements** "
+"([#1659](https://github.com/adap/flower/pull/1659), "
+"[#1646](https://github.com/adap/flower/pull/1646), "
+"[#1647](https://github.com/adap/flower/pull/1647), "
+"[#1471](https://github.com/adap/flower/pull/1471), "
+"[#1648](https://github.com/adap/flower/pull/1648), "
+"[#1651](https://github.com/adap/flower/pull/1651), "
+"[#1652](https://github.com/adap/flower/pull/1652), "
+"[#1653](https://github.com/adap/flower/pull/1653), "
+"[#1659](https://github.com/adap/flower/pull/1659), "
+"[#1665](https://github.com/adap/flower/pull/1665), "
+"[#1670](https://github.com/adap/flower/pull/1670), "
+"[#1672](https://github.com/adap/flower/pull/1672), "
+"[#1677](https://github.com/adap/flower/pull/1677), "
+"[#1684](https://github.com/adap/flower/pull/1684), "
+"[#1683](https://github.com/adap/flower/pull/1683), "
+"[#1686](https://github.com/adap/flower/pull/1686), "
+"[#1682](https://github.com/adap/flower/pull/1682), "
+"[#1685](https://github.com/adap/flower/pull/1685), "
+"[#1692](https://github.com/adap/flower/pull/1692), "
+"[#1705](https://github.com/adap/flower/pull/1705), "
+"[#1708](https://github.com/adap/flower/pull/1708), "
+"[#1711](https://github.com/adap/flower/pull/1711), "
+"[#1713](https://github.com/adap/flower/pull/1713), "
+"[#1714](https://github.com/adap/flower/pull/1714), "
+"[#1718](https://github.com/adap/flower/pull/1718), "
+"[#1716](https://github.com/adap/flower/pull/1716), "
+"[#1723](https://github.com/adap/flower/pull/1723), "
+"[#1735](https://github.com/adap/flower/pull/1735), "
+"[#1678](https://github.com/adap/flower/pull/1678), "
+"[#1750](https://github.com/adap/flower/pull/1750), "
+"[#1753](https://github.com/adap/flower/pull/1753), "
+"[#1736](https://github.com/adap/flower/pull/1736), "
+"[#1766](https://github.com/adap/flower/pull/1766), "
+"[#1760](https://github.com/adap/flower/pull/1760), "
+"[#1775](https://github.com/adap/flower/pull/1775), "
+"[#1776](https://github.com/adap/flower/pull/1776), "
+"[#1777](https://github.com/adap/flower/pull/1777), "
+"[#1779](https://github.com/adap/flower/pull/1779), "
+"[#1784](https://github.com/adap/flower/pull/1784), "
+"[#1773](https://github.com/adap/flower/pull/1773), "
+"[#1755](https://github.com/adap/flower/pull/1755), "
+"[#1789](https://github.com/adap/flower/pull/1789), "
+"[#1788](https://github.com/adap/flower/pull/1788), "
+"[#1798](https://github.com/adap/flower/pull/1798), "
+"[#1799](https://github.com/adap/flower/pull/1799), "
+"[#1739](https://github.com/adap/flower/pull/1739), "
+"[#1800](https://github.com/adap/flower/pull/1800), "
+"[#1804](https://github.com/adap/flower/pull/1804), "
+"[#1805](https://github.com/adap/flower/pull/1805))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:223
+msgid "v1.3.0 (2023-02-06)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:229
+msgid ""
+"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, "
+"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:233
+msgid ""
+"**Add support for** `workload_id` **and** `group_id` **in Driver API** "
+"([#1595](https://github.com/adap/flower/pull/1595))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:235
+msgid ""
+"The (experimental) Driver API now supports a `workload_id` that can be "
+"used to identify which workload a task belongs to. It also supports a new"
+" `group_id` that can be used, for example, to indicate the current "
+"training round. Both the `workload_id` and `group_id` enable client nodes"
+" to decide whether they want to handle a task or not."
+msgstr ""
+
+#: ../../source/ref-changelog.md:237
+msgid ""
+"**Make Driver API and Fleet API address configurable** "
+"([#1637](https://github.com/adap/flower/pull/1637))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:239
+msgid ""
+"The (experimental) long-running Flower server (Driver API and Fleet API) "
+"can now configure the server address of both Driver API (via `--driver-"
+"api-address`) and Fleet API (via `--fleet-api-address`) when starting:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:241
+msgid ""
+"`flower-server --driver-api-address \"0.0.0.0:8081\" --fleet-api-address "
+"\"0.0.0.0:8086\"`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:243
+msgid "Both IPv4 and IPv6 addresses are supported."
+msgstr ""
+
+#: ../../source/ref-changelog.md:245
+msgid ""
+"**Add new example of Federated Learning using fastai and Flower** "
+"([#1598](https://github.com/adap/flower/pull/1598))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:247
+msgid ""
+"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)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:249
+msgid ""
+"**Make Android example compatible with** `flwr >= 1.0.0` **and the latest"
+" versions of Android** "
+"([#1603](https://github.com/adap/flower/pull/1603))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:251
+msgid ""
+"The Android code example has received a substantial update: the project "
+"is compatible with Flower 1.0 (and later), the UI received a full "
+"refresh, and the project is updated to be compatible with newer Android "
+"tooling."
+msgstr ""
+
+#: ../../source/ref-changelog.md:253
+msgid ""
+"**Add new `FedProx` strategy** "
+"([#1619](https://github.com/adap/flower/pull/1619))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:255
+msgid ""
+"This "
+"[strategy](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedprox.py)"
+" is almost identical to "
+"[`FedAvg`](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedavg.py),"
+" but helps users replicate what is described in this "
+"[paper](https://arxiv.org/abs/1812.06127). It essentially adds a "
+"parameter called `proximal_mu` to regularize the local models with "
+"respect to the global models."
+msgstr ""
+
+#: ../../source/ref-changelog.md:257
+msgid ""
+"**Add new metrics to telemetry events** "
+"([#1640](https://github.com/adap/flower/pull/1640))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:259
+msgid ""
+"An updated event structure allows, for example, the clustering of events "
+"within the same workload."
+msgstr ""
+
+#: ../../source/ref-changelog.md:261
+msgid ""
+"**Add new custom strategy tutorial section** "
+"[#1623](https://github.com/adap/flower/pull/1623)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:263
+msgid ""
+"The Flower tutorial now has a new section that covers implementing a "
+"custom strategy from scratch: [Open in "
+"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source"
+"/tutorial-build-a-strategy-from-scratch-pytorch.ipynb)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:265
+msgid ""
+"**Add new custom serialization tutorial section** "
+"([#1622](https://github.com/adap/flower/pull/1622))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:267
+msgid ""
+"The Flower tutorial now has a new section that covers custom "
+"serialization: [Open in "
+"Colab](https://colab.research.google.com/github/adap/flower/blob/main/doc/source"
+"/tutorial-customize-the-client-pytorch.ipynb)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:269
+msgid ""
+"**General improvements** "
+"([#1638](https://github.com/adap/flower/pull/1638), "
+"[#1634](https://github.com/adap/flower/pull/1634), "
+"[#1636](https://github.com/adap/flower/pull/1636), "
+"[#1635](https://github.com/adap/flower/pull/1635), "
+"[#1633](https://github.com/adap/flower/pull/1633), "
+"[#1632](https://github.com/adap/flower/pull/1632), "
+"[#1631](https://github.com/adap/flower/pull/1631), "
+"[#1630](https://github.com/adap/flower/pull/1630), "
+"[#1627](https://github.com/adap/flower/pull/1627), "
+"[#1593](https://github.com/adap/flower/pull/1593), "
+"[#1616](https://github.com/adap/flower/pull/1616), "
+"[#1615](https://github.com/adap/flower/pull/1615), "
+"[#1607](https://github.com/adap/flower/pull/1607), "
+"[#1609](https://github.com/adap/flower/pull/1609), "
+"[#1608](https://github.com/adap/flower/pull/1608), "
+"[#1603](https://github.com/adap/flower/pull/1603), "
+"[#1590](https://github.com/adap/flower/pull/1590), "
+"[#1580](https://github.com/adap/flower/pull/1580), "
+"[#1599](https://github.com/adap/flower/pull/1599), "
+"[#1600](https://github.com/adap/flower/pull/1600), "
+"[#1601](https://github.com/adap/flower/pull/1601), "
+"[#1597](https://github.com/adap/flower/pull/1597), "
+"[#1595](https://github.com/adap/flower/pull/1595), "
+"[#1591](https://github.com/adap/flower/pull/1591), "
+"[#1588](https://github.com/adap/flower/pull/1588), "
+"[#1589](https://github.com/adap/flower/pull/1589), "
+"[#1587](https://github.com/adap/flower/pull/1587), "
+"[#1573](https://github.com/adap/flower/pull/1573), "
+"[#1581](https://github.com/adap/flower/pull/1581), "
+"[#1578](https://github.com/adap/flower/pull/1578), "
+"[#1574](https://github.com/adap/flower/pull/1574), "
+"[#1572](https://github.com/adap/flower/pull/1572), "
+"[#1586](https://github.com/adap/flower/pull/1586))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:273
+msgid ""
+"**Updated documentation** "
+"([#1629](https://github.com/adap/flower/pull/1629), "
+"[#1628](https://github.com/adap/flower/pull/1628), "
+"[#1620](https://github.com/adap/flower/pull/1620), "
+"[#1618](https://github.com/adap/flower/pull/1618), "
+"[#1617](https://github.com/adap/flower/pull/1617), "
+"[#1613](https://github.com/adap/flower/pull/1613), "
+"[#1614](https://github.com/adap/flower/pull/1614))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:275 ../../source/ref-changelog.md:342
+msgid ""
+"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!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:281
+msgid "v1.2.0 (2023-01-13)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:287
+msgid ""
+"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L."
+" Jiang`, `Ragy`, `Taner Topal`, `dannymcy`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:291
+msgid ""
+"**Introduce new Flower Baseline: FedAvg MNIST** "
+"([#1497](https://github.com/adap/flower/pull/1497), "
+"[#1552](https://github.com/adap/flower/pull/1552))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:293
+msgid ""
+"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/)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:295
+msgid ""
+"**Improve GPU support in simulations** "
+"([#1555](https://github.com/adap/flower/pull/1555))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:297
+msgid ""
+"The Ray-based Virtual Client Engine (`start_simulation`) has been updated"
+" to improve GPU support. The update includes some of the hard-earned "
+"lessons from scaling simulations in GPU cluster environments. New "
+"defaults make running GPU-based simulations substantially more robust."
+msgstr ""
+
+#: ../../source/ref-changelog.md:299
+msgid ""
+"**Improve GPU support in Jupyter Notebook tutorials** "
+"([#1527](https://github.com/adap/flower/pull/1527), "
+"[#1558](https://github.com/adap/flower/pull/1558))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:301
+msgid ""
+"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:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:303
+msgid ""
+"[An Introduction to Federated Learning](https://flower.dev/docs/framework"
+"/tutorial-get-started-with-flower-pytorch.html)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:304
+msgid ""
+"[Strategies in Federated Learning](https://flower.dev/docs/framework"
+"/tutorial-use-a-federated-learning-strategy-pytorch.html)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:305
+msgid ""
+"[Building a Strategy](https://flower.dev/docs/framework/tutorial-build-a"
+"-strategy-from-scratch-pytorch.html)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:306
+msgid ""
+"[Client and NumPyClient](https://flower.dev/docs/framework/tutorial-"
+"customize-the-client-pytorch.html)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:308
+msgid ""
+"**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))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:310
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:312
+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)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:314
+msgid ""
+"**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))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:316
+msgid ""
+"Flower now has a new (experimental) Driver API which will enable fully "
+"programmable, async, and multi-tenant Federated Learning and Federated "
+"Analytics applications. Phew, that's a lot! Going forward, the Driver API"
+" will be the abstraction that many upcoming features will be built on - "
+"and you can start building those things now, too."
+msgstr ""
+
+#: ../../source/ref-changelog.md:318
+msgid ""
+"The Driver API also enables a new execution mode in which the server runs"
+" indefinitely. Multiple individual workloads can run concurrently and "
+"start and stop their execution independent of the server. This is "
+"especially useful for users who want to deploy Flower in production."
+msgstr ""
+
+#: ../../source/ref-changelog.md:320
+msgid ""
+"To learn more, check out the `mt-pytorch` code example. We look forward "
+"to you feedback!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:322
+msgid ""
+"Please note: *The Driver API is still experimental and will likely change"
+" significantly over time.*"
+msgstr ""
+
+#: ../../source/ref-changelog.md:324
+msgid ""
+"**Add new Federated Analytics with Pandas example** "
+"([#1469](https://github.com/adap/flower/pull/1469), "
+"[#1535](https://github.com/adap/flower/pull/1535))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:326
+msgid ""
+"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)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:328
+msgid ""
+"**Add new strategies: Krum and MultiKrum** "
+"([#1481](https://github.com/adap/flower/pull/1481))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:330
+msgid ""
+"Edoardo, a computer science student at the Sapienza University of Rome, "
+"contributed a new `Krum` strategy that enables users to easily use Krum "
+"and MultiKrum in their workloads."
+msgstr ""
+
+#: ../../source/ref-changelog.md:332
+msgid ""
+"**Update C++ example to be compatible with Flower v1.2.0** "
+"([#1495](https://github.com/adap/flower/pull/1495))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:334
+msgid ""
+"The C++ code example has received a substantial update to make it "
+"compatible with the latest version of Flower."
+msgstr ""
+
+#: ../../source/ref-changelog.md:336
+msgid ""
+"**General improvements** "
+"([#1491](https://github.com/adap/flower/pull/1491), "
+"[#1504](https://github.com/adap/flower/pull/1504), "
+"[#1506](https://github.com/adap/flower/pull/1506), "
+"[#1514](https://github.com/adap/flower/pull/1514), "
+"[#1522](https://github.com/adap/flower/pull/1522), "
+"[#1523](https://github.com/adap/flower/pull/1523), "
+"[#1526](https://github.com/adap/flower/pull/1526), "
+"[#1528](https://github.com/adap/flower/pull/1528), "
+"[#1547](https://github.com/adap/flower/pull/1547), "
+"[#1549](https://github.com/adap/flower/pull/1549), "
+"[#1560](https://github.com/adap/flower/pull/1560), "
+"[#1564](https://github.com/adap/flower/pull/1564), "
+"[#1566](https://github.com/adap/flower/pull/1566))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:340
+msgid ""
+"**Updated documentation** "
+"([#1494](https://github.com/adap/flower/pull/1494), "
+"[#1496](https://github.com/adap/flower/pull/1496), "
+"[#1500](https://github.com/adap/flower/pull/1500), "
+"[#1503](https://github.com/adap/flower/pull/1503), "
+"[#1505](https://github.com/adap/flower/pull/1505), "
+"[#1524](https://github.com/adap/flower/pull/1524), "
+"[#1518](https://github.com/adap/flower/pull/1518), "
+"[#1519](https://github.com/adap/flower/pull/1519), "
+"[#1515](https://github.com/adap/flower/pull/1515))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:344
+msgid ""
+"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!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:350
+msgid "v1.1.0 (2022-10-31)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:354
+msgid ""
+"We would like to give our **special thanks** to all the contributors who "
+"made the new version of Flower possible (in `git shortlog` order):"
+msgstr ""
+
+#: ../../source/ref-changelog.md:356
+msgid ""
+"`Akis Linardos`, `Christopher S`, `Daniel J. Beutel`, `George`, `Jan "
+"Schlicht`, `Mohammad Fares`, `Pedro Porto Buarque de Gusmão`, `Philipp "
+"Wiesner`, `Rob Luke`, `Taner Topal`, `VasundharaAgarwal`, "
+"`danielnugraha`, `edogab33`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:360
+msgid ""
+"**Introduce Differential Privacy wrappers (preview)** "
+"([#1357](https://github.com/adap/flower/pull/1357), "
+"[#1460](https://github.com/adap/flower/pull/1460))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:362
+msgid ""
+"The first (experimental) preview of pluggable Differential Privacy "
+"wrappers enables easy configuration and usage of differential privacy "
+"(DP). The pluggable DP wrappers enable framework-agnostic **and** "
+"strategy-agnostic usage of both client-side DP and server-side DP. Head "
+"over to the Flower docs, a new explainer goes into more detail."
+msgstr ""
+
+#: ../../source/ref-changelog.md:364
+msgid ""
+"**New iOS CoreML code example** "
+"([#1289](https://github.com/adap/flower/pull/1289))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:366
+msgid ""
+"Flower goes iOS! A massive new code example shows how Flower clients can "
+"be built for iOS. The code example contains both Flower iOS SDK "
+"components that can be used for many tasks, and one task example running "
+"on CoreML."
+msgstr ""
+
+#: ../../source/ref-changelog.md:368
+msgid ""
+"**New FedMedian strategy** "
+"([#1461](https://github.com/adap/flower/pull/1461))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:370
+msgid ""
+"The new `FedMedian` strategy implements Federated Median (FedMedian) by "
+"[Yin et al., 2018](https://arxiv.org/pdf/1803.01498v1.pdf)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:372
+msgid ""
+"**Log** `Client` **exceptions in Virtual Client Engine** "
+"([#1493](https://github.com/adap/flower/pull/1493))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:374
+msgid ""
+"All `Client` exceptions happening in the VCE are now logged by default "
+"and not just exposed to the configured `Strategy` (via the `failures` "
+"argument)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:376
+msgid ""
+"**Improve Virtual Client Engine internals** "
+"([#1401](https://github.com/adap/flower/pull/1401), "
+"[#1453](https://github.com/adap/flower/pull/1453))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:378
+msgid ""
+"Some internals of the Virtual Client Engine have been revamped. The VCE "
+"now uses Ray 2.0 under the hood, the value type of the `client_resources`"
+" dictionary changed to `float` to allow fractions of resources to be "
+"allocated."
+msgstr ""
+
+#: ../../source/ref-changelog.md:380
+msgid ""
+"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual "
+"Client Engine**"
+msgstr ""
+
+#: ../../source/ref-changelog.md:382
+msgid ""
+"The Virtual Client Engine now has full support for optional `Client` (and"
+" `NumPyClient`) methods."
+msgstr ""
+
+#: ../../source/ref-changelog.md:384
+msgid ""
+"**Provide type information to packages using** `flwr` "
+"([#1377](https://github.com/adap/flower/pull/1377))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:386
+msgid ""
+"The package `flwr` is now bundled with a `py.typed` file indicating that "
+"the package is typed. This enables typing support for projects or "
+"packages that use `flwr` by enabling them to improve their code using "
+"static type checkers like `mypy`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:388
+msgid ""
+"**Updated code example** "
+"([#1344](https://github.com/adap/flower/pull/1344), "
+"[#1347](https://github.com/adap/flower/pull/1347))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:390
+msgid ""
+"The code examples covering scikit-learn and PyTorch Lightning have been "
+"updated to work with the latest version of Flower."
+msgstr ""
+
+#: ../../source/ref-changelog.md:392
+msgid ""
+"**Updated documentation** "
+"([#1355](https://github.com/adap/flower/pull/1355), "
+"[#1558](https://github.com/adap/flower/pull/1558), "
+"[#1379](https://github.com/adap/flower/pull/1379), "
+"[#1380](https://github.com/adap/flower/pull/1380), "
+"[#1381](https://github.com/adap/flower/pull/1381), "
+"[#1332](https://github.com/adap/flower/pull/1332), "
+"[#1391](https://github.com/adap/flower/pull/1391), "
+"[#1403](https://github.com/adap/flower/pull/1403), "
+"[#1364](https://github.com/adap/flower/pull/1364), "
+"[#1409](https://github.com/adap/flower/pull/1409), "
+"[#1419](https://github.com/adap/flower/pull/1419), "
+"[#1444](https://github.com/adap/flower/pull/1444), "
+"[#1448](https://github.com/adap/flower/pull/1448), "
+"[#1417](https://github.com/adap/flower/pull/1417), "
+"[#1449](https://github.com/adap/flower/pull/1449), "
+"[#1465](https://github.com/adap/flower/pull/1465), "
+"[#1467](https://github.com/adap/flower/pull/1467))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:394
+msgid ""
+"There have been so many documentation updates that it doesn't even make "
+"sense to list them individually."
+msgstr ""
+
+#: ../../source/ref-changelog.md:396
+msgid ""
+"**Restructured documentation** "
+"([#1387](https://github.com/adap/flower/pull/1387))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:398
+msgid ""
+"The documentation has been restructured to make it easier to navigate. "
+"This is just the first step in a larger effort to make the Flower "
+"documentation the best documentation of any project ever. Stay tuned!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:400
+msgid ""
+"**Open in Colab button** "
+"([#1389](https://github.com/adap/flower/pull/1389))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:402
+msgid ""
+"The four parts of the Flower Federated Learning Tutorial now come with a "
+"new `Open in Colab` button. No need to install anything on your local "
+"machine, you can now use and learn about Flower in your browser, it's "
+"only a single click away."
+msgstr ""
+
+#: ../../source/ref-changelog.md:404
+msgid ""
+"**Improved tutorial** ([#1468](https://github.com/adap/flower/pull/1468),"
+" [#1470](https://github.com/adap/flower/pull/1470), "
+"[#1472](https://github.com/adap/flower/pull/1472), "
+"[#1473](https://github.com/adap/flower/pull/1473), "
+"[#1474](https://github.com/adap/flower/pull/1474), "
+"[#1475](https://github.com/adap/flower/pull/1475))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:406
+msgid ""
+"The Flower Federated Learning Tutorial has two brand-new parts covering "
+"custom strategies (still WIP) and the distinction between `Client` and "
+"`NumPyClient`. The existing parts one and two have also been improved "
+"(many small changes and fixes)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:412
+msgid "v1.0.0 (2022-07-28)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:414
+msgid "Highlights"
+msgstr ""
+
+#: ../../source/ref-changelog.md:416
+msgid "Stable **Virtual Client Engine** (accessible via `start_simulation`)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:417
+msgid "All `Client`/`NumPyClient` methods are now optional"
+msgstr ""
+
+#: ../../source/ref-changelog.md:418
+msgid "Configurable `get_parameters`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:419
+msgid ""
+"Tons of small API cleanups resulting in a more coherent developer "
+"experience"
+msgstr ""
+
+#: ../../source/ref-changelog.md:423
+msgid ""
+"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):"
+msgstr ""
+
+#: ../../source/ref-changelog.md:425
+msgid ""
+"[@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)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:429
+msgid ""
+"**All arguments must be passed as keyword arguments** "
+"([#1338](https://github.com/adap/flower/pull/1338))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:431
+msgid ""
+"Pass all arguments as keyword arguments, positional arguments are not "
+"longer supported. Code that uses positional arguments (e.g., "
+"`start_client(\"127.0.0.1:8080\", FlowerClient())`) must add the keyword "
+"for each positional argument (e.g., "
+"`start_client(server_address=\"127.0.0.1:8080\", "
+"client=FlowerClient())`)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:433
+msgid ""
+"**Introduce configuration object** `ServerConfig` **in** `start_server` "
+"**and** `start_simulation` "
+"([#1317](https://github.com/adap/flower/pull/1317))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:435
+msgid ""
+"Instead of a config dictionary `{\"num_rounds\": 3, \"round_timeout\": "
+"600.0}`, `start_server` and `start_simulation` now expect a configuration"
+" object of type `flwr.server.ServerConfig`. `ServerConfig` takes the same"
+" arguments that as the previous config dict, but it makes writing type-"
+"safe code easier and the default parameters values more transparent."
+msgstr ""
+
+#: ../../source/ref-changelog.md:437
+msgid ""
+"**Rename built-in strategy parameters for clarity** "
+"([#1334](https://github.com/adap/flower/pull/1334))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:439
+msgid ""
+"The following built-in strategy parameters were renamed to improve "
+"readability and consistency with other API's:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:441
+msgid "`fraction_eval` --> `fraction_evaluate`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:442
+msgid "`min_eval_clients` --> `min_evaluate_clients`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:443
+msgid "`eval_fn` --> `evaluate_fn`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:445
+msgid ""
+"**Update default arguments of built-in strategies** "
+"([#1278](https://github.com/adap/flower/pull/1278))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:447
+msgid ""
+"All built-in strategies now use `fraction_fit=1.0` and "
+"`fraction_evaluate=1.0`, which means they select *all* currently "
+"available clients for training and evaluation. Projects that relied on "
+"the previous default values can get the previous behaviour by "
+"initializing the strategy in the following way:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:449
+msgid "`strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:451
+msgid ""
+"**Add** `server_round` **to** `Strategy.evaluate` "
+"([#1334](https://github.com/adap/flower/pull/1334))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:453
+msgid ""
+"The `Strategy` method `evaluate` now receives the current round of "
+"federated learning/evaluation as the first parameter."
+msgstr ""
+
+#: ../../source/ref-changelog.md:455
+msgid ""
+"**Add** `server_round` **and** `config` **parameters to** `evaluate_fn` "
+"([#1334](https://github.com/adap/flower/pull/1334))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:457
+msgid ""
+"The `evaluate_fn` passed to built-in strategies like `FedAvg` now takes "
+"three parameters: (1) The current round of federated learning/evaluation "
+"(`server_round`), (2) the model parameters to evaluate (`parameters`), "
+"and (3) a config dictionary (`config`)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:459
+msgid ""
+"**Rename** `rnd` **to** `server_round` "
+"([#1321](https://github.com/adap/flower/pull/1321))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:461
+msgid ""
+"Several Flower methods and functions (`evaluate_fn`, `configure_fit`, "
+"`aggregate_fit`, `configure_evaluate`, `aggregate_evaluate`) receive the "
+"current round of federated learning/evaluation as their first parameter. "
+"To improve reaability and avoid confusion with *random*, this parameter "
+"has been renamed from `rnd` to `server_round`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:463
+msgid ""
+"**Move** `flwr.dataset` **to** `flwr_baselines` "
+"([#1273](https://github.com/adap/flower/pull/1273))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:465
+msgid "The experimental package `flwr.dataset` was migrated to Flower Baselines."
+msgstr ""
+
+#: ../../source/ref-changelog.md:467
+msgid ""
+"**Remove experimental strategies** "
+"([#1280](https://github.com/adap/flower/pull/1280))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:469
+msgid ""
+"Remove unmaintained experimental strategies (`FastAndSlow`, `FedFSv0`, "
+"`FedFSv1`)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:471
+msgid ""
+"**Rename** `Weights` **to** `NDArrays` "
+"([#1258](https://github.com/adap/flower/pull/1258), "
+"[#1259](https://github.com/adap/flower/pull/1259))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:473
+msgid ""
+"`flwr.common.Weights` was renamed to `flwr.common.NDArrays` to better "
+"capture what this type is all about."
+msgstr ""
+
+#: ../../source/ref-changelog.md:475
+msgid ""
+"**Remove antiquated** `force_final_distributed_eval` **from** "
+"`start_server` ([#1258](https://github.com/adap/flower/pull/1258), "
+"[#1259](https://github.com/adap/flower/pull/1259))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:477
+msgid ""
+"The `start_server` parameter `force_final_distributed_eval` has long been"
+" a historic artefact, in this release it is finally gone for good."
+msgstr ""
+
+#: ../../source/ref-changelog.md:479
+msgid ""
+"**Make** `get_parameters` **configurable** "
+"([#1242](https://github.com/adap/flower/pull/1242))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:481
+msgid ""
+"The `get_parameters` method now accepts a configuration dictionary, just "
+"like `get_properties`, `fit`, and `evaluate`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:483
+msgid ""
+"**Replace** `num_rounds` **in** `start_simulation` **with new** `config` "
+"**parameter** ([#1281](https://github.com/adap/flower/pull/1281))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:485
+msgid ""
+"The `start_simulation` function now accepts a configuration dictionary "
+"`config` instead of the `num_rounds` integer. This improves the "
+"consistency between `start_simulation` and `start_server` and makes "
+"transitioning between the two easier."
+msgstr ""
+
+#: ../../source/ref-changelog.md:489
+msgid ""
+"**Support Python 3.10** "
+"([#1320](https://github.com/adap/flower/pull/1320))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:491
+msgid ""
+"The previous Flower release introduced experimental support for Python "
+"3.10, this release declares Python 3.10 support as stable."
+msgstr ""
+
+#: ../../source/ref-changelog.md:493
+msgid ""
+"**Make all** `Client` **and** `NumPyClient` **methods optional** "
+"([#1260](https://github.com/adap/flower/pull/1260), "
+"[#1277](https://github.com/adap/flower/pull/1277))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:495
+msgid ""
+"The `Client`/`NumPyClient` methods `get_properties`, `get_parameters`, "
+"`fit`, and `evaluate` are all optional. This enables writing clients that"
+" implement, for example, only `fit`, but no other method. No need to "
+"implement `evaluate` when using centralized evaluation!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:497
+msgid ""
+"**Enable passing a** `Server` **instance to** `start_simulation` "
+"([#1281](https://github.com/adap/flower/pull/1281))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:499
+msgid ""
+"Similar to `start_server`, `start_simulation` now accepts a full `Server`"
+" instance. This enables users to heavily customize the execution of "
+"eperiments and opens the door to running, for example, async FL using the"
+" Virtual Client Engine."
+msgstr ""
+
+#: ../../source/ref-changelog.md:501
+msgid ""
+"**Update code examples** "
+"([#1291](https://github.com/adap/flower/pull/1291), "
+"[#1286](https://github.com/adap/flower/pull/1286), "
+"[#1282](https://github.com/adap/flower/pull/1282))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:503
+msgid ""
+"Many code examples received small or even large maintenance updates, "
+"among them are"
+msgstr ""
+
+#: ../../source/ref-changelog.md:505
+msgid "`scikit-learn`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:506
+msgid "`simulation_pytorch`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:507
+msgid "`quickstart_pytorch`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:508
+msgid "`quickstart_simulation`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:509
+msgid "`quickstart_tensorflow`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:510
+msgid "`advanced_tensorflow`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:512
+msgid ""
+"**Remove the obsolete simulation example** "
+"([#1328](https://github.com/adap/flower/pull/1328))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:514
+msgid ""
+"Removes the obsolete `simulation` example and renames "
+"`quickstart_simulation` to `simulation_tensorflow` so it fits withs the "
+"naming of `simulation_pytorch`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:516
+msgid ""
+"**Update documentation** "
+"([#1223](https://github.com/adap/flower/pull/1223), "
+"[#1209](https://github.com/adap/flower/pull/1209), "
+"[#1251](https://github.com/adap/flower/pull/1251), "
+"[#1257](https://github.com/adap/flower/pull/1257), "
+"[#1267](https://github.com/adap/flower/pull/1267), "
+"[#1268](https://github.com/adap/flower/pull/1268), "
+"[#1300](https://github.com/adap/flower/pull/1300), "
+"[#1304](https://github.com/adap/flower/pull/1304), "
+"[#1305](https://github.com/adap/flower/pull/1305), "
+"[#1307](https://github.com/adap/flower/pull/1307))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:518
+msgid ""
+"One substantial documentation update fixes multiple smaller rendering "
+"issues, makes titles more succinct to improve navigation, removes a "
+"deprecated library, updates documentation dependencies, includes the "
+"`flwr.common` module in the API reference, includes support for markdown-"
+"based documentation, migrates the changelog from `.rst` to `.md`, and "
+"fixes a number of smaller details!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:520 ../../source/ref-changelog.md:575
+#: ../../source/ref-changelog.md:644 ../../source/ref-changelog.md:683
+msgid "**Minor updates**"
+msgstr ""
+
+#: ../../source/ref-changelog.md:522
+msgid ""
+"Add round number to fit and evaluate log messages "
+"([#1266](https://github.com/adap/flower/pull/1266))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:523
+msgid ""
+"Add secure gRPC connection to the `advanced_tensorflow` code example "
+"([#847](https://github.com/adap/flower/pull/847))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:524
+msgid ""
+"Update developer tooling "
+"([#1231](https://github.com/adap/flower/pull/1231), "
+"[#1276](https://github.com/adap/flower/pull/1276), "
+"[#1301](https://github.com/adap/flower/pull/1301), "
+"[#1310](https://github.com/adap/flower/pull/1310))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:525
+msgid ""
+"Rename ProtoBuf messages to improve consistency "
+"([#1214](https://github.com/adap/flower/pull/1214), "
+"[#1258](https://github.com/adap/flower/pull/1258), "
+"[#1259](https://github.com/adap/flower/pull/1259))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:527
+msgid "v0.19.0 (2022-05-18)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:531
+msgid ""
+"**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))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:533
+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). "
+"With this first preview release we're also inviting the community to "
+"[contribute their own baselines](https://flower.dev/docs/contributing-"
+"baselines.html)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:535
+msgid ""
+"**C++ client SDK (preview) and code example** "
+"([#1111](https://github.com/adap/flower/pull/1111))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:537
+msgid ""
+"Preview support for Flower clients written in C++. The C++ preview "
+"includes a Flower client SDK and a quickstart code example that "
+"demonstrates a simple C++ client using the SDK."
+msgstr ""
+
+#: ../../source/ref-changelog.md:539
+msgid ""
+"**Add experimental support for Python 3.10 and Python 3.11** "
+"([#1135](https://github.com/adap/flower/pull/1135))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:541
+msgid ""
+"Python 3.10 is the latest stable release of Python and Python 3.11 is due"
+" to be released in October. This Flower release adds experimental support"
+" for both Python versions."
+msgstr ""
+
+#: ../../source/ref-changelog.md:543
+msgid ""
+"**Aggregate custom metrics through user-provided functions** "
+"([#1144](https://github.com/adap/flower/pull/1144))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:545
+msgid ""
+"Custom metrics (e.g., `accuracy`) can now be aggregated without having to"
+" customize the strategy. Built-in strategies support two new arguments, "
+"`fit_metrics_aggregation_fn` and `evaluate_metrics_aggregation_fn`, that "
+"allow passing custom metric aggregation functions."
+msgstr ""
+
+#: ../../source/ref-changelog.md:547
+msgid ""
+"**User-configurable round timeout** "
+"([#1162](https://github.com/adap/flower/pull/1162))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:549
+msgid ""
+"A new configuration value allows the round timeout to be set for "
+"`start_server` and `start_simulation`. If the `config` dictionary "
+"contains a `round_timeout` key (with a `float` value in seconds), the "
+"server will wait *at least* `round_timeout` seconds before it closes the "
+"connection."
+msgstr ""
+
+#: ../../source/ref-changelog.md:551
+msgid ""
+"**Enable both federated evaluation and centralized evaluation to be used "
+"at the same time in all built-in strategies** "
+"([#1091](https://github.com/adap/flower/pull/1091))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:553
+msgid ""
+"Built-in strategies can now perform both federated evaluation (i.e., "
+"client-side) and centralized evaluation (i.e., server-side) in the same "
+"round. Federated evaluation can be disabled by setting `fraction_eval` to"
+" `0.0`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:555
+msgid ""
+"**Two new Jupyter Notebook tutorials** "
+"([#1141](https://github.com/adap/flower/pull/1141))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:557
+msgid ""
+"Two Jupyter Notebook tutorials (compatible with Google Colab) explain "
+"basic and intermediate Flower features:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:559
+msgid ""
+"*An Introduction to Federated Learning*: [Open in "
+"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-1"
+"-Intro-to-FL-PyTorch.ipynb)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:561
+msgid ""
+"*Using Strategies in Federated Learning*: [Open in "
+"Colab](https://colab.research.google.com/github/adap/flower/blob/main/tutorials/Flower-2"
+"-Strategies-in-FL-PyTorch.ipynb)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:563
+msgid ""
+"**New FedAvgM strategy (Federated Averaging with Server Momentum)** "
+"([#1076](https://github.com/adap/flower/pull/1076))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:565
+msgid ""
+"The new `FedAvgM` strategy implements Federated Averaging with Server "
+"Momentum \\[Hsu et al., 2019\\]."
+msgstr ""
+
+#: ../../source/ref-changelog.md:567
+msgid ""
+"**New advanced PyTorch code example** "
+"([#1007](https://github.com/adap/flower/pull/1007))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:569
+msgid ""
+"A new code example (`advanced_pytorch`) demonstrates advanced Flower "
+"concepts with PyTorch."
+msgstr ""
+
+#: ../../source/ref-changelog.md:571
+msgid ""
+"**New JAX code example** "
+"([#906](https://github.com/adap/flower/pull/906), "
+"[#1143](https://github.com/adap/flower/pull/1143))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:573
+msgid ""
+"A new code example (`jax_from_centralized_to_federated`) shows federated "
+"learning with JAX and Flower."
+msgstr ""
+
+#: ../../source/ref-changelog.md:577
+msgid ""
+"New option to keep Ray running if Ray was already initialized in "
+"`start_simulation` ([#1177](https://github.com/adap/flower/pull/1177))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:578
+msgid ""
+"Add support for custom `ClientManager` as a `start_simulation` parameter "
+"([#1171](https://github.com/adap/flower/pull/1171))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:579
+msgid ""
+"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))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:580
+msgid ""
+"New mobile-friendly documentation theme "
+"([#1174](https://github.com/adap/flower/pull/1174))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:581
+msgid ""
+"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))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:585
+msgid ""
+"**Remove deprecated support for Python 3.6** "
+"([#871](https://github.com/adap/flower/pull/871))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:586
+msgid ""
+"**Remove deprecated KerasClient** "
+"([#857](https://github.com/adap/flower/pull/857))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:587
+msgid ""
+"**Remove deprecated no-op extra installs** "
+"([#973](https://github.com/adap/flower/pull/973))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:588
+msgid ""
+"**Remove deprecated proto fields from** `FitRes` **and** `EvaluateRes` "
+"([#869](https://github.com/adap/flower/pull/869))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:589
+msgid ""
+"**Remove deprecated QffedAvg strategy (replaced by QFedAvg)** "
+"([#1107](https://github.com/adap/flower/pull/1107))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:590
+msgid ""
+"**Remove deprecated DefaultStrategy strategy** "
+"([#1142](https://github.com/adap/flower/pull/1142))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:591
+msgid ""
+"**Remove deprecated support for eval_fn accuracy return value** "
+"([#1142](https://github.com/adap/flower/pull/1142))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:592
+msgid ""
+"**Remove deprecated support for passing initial parameters as NumPy "
+"ndarrays** ([#1142](https://github.com/adap/flower/pull/1142))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:594
+msgid "v0.18.0 (2022-02-28)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:598
+msgid ""
+"**Improved Virtual Client Engine compatibility with Jupyter Notebook / "
+"Google Colab** ([#866](https://github.com/adap/flower/pull/866), "
+"[#872](https://github.com/adap/flower/pull/872), "
+"[#833](https://github.com/adap/flower/pull/833), "
+"[#1036](https://github.com/adap/flower/pull/1036))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:600
+msgid ""
+"Simulations (using the Virtual Client Engine through `start_simulation`) "
+"now work more smoothly on Jupyter Notebooks (incl. Google Colab) after "
+"installing Flower with the `simulation` extra (`pip install "
+"flwr[simulation]`)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:602
+msgid ""
+"**New Jupyter Notebook code example** "
+"([#833](https://github.com/adap/flower/pull/833))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:604
+msgid ""
+"A new code example (`quickstart_simulation`) demonstrates Flower "
+"simulations using the Virtual Client Engine through Jupyter Notebook "
+"(incl. Google Colab)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:606
+msgid ""
+"**Client properties (feature preview)** "
+"([#795](https://github.com/adap/flower/pull/795))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:608
+msgid ""
+"Clients can implement a new method `get_properties` to enable server-side"
+" strategies to query client properties."
+msgstr ""
+
+#: ../../source/ref-changelog.md:610
+msgid ""
+"**Experimental Android support with TFLite** "
+"([#865](https://github.com/adap/flower/pull/865))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:612
+msgid ""
+"Android support has finally arrived in `main`! Flower is both client-"
+"agnostic and framework-agnostic by design. One can integrate arbitrary "
+"client platforms and with this release, using Flower on Android has "
+"become a lot easier."
+msgstr ""
+
+#: ../../source/ref-changelog.md:614
+msgid ""
+"The example uses TFLite on the client side, along with a new "
+"`FedAvgAndroid` strategy. The Android client and `FedAvgAndroid` are "
+"still experimental, but they are a first step towards a fully-fledged "
+"Android SDK and a unified `FedAvg` implementation that integrated the new"
+" functionality from `FedAvgAndroid`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:616
+msgid ""
+"**Make gRPC keepalive time user-configurable and decrease default "
+"keepalive time** ([#1069](https://github.com/adap/flower/pull/1069))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:618
+msgid ""
+"The default gRPC keepalive time has been reduced to increase the "
+"compatibility of Flower with more cloud environments (for example, "
+"Microsoft Azure). Users can configure the keepalive time to customize the"
+" gRPC stack based on specific requirements."
+msgstr ""
+
+#: ../../source/ref-changelog.md:620
+msgid ""
+"**New differential privacy example using Opacus and PyTorch** "
+"([#805](https://github.com/adap/flower/pull/805))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:622
+msgid ""
+"A new code example (`opacus`) demonstrates differentially-private "
+"federated learning with Opacus, PyTorch, and Flower."
+msgstr ""
+
+#: ../../source/ref-changelog.md:624
+msgid ""
+"**New Hugging Face Transformers code example** "
+"([#863](https://github.com/adap/flower/pull/863))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:626
+msgid ""
+"A new code example (`quickstart_huggingface`) demonstrates usage of "
+"Hugging Face Transformers with Flower."
+msgstr ""
+
+#: ../../source/ref-changelog.md:628
+msgid ""
+"**New MLCube code example** "
+"([#779](https://github.com/adap/flower/pull/779), "
+"[#1034](https://github.com/adap/flower/pull/1034), "
+"[#1065](https://github.com/adap/flower/pull/1065), "
+"[#1090](https://github.com/adap/flower/pull/1090))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:630
+msgid ""
+"A new code example (`quickstart_mlcube`) demonstrates usage of MLCube "
+"with Flower."
+msgstr ""
+
+#: ../../source/ref-changelog.md:632
+msgid ""
+"**SSL-enabled server and client** "
+"([#842](https://github.com/adap/flower/pull/842),  "
+"[#844](https://github.com/adap/flower/pull/844),  "
+"[#845](https://github.com/adap/flower/pull/845), "
+"[#847](https://github.com/adap/flower/pull/847), "
+"[#993](https://github.com/adap/flower/pull/993), "
+"[#994](https://github.com/adap/flower/pull/994))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:634
+msgid ""
+"SSL enables secure encrypted connections between clients and servers. "
+"This release open-sources the Flower secure gRPC implementation to make "
+"encrypted communication channels accessible to all Flower users."
+msgstr ""
+
+#: ../../source/ref-changelog.md:636
+msgid ""
+"**Updated** `FedAdam` **and** `FedYogi` **strategies** "
+"([#885](https://github.com/adap/flower/pull/885), "
+"[#895](https://github.com/adap/flower/pull/895))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:638
+msgid ""
+"`FedAdam` and `FedAdam` match the latest version of the Adaptive "
+"Federated Optimization paper."
+msgstr ""
+
+#: ../../source/ref-changelog.md:640
+msgid ""
+"**Initialize** `start_simulation` **with a list of client IDs** "
+"([#860](https://github.com/adap/flower/pull/860))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:642
+msgid ""
+"`start_simulation` can now be called with a list of client IDs "
+"(`clients_ids`, type: `List[str]`). Those IDs will be passed to the "
+"`client_fn` whenever a client needs to be initialized, which can make it "
+"easier to load data partitions that are not accessible through `int` "
+"identifiers."
+msgstr ""
+
+#: ../../source/ref-changelog.md:646
+msgid ""
+"Update `num_examples` calculation in PyTorch code examples in "
+"([#909](https://github.com/adap/flower/pull/909))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:647
+msgid ""
+"Expose Flower version through `flwr.__version__` "
+"([#952](https://github.com/adap/flower/pull/952))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:648
+msgid ""
+"`start_server` in `app.py` now returns a `History` object containing "
+"metrics from training ([#974](https://github.com/adap/flower/pull/974))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:649
+msgid ""
+"Make `max_workers` (used by `ThreadPoolExecutor`) configurable "
+"([#978](https://github.com/adap/flower/pull/978))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:650
+msgid ""
+"Increase sleep time after server start to three seconds in all code "
+"examples ([#1086](https://github.com/adap/flower/pull/1086))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:651
+msgid ""
+"Added a new FAQ section to the documentation "
+"([#948](https://github.com/adap/flower/pull/948))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:652
+msgid ""
+"And many more under-the-hood changes, library updates, documentation "
+"changes, and tooling improvements!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:656
+msgid ""
+"**Removed** `flwr_example` **and** `flwr_experimental` **from release "
+"build** ([#869](https://github.com/adap/flower/pull/869))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:658
+msgid ""
+"The packages `flwr_example` and `flwr_experimental` have been deprecated "
+"since Flower 0.12.0 and they are not longer included in Flower release "
+"builds. The associated extras (`baseline`, `examples-pytorch`, `examples-"
+"tensorflow`, `http-logger`, `ops`) are now no-op and will be removed in "
+"an upcoming release."
+msgstr ""
+
+#: ../../source/ref-changelog.md:660
+msgid "v0.17.0 (2021-09-24)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:664
+msgid ""
+"**Experimental virtual client engine** "
+"([#781](https://github.com/adap/flower/pull/781) "
+"[#790](https://github.com/adap/flower/pull/790) "
+"[#791](https://github.com/adap/flower/pull/791))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:666
+msgid ""
+"One of Flower's goals is to enable research at scale. This release "
+"enables a first (experimental) peek at a major new feature, codenamed the"
+" virtual client engine. Virtual clients enable simulations that scale to "
+"a (very) large number of clients on a single machine or compute cluster. "
+"The easiest way to test the new functionality is to look at the two new "
+"code examples called `quickstart_simulation` and `simulation_pytorch`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:668
+msgid ""
+"The feature is still experimental, so there's no stability guarantee for "
+"the API. It's also not quite ready for prime time and comes with a few "
+"known caveats. However, those who are curious are encouraged to try it "
+"out and share their thoughts."
+msgstr ""
+
+#: ../../source/ref-changelog.md:670
+msgid ""
+"**New built-in strategies** "
+"([#828](https://github.com/adap/flower/pull/828) "
+"[#822](https://github.com/adap/flower/pull/822))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:672
+msgid ""
+"FedYogi - Federated learning strategy using Yogi on server-side. "
+"Implementation based on https://arxiv.org/abs/2003.00295"
+msgstr ""
+
+#: ../../source/ref-changelog.md:673
+msgid ""
+"FedAdam - Federated learning strategy using Adam on server-side. "
+"Implementation based on https://arxiv.org/abs/2003.00295"
+msgstr ""
+
+#: ../../source/ref-changelog.md:675
+msgid ""
+"**New PyTorch Lightning code example** "
+"([#617](https://github.com/adap/flower/pull/617))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:677
+msgid ""
+"**New Variational Auto-Encoder code example** "
+"([#752](https://github.com/adap/flower/pull/752))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:679
+msgid ""
+"**New scikit-learn code example** "
+"([#748](https://github.com/adap/flower/pull/748))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:681
+msgid ""
+"**New experimental TensorBoard strategy** "
+"([#789](https://github.com/adap/flower/pull/789))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:685
+msgid ""
+"Improved advanced TensorFlow code example "
+"([#769](https://github.com/adap/flower/pull/769))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:686
+msgid ""
+"Warning when `min_available_clients` is misconfigured "
+"([#830](https://github.com/adap/flower/pull/830))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:687
+msgid ""
+"Improved gRPC server docs "
+"([#841](https://github.com/adap/flower/pull/841))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:688
+msgid ""
+"Improved error message in `NumPyClient` "
+"([#851](https://github.com/adap/flower/pull/851))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:689
+msgid ""
+"Improved PyTorch quickstart code example "
+"([#852](https://github.com/adap/flower/pull/852))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:693
+msgid ""
+"**Disabled final distributed evaluation** "
+"([#800](https://github.com/adap/flower/pull/800))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:695
+msgid ""
+"Prior behaviour was to perform a final round of distributed evaluation on"
+" all connected clients, which is often not required (e.g., when using "
+"server-side evaluation). The prior behaviour can be enabled by passing "
+"`force_final_distributed_eval=True` to `start_server`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:697
+msgid ""
+"**Renamed q-FedAvg strategy** "
+"([#802](https://github.com/adap/flower/pull/802))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:699
+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 "
+"deprecated) `QffedAvg` class is still available for compatibility reasons"
+" (it will be removed in a future release)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:701
+msgid ""
+"**Deprecated and renamed code example** `simulation_pytorch` **to** "
+"`simulation_pytorch_legacy` "
+"([#791](https://github.com/adap/flower/pull/791))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:703
+msgid ""
+"This example has been replaced by a new example. The new example is based"
+" on the experimental virtual client engine, which will become the new "
+"default way of doing most types of large-scale simulations in Flower. The"
+" existing example was kept for reference purposes, but it might be "
+"removed in the future."
+msgstr ""
+
+#: ../../source/ref-changelog.md:705
+msgid "v0.16.0 (2021-05-11)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:709
+msgid ""
+"**New built-in strategies** "
+"([#549](https://github.com/adap/flower/pull/549))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:711
+msgid "(abstract) FedOpt"
+msgstr ""
+
+#: ../../source/ref-changelog.md:712
+msgid "FedAdagrad"
+msgstr ""
+
+#: ../../source/ref-changelog.md:714
+msgid ""
+"**Custom metrics for server and strategies** "
+"([#717](https://github.com/adap/flower/pull/717))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:716
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:718
+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 "
+"`eval_fn`) to return more than two evaluation metrics. Strategies can "
+"even return *aggregated* metrics dictionaries for the server to keep "
+"track of."
+msgstr ""
+
+#: ../../source/ref-changelog.md:720
+msgid ""
+"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}`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:722
+msgid ""
+"Flower 0.15-style return types are deprecated (but still supported), "
+"compatibility will be removed in a future release."
+msgstr ""
+
+#: ../../source/ref-changelog.md:724
+msgid ""
+"**Migration warnings for deprecated functionality** "
+"([#690](https://github.com/adap/flower/pull/690))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:726
+msgid ""
+"Earlier versions of Flower were often migrated to new APIs, while "
+"maintaining compatibility with legacy APIs. This release introduces "
+"detailed warning messages if usage of deprecated APIs is detected. The "
+"new warning messages often provide details on how to migrate to more "
+"recent APIs, thus easing the transition from one release to another."
+msgstr ""
+
+#: ../../source/ref-changelog.md:728
+msgid ""
+"Improved docs and docstrings "
+"([#691](https://github.com/adap/flower/pull/691) "
+"[#692](https://github.com/adap/flower/pull/692) "
+"[#713](https://github.com/adap/flower/pull/713))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:730
+msgid "MXNet example and documentation"
+msgstr ""
+
+#: ../../source/ref-changelog.md:732
+msgid ""
+"FedBN implementation in example PyTorch: From Centralized To Federated "
+"([#696](https://github.com/adap/flower/pull/696) "
+"[#702](https://github.com/adap/flower/pull/702) "
+"[#705](https://github.com/adap/flower/pull/705))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:736
+msgid ""
+"**Serialization-agnostic server** "
+"([#721](https://github.com/adap/flower/pull/721))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:738
+msgid ""
+"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)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:740
+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 "
+"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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:742
+msgid ""
+"Deprecated `flwr.server.Server.evaluate`, use "
+"`flwr.server.Server.evaluate_round` instead "
+"([#717](https://github.com/adap/flower/pull/717))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:744
+msgid "v0.15.0 (2021-03-12)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:748
+msgid ""
+"**Server-side parameter initialization** "
+"([#658](https://github.com/adap/flower/pull/658))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:750
+msgid ""
+"Model parameters can now be initialized on the server-side. Server-side "
+"parameter initialization works via a new `Strategy` method called "
+"`initialize_parameters`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:752
+msgid ""
+"Built-in strategies support a new constructor argument called "
+"`initial_parameters` to set the initial parameters. Built-in strategies "
+"will provide these initial parameters to the server on startup and then "
+"delete them to free the memory afterwards."
+msgstr ""
+
+#: ../../source/ref-changelog.md:771
+msgid ""
+"If no initial parameters are provided to the strategy, the server will "
+"continue to use the current behaviour (namely, it will ask one of the "
+"connected clients for its parameters and use these as the initial global "
+"parameters)."
+msgstr ""
+
+#: ../../source/ref-changelog.md:773
+msgid "Deprecations"
+msgstr ""
+
+#: ../../source/ref-changelog.md:775
+msgid ""
+"Deprecate `flwr.server.strategy.DefaultStrategy` (migrate to "
+"`flwr.server.strategy.FedAvg`, which is equivalent)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:777
+msgid "v0.14.0 (2021-02-18)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:781
+msgid ""
+"**Generalized** `Client.fit` **and** `Client.evaluate` **return values** "
+"([#610](https://github.com/adap/flower/pull/610) "
+"[#572](https://github.com/adap/flower/pull/572) "
+"[#633](https://github.com/adap/flower/pull/633))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:783
+msgid ""
+"Clients can now return an additional dictionary mapping `str` keys to "
+"values of the following types: `bool`, `bytes`, `float`, `int`, `str`. "
+"This means one can return almost arbitrary values from `fit`/`evaluate` "
+"and make use of them on the server side!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:785
+msgid ""
+"This improvement also allowed for more consistent return types between "
+"`fit` and `evaluate`: `evaluate` should now return a tuple `(float, int, "
+"dict)` representing the loss, number of examples, and a dictionary "
+"holding arbitrary problem-specific values like accuracy."
+msgstr ""
+
+#: ../../source/ref-changelog.md:787
+msgid ""
+"In case you wondered: this feature is compatible with existing projects, "
+"the additional dictionary return value is optional. New code should "
+"however migrate to the new return types to be compatible with upcoming "
+"Flower releases (`fit`: `List[np.ndarray], int, Dict[str, Scalar]`, "
+"`evaluate`: `float, int, Dict[str, Scalar]`). See the example below for "
+"details."
+msgstr ""
+
+#: ../../source/ref-changelog.md:789
+msgid ""
+"*Code example:* note the additional dictionary return values in both "
+"`FlwrClient.fit` and `FlwrClient.evaluate`:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:804
+msgid ""
+"**Generalized** `config` **argument in** `Client.fit` **and** "
+"`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:806
+msgid ""
+"The `config` argument used to be of type `Dict[str, str]`, which means "
+"that dictionary values were expected to be strings. The new release "
+"generalizes this to enable values of the following types: `bool`, "
+"`bytes`, `float`, `int`, `str`."
+msgstr ""
+
+#: ../../source/ref-changelog.md:808
+msgid ""
+"This means one can now pass almost arbitrary values to `fit`/`evaluate` "
+"using the `config` dictionary. Yay, no more `str(epochs)` on the server-"
+"side and `int(config[\"epochs\"])` on the client side!"
+msgstr ""
+
+#: ../../source/ref-changelog.md:810
+msgid ""
+"*Code example:* note that the `config` dictionary now contains non-`str` "
+"values in both `Client.fit` and `Client.evaluate`:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:827
+msgid "v0.13.0 (2021-01-08)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:831
+msgid ""
+"New example: PyTorch From Centralized To Federated "
+"([#549](https://github.com/adap/flower/pull/549))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:832
+msgid "Improved documentation"
+msgstr ""
+
+#: ../../source/ref-changelog.md:833
+msgid "New documentation theme ([#551](https://github.com/adap/flower/pull/551))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:834
+msgid "New API reference ([#554](https://github.com/adap/flower/pull/554))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:835
+msgid ""
+"Updated examples documentation "
+"([#549](https://github.com/adap/flower/pull/549))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:836
+msgid ""
+"Removed obsolete documentation "
+"([#548](https://github.com/adap/flower/pull/548))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:838
+msgid "Bugfix:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:840
+msgid ""
+"`Server.fit` does not disconnect clients when finished, disconnecting the"
+" clients is now handled in `flwr.server.start_server` "
+"([#553](https://github.com/adap/flower/pull/553) "
+"[#540](https://github.com/adap/flower/issues/540))."
+msgstr ""
+
+#: ../../source/ref-changelog.md:842
+msgid "v0.12.0 (2020-12-07)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:844 ../../source/ref-changelog.md:860
+msgid "Important changes:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:846
+msgid ""
+"Added an example for embedded devices "
+"([#507](https://github.com/adap/flower/pull/507))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:847
+msgid ""
+"Added a new NumPyClient (in addition to the existing KerasClient) "
+"([#504](https://github.com/adap/flower/pull/504) "
+"[#508](https://github.com/adap/flower/pull/508))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:848
+msgid ""
+"Deprecated `flwr_example` package and started to migrate examples into "
+"the top-level `examples` directory "
+"([#494](https://github.com/adap/flower/pull/494) "
+"[#512](https://github.com/adap/flower/pull/512))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:850
+msgid "v0.11.0 (2020-11-30)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:852
+msgid "Incompatible changes:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:854
+msgid ""
+"Renamed strategy methods "
+"([#486](https://github.com/adap/flower/pull/486)) to unify the naming of "
+"Flower's public APIs. Other public methods/functions (e.g., every method "
+"in `Client`, but also `Strategy.evaluate`) do not use the `on_` prefix, "
+"which is why we're removing it from the four methods in Strategy. To "
+"migrate rename the following `Strategy` methods accordingly:"
+msgstr ""
+
+#: ../../source/ref-changelog.md:855
+msgid "`on_configure_evaluate` => `configure_evaluate`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:856
+msgid "`on_aggregate_evaluate` => `aggregate_evaluate`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:857
+msgid "`on_configure_fit` => `configure_fit`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:858
+msgid "`on_aggregate_fit` => `aggregate_fit`"
+msgstr ""
+
+#: ../../source/ref-changelog.md:862
+msgid ""
+"Deprecated `DefaultStrategy` "
+"([#479](https://github.com/adap/flower/pull/479)). To migrate use "
+"`FedAvg` instead."
+msgstr ""
+
+#: ../../source/ref-changelog.md:863
+msgid ""
+"Simplified examples and baselines "
+"([#484](https://github.com/adap/flower/pull/484))."
+msgstr ""
+
+#: ../../source/ref-changelog.md:864
+msgid ""
+"Removed presently unused `on_conclude_round` from strategy interface "
+"([#483](https://github.com/adap/flower/pull/483))."
+msgstr ""
+
+#: ../../source/ref-changelog.md:865
+msgid ""
+"Set minimal Python version to 3.6.1 instead of 3.6.9 "
+"([#471](https://github.com/adap/flower/pull/471))."
+msgstr ""
+
+#: ../../source/ref-changelog.md:866
+msgid ""
+"Improved `Strategy` docstrings "
+"([#470](https://github.com/adap/flower/pull/470))."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:2
+msgid "Example projects"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:4
+msgid ""
+"Flower comes with a number of usage examples. The examples demonstrate "
+"how Flower can be used to federate different kinds of existing machine "
+"learning pipelines, usually leveraging popular machine learning "
+"frameworks such as `PyTorch <https://pytorch.org/>`_ or `TensorFlow "
+"<https://www.tensorflow.org/>`_."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:11
+msgid ""
+"Flower usage examples used to be bundled with Flower in a package called "
+"``flwr_example``. We are migrating those examples to standalone projects "
+"to make them easier to use. All new examples are based in the directory "
+"`examples <https://github.com/adap/flower/tree/main/examples>`_."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:16
+msgid "The following examples are available as standalone projects."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:20
+msgid "Quickstart TensorFlow/Keras"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:22
+msgid ""
+"The TensorFlow/Keras quickstart example shows CIFAR-10 image "
+"classification with MobileNetV2:"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:25
+msgid ""
+"`Quickstart TensorFlow (Code) "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-"
+"tensorflow>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:26
+msgid ""
+"`Quickstart TensorFlow (Tutorial) <https://flower.dev/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"
+"-federated-learning-in-less-than-20-lines-of-code>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:31
+#: ../../source/tutorial-quickstart-pytorch.rst:5
+msgid "Quickstart PyTorch"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:33
+msgid ""
+"The PyTorch quickstart example shows CIFAR-10 image classification with a"
+" simple Convolutional Neural Network:"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:36
+msgid ""
+"`Quickstart PyTorch (Code) "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:37
+msgid ""
+"`Quickstart PyTorch (Tutorial) <https://flower.dev/docs/framework"
+"/tutorial-quickstart-pytorch.html>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:41
+msgid "PyTorch: From Centralized To Federated"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:43
+msgid ""
+"This example shows how a regular PyTorch project can be federated using "
+"Flower:"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:45
+msgid ""
+"`PyTorch: From Centralized To Federated (Code) "
+"<https://github.com/adap/flower/tree/main/examples/pytorch-from-"
+"centralized-to-federated>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:46
+msgid ""
+"`PyTorch: From Centralized To Federated (Tutorial) "
+"<https://flower.dev/docs/framework/example-pytorch-from-centralized-to-"
+"federated.html>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:50
+msgid "Federated Learning on Raspberry Pi and Nvidia Jetson"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:52
+msgid ""
+"This example shows how Flower can be used to build a federated learning "
+"system that run across Raspberry Pi and Nvidia Jetson:"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:54
+msgid ""
+"`Federated Learning on Raspberry Pi and Nvidia Jetson (Code) "
+"<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_"
+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>`_"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:60
+msgid "Legacy Examples (`flwr_example`)"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:63
+msgid ""
+"The useage 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>`_."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:69
+msgid "Extra Dependencies"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:71
+msgid ""
+"The core Flower framework keeps a minimal set of dependencies. The "
+"examples demonstrate Flower in the context of different machine learning "
+"frameworks, so additional dependencies need to be installed before an "
+"example can be run."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:75
+msgid "For PyTorch examples::"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:79
+msgid "For TensorFlow examples::"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:83
+msgid "For both PyTorch and TensorFlow examples::"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:87
+msgid ""
+"Please consult :code:`pyproject.toml` for a full list of possible extras "
+"(section :code:`[tool.poetry.extras]`)."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:92
+msgid "PyTorch Examples"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:94
+msgid ""
+"Our PyTorch examples are based on PyTorch 1.7. They should work with "
+"other releases as well. So far, we provide the following examples."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:98
+msgid "CIFAR-10 Image Classification"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:100
+msgid ""
+"`CIFAR-10 and CIFAR-100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ "
+"are popular RGB image datasets. The Flower CIFAR-10 example uses PyTorch "
+"to train a simple CNN classifier in a federated learning setup with two "
+"clients."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:104
+#: ../../source/ref-example-projects.rst:121
+#: ../../source/ref-example-projects.rst:146
+msgid "First, start a Flower server:"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:106
+msgid "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:108
+#: ../../source/ref-example-projects.rst:125
+#: ../../source/ref-example-projects.rst:150
+msgid "Then, start the two clients in a new terminal window:"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:110
+msgid "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:112
+msgid "For more details, see :code:`src/py/flwr_example/pytorch_cifar`."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:115
+msgid "ImageNet-2012 Image Classification"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:117
+msgid ""
+"`ImageNet-2012 <http://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."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:123
+msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:127
+msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:129
+msgid "For more details, see :code:`src/py/flwr_example/pytorch_imagenet`."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:133
+msgid "TensorFlow Examples"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:135
+msgid ""
+"Our TensorFlow examples are based on TensorFlow 2.0 or newer. So far, we "
+"provide the following examples."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:139
+msgid "Fashion-MNIST Image Classification"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:141
+msgid ""
+"`Fashion-MNIST <https://github.com/zalandoresearch/fashion-mnist>`_ is "
+"often used as the \"Hello, world!\" of machine learning. We follow this "
+"tradition and provide an example which samples random local datasets from"
+" Fashion-MNIST and trains a simple image classification model over those "
+"partitions."
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:148
+msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:152
+msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh"
+msgstr ""
+
+#: ../../source/ref-example-projects.rst:154
+msgid ""
+"For more details, see "
+":code:`src/py/flwr_example/tensorflow_fashion_mnist`."
+msgstr ""
+
+#: ../../source/ref-faq.rst:4
+msgid ""
+"This page collects answers to commonly asked questions about Federated "
+"Learning with Flower."
+msgstr ""
+
+#: ../../source/ref-faq.rst
+msgid ":fa:`eye,mr-1` Can Flower run on Juptyter Notebooks / Google Colab?"
+msgstr ""
+
+#: ../../source/ref-faq.rst:8
+msgid ""
+"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:"
+msgstr ""
+
+#: ../../source/ref-faq.rst:10
+msgid ""
+"`Flower simulation PyTorch "
+"<https://colab.research.google.com/github/adap/flower/blob/main/examples"
+"/simulation-pytorch/sim.ipynb>`_"
+msgstr ""
+
+#: ../../source/ref-faq.rst:11
+msgid ""
+"`Flower simulation TensorFlow/Keras "
+"<https://colab.research.google.com/github/adap/flower/blob/main/examples"
+"/simulation-tensorflow/sim.ipynb>`_"
+msgstr ""
+
+#: ../../source/ref-faq.rst
+msgid ":fa:`eye,mr-1` How can I run Federated Learning on a Raspberry Pi?"
+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>`_"
+" and the corresponding `GitHub code example "
+"<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_."
+msgstr ""
+
+#: ../../source/ref-faq.rst
+msgid ":fa:`eye,mr-1` Does Flower support federated learning on Android devices?"
+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-"
+"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-"
+"kotlin.html>`_"
+msgstr ""
+
+#: ../../source/ref-faq.rst:22
+msgid "`Android Java example <https://flower.dev/docs/examples/android.html>`_"
+msgstr ""
+
+#: ../../source/ref-faq.rst
+msgid ":fa:`eye,mr-1` Can I combine federated learning with blockchain?"
+msgstr ""
+
+#: ../../source/ref-faq.rst:26
+msgid ""
+"Yes, of course. A list of available examples using Flower within a "
+"blockchain environment is available here:"
+msgstr ""
+
+#: ../../source/ref-faq.rst:28
+msgid ""
+"`Flower meets Nevermined GitHub Repository <https://github.com"
+"/nevermined-io/fl-demo/tree/master/image-classification-flower>`_."
+msgstr ""
+
+#: ../../source/ref-faq.rst:29
+msgid ""
+"`Flower meets Nevermined YouTube video "
+"<https://www.youtube.com/watch?v=A0A9hSlPhKI>`_."
+msgstr ""
+
+#: ../../source/ref-faq.rst:30
+msgid ""
+"`Flower meets KOSMoS <https://www.kosmos-bmbf.de/wp-"
+"content/uploads/sites/13/2021/05/Talk-Flower-Summit-2021.pdf>`_."
+msgstr ""
+
+#: ../../source/ref-faq.rst:31
+msgid ""
+"`Flower meets Talan blog post <https://www.linkedin.com/pulse/federated-"
+"learning-same-mask-different-faces-imen-"
+"ayari/?trackingId=971oIlxLQ9%2BA9RB0IQ73XQ%3D%3D>`_ ."
+msgstr ""
+
+#: ../../source/ref-faq.rst:32
+msgid ""
+"`Flower meets Talan GitHub Repository "
+"<https://gitlab.com/Talan_Innovation_Factory/food-waste-prevention>`_ ."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:1
+msgid "Telemetry"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:3
+msgid ""
+"The Flower open-source project collects **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."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:5
+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 that do not want to share anonymous usage metrics."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:7
+msgid "Principles"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:9
+msgid "We follow strong principles guarding anonymous usage metrics collection:"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:11
+msgid ""
+"**Optional:** You will always be able to disable telemetry; read on to "
+"learn “[How to opt-out](#how-to-opt-out)”."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:12
+msgid ""
+"**Anonymous:** The reported usage metrics are anonymous and do not "
+"contain any personally identifiable information (PII). See “[Collected "
+"metrics](#collected-metrics)” to understand what metrics are being "
+"reported."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:13
+msgid ""
+"**Transparent:** You can easily inspect what anonymous metrics are being "
+"reported; see the section “[How to inspect what is being reported](#how-"
+"to-inspect-what-is-being-reported)”"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:14
+msgid ""
+"**Open for feedback:** You can always reach out to us if you have "
+"feedback; see the section “[How to contact us](#how-to-contact-us)” for "
+"details."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:16
+msgid "How to opt-out"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:18
+msgid ""
+"When Flower starts, it will check for an environment variable called "
+"`FLWR_TELEMETRY_ENABLED`. Telemetry can easily be disabled by setting "
+"`FLWR_TELEMETRY_ENABLED=0`. Assuming you are starting a Flower server or "
+"client, simply do so by prepending your command as in:"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:24
+msgid ""
+"Alternatively, you can export `FLWR_TELEMETRY_ENABLED=0` in, for example,"
+" `.bashrc` (or whatever configuration file applies to your environment) "
+"to disable Flower telemetry permanently."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:26
+msgid "Collected metrics"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:28
+msgid "Flower telemetry collects the following metrics:"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:30
+msgid ""
+"**Flower version.** Understand which versions of Flower are currently "
+"being used. This helps us to decide whether we should invest effort into "
+"releasing a patch version for an older version of Flower or instead use "
+"the bandwidth to build new features."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:32
+msgid ""
+"**Operating system.** Enables us to answer questions such as: *Should we "
+"create more guides for Linux, macOS, or Windows?*"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:34
+msgid ""
+"**Python version.** Knowing the Python version helps us, for example, to "
+"decide whether we should invest effort into supporting old versions of "
+"Python or stop supporting them and start taking advantage of new Python "
+"features."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:36
+msgid ""
+"**Hardware properties.** Understanding the hardware environment that "
+"Flower is being used in helps to decide whether we should, for example, "
+"put more effort into supporting low-resource environments."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:38
+msgid ""
+"**Execution mode.** Knowing what execution mode Flower starts in enables "
+"us to understand how heavily certain features are being used and better "
+"prioritize based on that."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:40
+msgid ""
+"**Cluster.** Flower telemetry assigns a random in-memory cluster ID each "
+"time a Flower workload starts. This allows us to understand which device "
+"types not only start Flower workloads but also successfully complete "
+"them."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:42
+msgid ""
+"**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."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:44
+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 "
+"related to that source ID will then be permanently deleted."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:46
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:48
+msgid ""
+"If you think other metrics would be helpful for us to better guide our "
+"decisions, please let us know! We will carefully review them; if we are "
+"confident that they do not compromise user privacy, we may add them."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:50
+msgid "How to inspect what is being reported"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:52
+msgid ""
+"We wanted to make it very easy for you to inspect what anonymous usage "
+"metrics are reported. You can view all the reported telemetry information"
+" by setting the environment variable `FLWR_TELEMETRY_LOGGING=1`. Logging "
+"is disabled by default. You may use logging independently from "
+"`FLWR_TELEMETRY_ENABLED` so that you can inspect the telemetry feature "
+"without sending any metrics."
+msgstr ""
+
+#: ../../source/ref-telemetry.md:58
+msgid ""
+"The inspect Flower telemetry without sending any anonymous usage metrics,"
+" use both environment variables:"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:64
+msgid "How to contact us"
+msgstr ""
+
+#: ../../source/ref-telemetry.md:66
+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`)."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-android.rst:-1
+msgid ""
+"Read this Federated Learning quickstart tutorial for creating an Android "
+"app using Flower."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-android.rst:5
+msgid "Quickstart Android"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-android.rst:10
+msgid ""
+"Let's build a federated learning system using TFLite and Flower on "
+"Android!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-android.rst:12
+msgid ""
+"Please refer to the `full code example "
+"<https://github.com/adap/flower/tree/main/examples/android>`_ to learn "
+"more."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-fastai.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with FastAI to train a vision model on CIFAR-10."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-fastai.rst:5
+msgid "Quickstart fastai"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-fastai.rst:10
+msgid "Let's build a federated learning system using fastai and Flower!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-fastai.rst:12
+msgid ""
+"Please refer to the `full code example "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-fastai>`_ "
+"to learn more."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:-1
+msgid ""
+"Check out this Federating Learning quickstart tutorial for using Flower "
+"with HuggingFace Transformers in order to fine-tune an LLM."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:5
+msgid "Quickstart 🤗 Transformers"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:10
+msgid ""
+"Let's build a federated learning system using Hugging Face Transformers "
+"and Flower!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:12
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:18
+msgid "Dependencies"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:20
+msgid ""
+"To follow along this tutorial you will need to install the following "
+"packages: :code:`datasets`, :code:`evaluate`, :code:`flwr`, "
+":code:`torch`, and :code:`transformers`. This can be done using "
+":code:`pip`:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:30
+msgid "Standard Hugging Face workflow"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:33
+msgid "Handling the data"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:35
+msgid ""
+"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:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:81
+msgid "Training and testing the model"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:83
+msgid ""
+"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:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:121
+msgid "Creating the model itself"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:123
+msgid ""
+"To create the model itself, we will just load the pre-trained distillBERT"
+" model using Hugging Face’s :code:`AutoModelForSequenceClassification` :"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:136
+msgid "Federating the example"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:139
+msgid "Creating the IMDBClient"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:141
+msgid ""
+"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:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:169
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:175
+msgid "Starting the server"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:177
+msgid ""
+"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:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:205
+msgid ""
+"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)."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:209
+msgid "Putting everything together"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:211
+msgid "We can now start client instances using:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:221
+msgid ""
+"And they will be able to connect to the server and start the federated "
+"training."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:223
+msgid ""
+"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)."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:227
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:230
+msgid ""
+"Note that in this example we used :code:`PyTorch`, but we could have very"
+" well used :code:`TensorFlow`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:-1
+msgid ""
+"Read this Federated Learning quickstart tutorial for creating an iOS app "
+"using Flower to train a neural network on MNIST."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:5
+msgid "Quickstart iOS"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:10
+msgid ""
+"In this tutorial we will learn how to train a Neural Network on MNIST "
+"using Flower and CoreML on iOS devices."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:12
+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 "
+"client implementation in iOS, it is recommended to use Xcode as our IDE."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:15
+msgid ""
+"Our example consists of one Python *server* and two iPhone *clients* that"
+" all have the same model."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:17
+msgid ""
+"*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*."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:21
+msgid ""
+"Now that we have a rough idea of what is going on, let's get started to "
+"setup our Flower server environment. We first need to install Flower. You"
+" can do this by using pip:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:27
+msgid "Or Poetry:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:36
+msgid ""
+"Now that we have all our dependencies installed, let's run a simple "
+"distributed training using CoreML as our local training pipeline and "
+"MNIST as our dataset. For simplicity reasons we will use the complete "
+"Flower client with CoreML, that has been implemented and stored inside "
+"the Swift SDK. The client implementation can be seen below:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:72
+msgid ""
+"Let's create a new application project in Xcode and add :code:`flwr` as a"
+" dependency in your project. For our application, we will store the logic"
+" of our app in :code:`FLiOSModel.swift` and the UI elements in "
+":code:`ContentView.swift`. We will focus more on :code:`FLiOSModel.swift`"
+" in this quickstart. Please refer to the `full code example "
+"<https://github.com/adap/flower/tree/main/examples/ios>`_ to learn more "
+"about the app."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:75
+msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:83
+msgid ""
+"Then add the mlmodel to the project simply by drag-and-drop, the mlmodel "
+"will be bundled inside the application during deployment to your iOS "
+"device. We need to pass the url to access mlmodel and run CoreML machine "
+"learning processes, it can be retrieved by calling the function "
+":code:`Bundle.main.url`. For the MNIST dataset, we need to preprocess it "
+"into :code:`MLBatchProvider` object. The preprocessing is done inside "
+":code:`DataLoader.swift`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:99
+msgid ""
+"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`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:102
+msgid ""
+"After we have all of the necessary informations, let's create our Flower "
+"client."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:117
+msgid ""
+"Then start the Flower gRPC client and start communicating to the server "
+"by passing our Flower client to the function :code:`startFlwrGRPC`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:124
+msgid ""
+"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. This can be done by entering the hostname and port "
+"in the application before clicking the start button to start the "
+"federated learning process."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:131
+#: ../../source/tutorial-quickstart-mxnet.rst:226
+#: ../../source/tutorial-quickstart-pytorch.rst:205
+#: ../../source/tutorial-quickstart-tensorflow.rst:100
+msgid ""
+"For simple workloads we can start a Flower server and leave all the "
+"configuration possibilities at their default values. In a file named "
+":code:`server.py`, import Flower and start the server:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:142
+#: ../../source/tutorial-quickstart-mxnet.rst:237
+#: ../../source/tutorial-quickstart-pytorch.rst:216
+#: ../../source/tutorial-quickstart-scikitlearn.rst:215
+#: ../../source/tutorial-quickstart-tensorflow.rst:112
+msgid "Train the model, federated!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:144
+#: ../../source/tutorial-quickstart-pytorch.rst:218
+#: ../../source/tutorial-quickstart-tensorflow.rst:114
+#: ../../source/tutorial-quickstart-xgboost.rst:522
+msgid ""
+"With both client and server ready, we can now run everything and see "
+"federated learning in action. FL systems usually have a server and "
+"multiple clients. We therefore have to start the server first:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:152
+msgid ""
+"Once the server is running we can start the clients in different "
+"terminals. Build and run the client through your Xcode, one through Xcode"
+" Simulator and the other by deploying it to your iPhone. To see more "
+"about how to deploy your app to iPhone or Simulator visit `here "
+"<https://developer.apple.com/documentation/xcode/running-your-app-in-"
+"simulator-or-on-a-device>`_."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:156
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"learning system in your ios device. The full `source code "
+"<https://github.com/adap/flower/blob/main/examples/ios>`_ for this "
+"example can be found in :code:`examples/ios`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-jax.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with Jax to train a linear regression model on a scikit-learn dataset."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-jax.rst:5
+msgid "Quickstart JAX"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with MXNet to train a Sequential model on MNIST."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:5
+msgid "Quickstart MXNet"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:10
+msgid ""
+"In this tutorial, we will learn how to train a :code:`Sequential` model "
+"on MNIST using Flower and MXNet."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:12
+#: ../../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-"
+"setup.html>`_."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:16
+#: ../../source/tutorial-quickstart-scikitlearn.rst:16
+msgid ""
+"*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*."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:20
+#: ../../source/tutorial-quickstart-scikitlearn.rst:20
+msgid ""
+"Now that we have a rough idea of what is going on, let's get started. We "
+"first need to install Flower. You can do this by running:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:26
+msgid "Since we want to use MXNet, let's go ahead and install it:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:36
+msgid ""
+"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>`_."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:38
+msgid ""
+"In a file called :code:`client.py`, import Flower and MXNet related "
+"packages:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:53
+msgid "In addition, define the device allocation in MXNet with:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:59
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:73
+msgid ""
+"Define the training and loss with MXNet. We train the model by looping "
+"over the dataset, measure the corresponding loss, and optimize it."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:111
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:135
+msgid ""
+"After defining the training and testing of a MXNet machine learning "
+"model, we use these functions to implement a Flower client."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:137
+msgid "Our Flower clients will use a simple :code:`Sequential` model:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:156
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:158
+#: ../../source/tutorial-quickstart-pytorch.rst:144
+#: ../../source/tutorial-quickstart-tensorflow.rst:54
+msgid ""
+"The Flower server interacts with clients through an interface called "
+":code:`Client`. When the server selects a particular client for training,"
+" it sends training instructions over the network. The client receives "
+"those instructions and calls one of the :code:`Client` methods to run "
+"your code (i.e., to train the neural network we defined earlier)."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:164
+msgid ""
+"Flower provides a convenience class called :code:`NumPyClient` which "
+"makes it easier to implement the :code:`Client` interface when your "
+"workload uses MXNet. Implementing :code:`NumPyClient` usually means "
+"defining the following methods (:code:`set_parameters` is optional "
+"though):"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:170
+#: ../../source/tutorial-quickstart-pytorch.rst:156
+#: ../../source/tutorial-quickstart-scikitlearn.rst:109
+msgid "return the model weight as a list of NumPy ndarrays"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:171
+#: ../../source/tutorial-quickstart-pytorch.rst:157
+#: ../../source/tutorial-quickstart-scikitlearn.rst:111
+msgid ":code:`set_parameters` (optional)"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:172
+#: ../../source/tutorial-quickstart-pytorch.rst:158
+#: ../../source/tutorial-quickstart-scikitlearn.rst:111
+msgid ""
+"update the local model weights with the parameters received from the "
+"server"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:174
+#: ../../source/tutorial-quickstart-pytorch.rst:160
+#: ../../source/tutorial-quickstart-scikitlearn.rst:114
+msgid "set the local model weights"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:175
+#: ../../source/tutorial-quickstart-pytorch.rst:161
+#: ../../source/tutorial-quickstart-scikitlearn.rst:115
+msgid "train the local model"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:176
+#: ../../source/tutorial-quickstart-pytorch.rst:162
+#: ../../source/tutorial-quickstart-scikitlearn.rst:116
+msgid "receive the updated local model weights"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:178
+#: ../../source/tutorial-quickstart-pytorch.rst:164
+#: ../../source/tutorial-quickstart-scikitlearn.rst:118
+msgid "test the local model"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:180
+msgid "They can be implemented in the following way:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:210
+msgid ""
+"We can now create an instance of our class :code:`MNISTClient` and add "
+"one line to actually run this client:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:217
+#: ../../source/tutorial-quickstart-scikitlearn.rst:150
+msgid ""
+"That's it for the client. We only have to implement :code:`Client` or "
+":code:`NumPyClient` and call :code:`fl.client.start_client()` or "
+":code:`fl.client.start_numpy_client()`. The string "
+":code:`\"0.0.0.0:8080\"` tells the client which server to connect to. In "
+"our case we can run the server and the client on the same machine, "
+"therefore we use :code:`\"0.0.0.0:8080\"`. If we run a truly federated "
+"workload with the server and clients running on different machines, all "
+"that needs to change is the :code:`server_address` we pass to the client."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:239
+msgid ""
+"With both client and server ready, we can now run everything and see "
+"federated learning in action. Federated learning systems usually have a "
+"server and multiple clients. We therefore have to start the server first:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:247
+#: ../../source/tutorial-quickstart-pytorch.rst:226
+#: ../../source/tutorial-quickstart-scikitlearn.rst:224
+#: ../../source/tutorial-quickstart-tensorflow.rst:122
+#: ../../source/tutorial-quickstart-xgboost.rst:530
+msgid ""
+"Once the server is running we can start the clients in different "
+"terminals. Open a new terminal and start the first client:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:254
+#: ../../source/tutorial-quickstart-pytorch.rst:233
+#: ../../source/tutorial-quickstart-scikitlearn.rst:231
+#: ../../source/tutorial-quickstart-tensorflow.rst:129
+#: ../../source/tutorial-quickstart-xgboost.rst:537
+msgid "Open another terminal and start the second client:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:260
+#: ../../source/tutorial-quickstart-pytorch.rst:239
+#: ../../source/tutorial-quickstart-scikitlearn.rst:237
+#: ../../source/tutorial-quickstart-xgboost.rst:543
+msgid ""
+"Each client will have its own dataset. You should now see how the "
+"training does in the very first terminal (the one that started the "
+"server):"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:292
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"learning system. The full `source code "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"mxnet/client.py>`_ for this example can be found in :code:`examples"
+"/quickstart-mxnet`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pandas.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with Pandas to perform Federated Analytics."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pandas.rst:5
+msgid "Quickstart Pandas"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pandas.rst:10
+msgid "Let's build a federated analytics system using Pandas and Flower!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pandas.rst:12
+msgid ""
+"Please refer to the `full code example "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-pandas>`_ "
+"to learn more."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with PyTorch to train a CNN model on MNIST."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:13
+msgid ""
+"In this tutorial we will learn how to train a Convolutional Neural "
+"Network on CIFAR10 using Flower and PyTorch."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:15
+#: ../../source/tutorial-quickstart-xgboost.rst:36
+msgid ""
+"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>`_."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:29
+msgid ""
+"Since we want to use PyTorch to solve a computer vision task, let's go "
+"ahead and install PyTorch and the **torchvision** library:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:39
+msgid ""
+"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>`_."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:41
+msgid ""
+"In a file called :code:`client.py`, import Flower and PyTorch related "
+"packages:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:56
+msgid "In addition, we define the device allocation in PyTorch with:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:62
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:78
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:94
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:113
+msgid ""
+"After defining the training and testing of a PyTorch machine learning "
+"model, we use the functions for the Flower clients."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:115
+msgid ""
+"The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 "
+"Minute Blitz':"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:142
+msgid ""
+"After loading the data set with :code:`load_data()` we define the Flower "
+"interface."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:150
+msgid ""
+"Flower provides a convenience class called :code:`NumPyClient` which "
+"makes it easier to implement the :code:`Client` interface when your "
+"workload uses PyTorch. Implementing :code:`NumPyClient` usually means "
+"defining the following methods (:code:`set_parameters` is optional "
+"though):"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:166
+msgid "which can be implemented in the following way:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:189
+#: ../../source/tutorial-quickstart-tensorflow.rst:82
+msgid ""
+"We can now create an instance of our class :code:`CifarClient` and add "
+"one line to actually run this client:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:196
+#: ../../source/tutorial-quickstart-tensorflow.rst:90
+msgid ""
+"That's it for the client. We only have to implement :code:`Client` or "
+":code:`NumPyClient` and call :code:`fl.client.start_client()` or "
+":code:`fl.client.start_numpy_client()`. The string :code:`\"[::]:8080\"` "
+"tells the client which server to connect to. In our case we can run the "
+"server and the client on the same machine, therefore we use "
+":code:`\"[::]:8080\"`. If we run a truly federated workload with the "
+"server and clients running on different machines, all that needs to "
+"change is the :code:`server_address` we point the client at."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch.rst:271
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"learning system. The full `source code "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"pytorch/client.py>`_ for this example can be found in :code:`examples"
+"/quickstart-pytorch`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch-lightning.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with PyTorch Lightning to train an Auto Encoder model on MNIST."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch-lightning.rst:5
+msgid "Quickstart PyTorch Lightning"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch-lightning.rst:10
+msgid ""
+"Let's build a horizontal federated learning system using PyTorch "
+"Lightning and Flower!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12
+msgid ""
+"Please refer to the `full code example "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch-"
+"lightning>`_ to learn more."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with scikit-learn to train a linear regression model."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:5
+msgid "Quickstart scikit-learn"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:10
+msgid ""
+"In this tutorial, we will learn how to train a :code:`Logistic "
+"Regression` model on MNIST using Flower and scikit-learn."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:26
+msgid "Since we want to use scikt-learn, let's go ahead and install it:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:32
+msgid "Or simply install all dependencies using Poetry:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:42
+msgid ""
+"Now that we have all our dependencies installed, let's run a simple "
+"distributed training with two clients and one server. 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:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:45
+msgid ":code:`get_model_parameters()`"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:46
+msgid "Returns the paramters of a :code:`sklearn` LogisticRegression model"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:47
+msgid ":code:`set_model_params()`"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:48
+msgid "Sets the parameters of a :code:`sklean` LogisticRegression model"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:49
+msgid ":code:`set_initial_params()`"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:50
+msgid "Initializes the model parameters that the Flower server will ask for"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:51
+msgid ":code:`load_mnist()`"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:52
+msgid "Loads the MNIST dataset using OpenML"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:53
+msgid ":code:`shuffle()`"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:54
+msgid "Shuffles data and its label"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:56
+msgid ":code:`partition()`"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:56
+msgid "Splits datasets into a number of partitions"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:58
+msgid ""
+"Please check out :code:`utils.py` `here "
+"<https://github.com/adap/flower/blob/main/examples/sklearn-logreg-"
+"mnist/utils.py>`_ for more details. The pre-defined functions are used in"
+" the :code:`client.py` and imported. The :code:`client.py` also requires "
+"to import several packages such as Flower and scikit-learn:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:73
+msgid ""
+"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()`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:85
+msgid ""
+"Next, the logistic regression model is defined and initialized with "
+":code:`utils.set_initial_params()`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:97
+msgid ""
+"The Flower server interacts with clients through an interface called "
+":code:`Client`. When the server selects a particular client for training,"
+" it sends training instructions over the network. The client receives "
+"those instructions and calls one of the :code:`Client` methods to run "
+"your code (i.e., to fit the logistic regression we defined earlier)."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:103
+msgid ""
+"Flower provides a convenience class called :code:`NumPyClient` which "
+"makes it easier to implement the :code:`Client` interface when your "
+"workload uses scikit-learn. Implementing :code:`NumPyClient` usually "
+"means defining the following methods (:code:`set_parameters` is optional "
+"though):"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:112
+msgid "is directly imported with :code:`utils.set_model_params()`"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:120
+msgid "The methods can be implemented in the following way:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:143
+msgid ""
+"We can now create an instance of our class :code:`MnistClient` and add "
+"one line to actually run this client:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:159
+msgid ""
+"The following Flower server is a little bit more advanced and returns an "
+"evaluation function for the server-side evaluation. First, we import "
+"again all required libraries such as Flower and scikit-learn."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:162
+msgid ":code:`server.py`, import Flower and start the server:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:173
+msgid ""
+"The number of federated learning rounds is set in :code:`fit_round()` and"
+" the evaluation is defined in :code:`get_evaluate_fn()`. The evaluation "
+"function is called after each federated learning round and gives you "
+"information about loss and accuracy."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:198
+msgid ""
+"The :code:`main` contains the server-side parameter initialization "
+":code:`utils.set_initial_params()` as well as the aggregation strategy "
+":code:`fl.server.strategy:FedAvg()`. The strategy is the default one, "
+"federated averaging (or FedAvg), with two clients and evaluation after "
+"each federated learning round. The server can be started with the command"
+" :code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", "
+"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:217
+msgid ""
+"With both client and server ready, we can now run everything and see "
+"federated learning in action. Federated learning systems usually have a "
+"server and multiple clients. We, therefore, have to start the server "
+"first:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:271
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"learning system. The full `source code "
+"<https://github.com/adap/flower/tree/main/examples/sklearn-logreg-"
+"mnist>`_ for this example can be found in :code:`examples/sklearn-logreg-"
+"mnist`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with TensorFlow to train a MobilNetV2 model on CIFAR-10."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:5
+msgid "Quickstart TensorFlow"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:13
+msgid "Let's build a federated learning system in less than 20 lines of code!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:15
+msgid "Before Flower can be imported we have to install it:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:21
+msgid ""
+"Since we want to use the Keras API of TensorFlow (TF), we have to install"
+" TF as well:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:31
+msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:38
+msgid ""
+"We use the Keras utilities of TF to load CIFAR10, a popular colored image"
+" classification dataset for machine learning. The call to "
+":code:`tf.keras.datasets.cifar10.load_data()` downloads CIFAR10, caches "
+"it locally, and then returns the entire training and test set as NumPy "
+"ndarrays."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:47
+msgid ""
+"Next, we need a model. For the purpose of this tutorial, we use "
+"MobilNetV2 with 10 output classes:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:60
+msgid ""
+"Flower provides a convenience class called :code:`NumPyClient` which "
+"makes it easier to implement the :code:`Client` interface when your "
+"workload uses Keras. The :code:`NumPyClient` interface defines three "
+"methods which can be implemented in the following way:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:135
+msgid "Each client will have its own dataset."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:137
+msgid ""
+"You should now see how the training does in the very first terminal (the "
+"one that started the server):"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:169
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"learning system. The full `source code "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"tensorflow/client.py>`_ for this can be found in :code:`examples"
+"/quickstart-tensorflow/client.py`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with XGBoost to train classification models on trees."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:5
+msgid "Quickstart XGBoost"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:11
+msgid "Federated XGBoost"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:13
+msgid ""
+"EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient "
+"implementation of gradient-boosted decision tree (**GBDT**), that "
+"maximises the computational boundaries for boosted tree methods. It's "
+"primarily designed to enhance both the performance and computational "
+"speed of machine learning models. In XGBoost, trees are constructed "
+"concurrently, unlike the sequential approach taken by GBDT."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:17
+msgid ""
+"Often, for tabular data on medium-sized datasets with fewer than 10k "
+"training examples, XGBoost surpasses the results of deep learning "
+"techniques."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:20
+msgid "Why federated XGBoost?"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:22
+msgid ""
+"Indeed, as the demand for data privacy and decentralized learning grows, "
+"there's an increasing requirement to implement federated XGBoost systems "
+"for specialised applications, like survival analysis and financial fraud "
+"detection."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:24
+msgid ""
+"Federated learning ensures that raw data remains on the local device, "
+"making it an attractive approach for sensitive domains where data "
+"security and privacy are paramount. Given the robustness and efficiency "
+"of XGBoost, combining it with federated learning offers a promising "
+"solution for these specific challenges."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:27
+msgid ""
+"In this tutorial we will learn how to train a federated XGBoost model on "
+"HIGGS dataset using Flower and :code:`xgboost` package. We use a simple "
+"example (`full code xgboost-quickstart "
+"<https://github.com/adap/flower/tree/main/examples/xgboost-quickstart>`_)"
+" with two *clients* and one *server* to demonstrate how federated XGBoost"
+" works, and then we dive into a more complex example (`full code xgboost-"
+"comprehensive <https://github.com/adap/flower/tree/main/examples/xgboost-"
+"comprehensive>`_) to run various experiments."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:34
+msgid "Environment Setup"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:38
+msgid ""
+"We first need to install Flower and Flower Datasets. You can do this by "
+"running :"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:44
+msgid ""
+"Since we want to use :code:`xgboost` package to build up XGBoost trees, "
+"let's go ahead and install :code:`xgboost`:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:54
+msgid ""
+"*Clients* are responsible for generating individual weight-updates for "
+"the model based on their local datasets. Now that we have all our "
+"dependencies installed, let's run a simple distributed training with two "
+"clients and one server."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:57
+msgid ""
+"In a file called :code:`client.py`, import xgboost, Flower, Flower "
+"Datasets and other related functions:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:84
+msgid "Dataset partition and hyper-parameter selection"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:86
+msgid ""
+"Prior to local training, we require loading the HIGGS dataset from Flower"
+" Datasets and conduct data partitioning for FL:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:99
+msgid ""
+"In this example, we split the dataset into two partitions with uniform "
+"distribution (:code:`IidPartitioner(num_partitions=2)`). Then, we load "
+"the partition for the given client based on :code:`node_id`:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:118
+msgid ""
+"After that, we do train/test splitting on the given partition (client's "
+"local data), and transform data format for :code:`xgboost` package."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:131
+msgid ""
+"The functions of :code:`train_test_split` and "
+":code:`transform_dataset_to_dmatrix` are defined as below:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:155
+msgid "Finally, we define the hyper-parameters used for XGBoost training."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:171
+msgid ""
+"The :code:`num_local_round` represents the number of iterations for local"
+" tree boost. We use CPU for the training in default. One can shift it to "
+"GPU by setting :code:`tree_method` to :code:`gpu_hist`. We use AUC as "
+"evaluation metric."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:178
+msgid "Flower client definition for XGBoost"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:180
+msgid ""
+"After loading the dataset we define the Flower client. We follow the "
+"general rule to define :code:`XgbClient` class inherited from "
+":code:`fl.client.Client`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:190
+msgid ""
+"The :code:`self.bst` is used to keep the Booster objects that remain "
+"consistent across rounds, allowing them to store predictions from trees "
+"integrated in earlier rounds and maintain other essential data structures"
+" for training."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:193
+msgid ""
+"Then, we override :code:`get_parameters`, :code:`fit` and "
+":code:`evaluate` methods insides :code:`XgbClient` class as follows."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:207
+msgid ""
+"Unlike neural network training, XGBoost trees are not started from a "
+"specified random weights. In this case, we do not use "
+":code:`get_parameters` and :code:`set_parameters` to initialise model "
+"parameters for XGBoost. As a result, let's return an empty tensor in "
+":code:`get_parameters` when it is called by the server at the first "
+"round."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:248
+msgid ""
+"In :code:`fit`, at the first round, we call :code:`xgb.train()` to build "
+"up the first set of trees. the returned Booster object and config are "
+"stored in :code:`self.bst` and :code:`self.config`, respectively. From "
+"the second round, we load the global model sent from server to "
+":code:`self.bst`, and then update model weights on local training data "
+"with function :code:`local_boost` as follows:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:266
+msgid ""
+"Given :code:`num_local_round`, we update trees by calling "
+":code:`self.bst.update` method. After training, the last "
+":code:`N=num_local_round` trees will be extracted to send to the server."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:288
+msgid ""
+"In :code:`evaluate`, we call :code:`self.bst.eval_set` function to "
+"conduct evaluation on valid set. The AUC value will be returned."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:291
+msgid ""
+"Now, we can create an instance of our class :code:`XgbClient` and add one"
+" line to actually run this client:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:297
+msgid ""
+"That's it for the client. We only have to implement :code:`Client`and "
+"call :code:`fl.client.start_client()`. The string :code:`\"[::]:8080\"` "
+"tells the client which server to connect to. In our case we can run the "
+"server and the client on the same machine, therefore we use "
+":code:`\"[::]:8080\"`. If we run a truly federated workload with the "
+"server and clients running on different machines, all that needs to "
+"change is the :code:`server_address` we point the client at."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:308
+msgid ""
+"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* to finish a complete FL round."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:311
+msgid ""
+"In a file named :code:`server.py`, import Flower and FedXgbBagging from "
+":code:`flwr.server.strategy`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:313
+msgid "We first define a strategy for XGBoost bagging aggregation."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:336
+msgid ""
+"We use two clients for this example. An "
+":code:`evaluate_metrics_aggregation` function is defined to collect and "
+"wighted average the AUC values from clients."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:339
+msgid "Then, we start the server:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:351
+msgid "Tree-based bagging aggregation"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:353
+msgid ""
+"You must be curious about how bagging aggregation works. Let's look into "
+"the details."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:355
+msgid ""
+"In file :code:`flwr.server.strategy.fedxgb_bagging.py`, we define "
+":code:`FedXgbBagging` inherited from :code:`flwr.server.strategy.FedAvg`."
+" Then, we override the :code:`aggregate_fit`, :code:`aggregate_evaluate` "
+"and :code:`evaluate` methods as follows:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:451
+msgid ""
+"In :code:`aggregate_fit`, we sequentially aggregate the clients' XGBoost "
+"trees by calling :code:`aggregate()` function:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:510
+msgid ""
+"In this function, we first fetch the number of trees and the number of "
+"parallel trees for the current and previous model by calling "
+":code:`_get_tree_nums`. Then, the fetched information will be aggregated."
+" After that, the trees (containing model weights) are aggregated to "
+"generate a new tree model."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:515
+msgid ""
+"After traversal of all clients' models, a new global model is generated, "
+"followed by the serialisation, and sending back to each client."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:520
+msgid "Launch Federated XGBoost!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:582
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"XGBoost system. The AUC values can be checked in "
+":code:`metrics_distributed`. One can see that the average AUC increases "
+"over FL rounds."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:587
+msgid ""
+"The full `source code <https://github.com/adap/flower/blob/main/examples"
+"/xgboost-quickstart/>`_ for this example can be found in :code:`examples"
+"/xgboost-quickstart`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:591
+msgid "Comprehensive Federated XGBoost"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:593
+msgid ""
+"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 data partitioning and centralised/distributed "
+"evaluation. Let's take a look!"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:599
+msgid "Customised data partitioning"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:601
+msgid ""
+"In :code:`dataset.py`, we have a function :code:`instantiate_partitioner`"
+" to instantiate the data partitioner based on the given "
+":code:`num_partitions` and :code:`partitioner_type`. Currently, we "
+"provide four supported partitioner type to simulate the uniformity/non-"
+"uniformity in data quantity (uniform, linear, square, exponential)."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:632
+msgid "Customised centralised/distributed evaluation"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:634
+msgid ""
+"To facilitate centralised evaluation, we define a function in "
+":code:`server.py`:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:666
+msgid ""
+"This function returns a evaluation function which instantiates a "
+":code:`Booster` object and loads the global model weights to it. The "
+"evaluation is conducted by calling :code:`eval_set()` method, and the "
+"tested AUC value is reported."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:669
+msgid ""
+"As for distributed evaluation on the clients, it's same as the quick-"
+"start example by overriding the :code:`evaluate()` method insides the "
+":code:`XgbClient` class in :code:`client.py`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:673
+msgid "Arguments parser"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:675
+msgid ""
+"In :code:`utils.py`, we define the arguments parsers for clients and "
+"server, allowing users to specify different experimental settings. Let's "
+"first see the sever side:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:714
+msgid ""
+"This allows user to specify the number of total clients / FL rounds / "
+"participating clients / clients for evaluation, and evaluation fashion. "
+"Note that with :code:`--centralised-eval`, the sever will do centralised "
+"evaluation and all functionalities for client evaluation will be "
+"disabled."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:718
+msgid "Then, the argument parser on client side:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:760
+msgid ""
+"This defines various options for client data partitioning. Besides, "
+"clients also have a option to conduct evaluation on centralised test set "
+"by setting :code:`--centralised-eval`."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:764
+msgid "Example commands"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:766
+msgid ""
+"To run a centralised evaluated experiment on 5 clients with exponential "
+"distribution for 50 rounds, we first start the server as below:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:773
+msgid "Then, on each client terminal, we start the clients:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:779
+msgid ""
+"The full `source code <https://github.com/adap/flower/blob/main/examples"
+"/xgboost-comprehensive/>`_ for this comprehensive example can be found in"
+" :code:`examples/xgboost-comprehensive`."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:9
+msgid "Build a strategy from scratch"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:11
+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-"
+"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>`__)."
+msgstr ""
+
+#: ../../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 "
+"<https://pytorch.org/>`__)."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:15
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:16
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:15
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:15
+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 "
+"you in the ``#introductions`` channel! And if anything is unclear, head "
+"over to the ``#questions`` channel."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:17
+msgid "Let's build a new ``Strategy`` from scratch!"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:29
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:29
+msgid "Preparation"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:31
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:32
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:31
+msgid ""
+"Before we begin with the actual code, let's make sure that we have "
+"everything we need."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:43
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:44
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:43
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:43
+msgid "Installing dependencies"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:45
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:46
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:45
+msgid "First, we install the necessary packages:"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:65
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:66
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:65
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:65
+msgid ""
+"Now that we have all dependencies installed, we can import everything we "
+"need for this tutorial:"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:101
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:102
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:104
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:101
+msgid ""
+"It is possible to switch to a runtime that has GPU acceleration enabled "
+"(on Google Colab: ``Runtime > Change runtime type > Hardware acclerator: "
+"GPU > Save``). Note, however, that Google Colab is not always able to "
+"offer GPU acceleration. If you see an error related to GPU availability "
+"in one of the following sections, consider switching back to CPU-based "
+"execution by setting ``DEVICE = torch.device(\"cpu\")``. If the runtime "
+"has GPU acceleration enabled, you should see the output ``Training on "
+"cuda``, otherwise it'll say ``Training on cpu``."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:114
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:115
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:114
+msgid "Data loading"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:116
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:116
+msgid ""
+"Let's now load the CIFAR-10 training and test set, partition them into "
+"ten smaller datasets (each split into training and validation set), and "
+"wrap everything in their own ``DataLoader``. We introduce a new parameter"
+" ``num_clients`` which allows us to call ``load_datasets`` with different"
+" numbers of clients."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:167
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:168
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:167
+msgid "Model training/evaluation"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:169
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:170
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:169
+msgid ""
+"Let's continue with the usual model definition (including "
+"``set_parameters`` and ``get_parameters``), training and test functions:"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:258
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:258
+msgid "Flower client"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:260
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:260
+msgid ""
+"To implement the Flower client, we (again) create a subclass of "
+"``flwr.client.NumPyClient`` and implement the three methods "
+"``get_parameters``, ``fit``, and ``evaluate``. Here, we also pass the "
+"``cid`` to the client and use it log additional details:"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:308
+msgid "Let's test what we have so far before we continue:"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:339
+msgid "Build a Strategy from scratch"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:341
+msgid ""
+"Let’s overwrite the ``configure_fit`` method such that it passes a higher"
+" learning rate (potentially also other hyperparameters) to the optimizer "
+"of a fraction of the clients. We will keep the sampling of the clients as"
+" it is in ``FedAvg`` and then change the configuration dictionary (one of"
+" the ``FitIns`` attributes)."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:507
+msgid ""
+"The only thing left is to use the newly created custom Strategy "
+"``FedCustom`` when starting the experiment:"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:534
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:932
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:697
+msgid "Recap"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:536
+msgid ""
+"In this notebook, we’ve seen how to implement a custom strategy. A custom"
+" strategy enables granular control over client node configuration, result"
+" aggregation, and more. To define a custom strategy, you only have to "
+"overwrite the abstract methods of the (abstract) base class ``Strategy``."
+" To make custom strategies even more powerful, you can pass custom "
+"functions to the constructor of your new class (``__init__``) and then "
+"call these functions whenever needed."
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:550
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:948
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:749
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:715
+#: ../../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/>`__"
+msgstr ""
+
+#: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:552
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:950
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:751
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:717
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:371
+msgid ""
+"There's a dedicated ``#questions`` channel if you need help, but we'd "
+"also love to hear who you are in ``#introductions``!"
+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-"
+"pytorch.html>`__ introduces ``Client``, the flexible API underlying "
+"``NumPyClient``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:9
+msgid "Customize the client"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:11
+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"
+"/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>`__)."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:14
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:18
+msgid ""
+"Let's go deeper and see what it takes to move from ``NumPyClient`` to "
+"``Client``!"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:30
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:29
+msgid "Step 0: Preparation"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:117
+msgid ""
+"Let's now load the CIFAR-10 training and test set, partition them into "
+"ten smaller datasets (each split into training and validation set), and "
+"wrap everything in their own ``DataLoader``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:259
+msgid "Step 1: Revisiting NumPyClient"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:261
+msgid ""
+"So far, we've implemented our client by subclassing "
+"``flwr.client.NumPyClient``. The three methods we implemented are "
+"``get_parameters``, ``fit``, and ``evaluate``. Finally, we wrap the "
+"creation of instances of this class in a function called ``client_fn``:"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:309
+msgid ""
+"We've seen this before, there's nothing new so far. The only *tiny* "
+"difference compared to the previous notebook is naming, we've changed "
+"``FlowerClient`` to ``FlowerNumPyClient`` and ``client_fn`` to "
+"``numpyclient_fn``. Let's run it to see the output we get:"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:339
+msgid ""
+"This works as expected, two clients are training for three rounds of "
+"federated learning."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:341
+msgid ""
+"Let's dive a little bit deeper and discuss how Flower executes this "
+"simulation. Whenever a client is selected to do some work, "
+"``start_simulation`` calls the function ``numpyclient_fn`` to create an "
+"instance of our ``FlowerNumPyClient`` (along with loading the model and "
+"the data)."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:343
+msgid ""
+"But here's the perhaps surprising part: Flower doesn't actually use the "
+"``FlowerNumPyClient`` object directly. Instead, it wraps the object to "
+"makes it look like a subclass of ``flwr.client.Client``, not "
+"``flwr.client.NumPyClient``. In fact, the Flower core framework doesn't "
+"know how to handle ``NumPyClient``'s, it only knows how to handle "
+"``Client``'s. ``NumPyClient`` is just a convenience abstraction built on "
+"top of ``Client``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:345
+msgid ""
+"Instead of building on top of ``NumPyClient``, we can directly build on "
+"top of ``Client``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:357
+msgid "Step 2: Moving from ``NumPyClient`` to ``Client``"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:359
+msgid ""
+"Let's try to do the same thing using ``Client`` instead of "
+"``NumPyClient``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:465
+msgid ""
+"Before we discuss the code in more detail, let's try to run it! Gotta "
+"make sure our new ``Client``-based client works, right?"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:490
+msgid ""
+"That's it, we're now using ``Client``. It probably looks similar to what "
+"we've done with ``NumPyClient``. So what's the difference?"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:492
+msgid ""
+"First of all, it's more code. But why? The difference comes from the fact"
+" that ``Client`` expects us to take care of parameter serialization and "
+"deserialization. For Flower to be able to send parameters over the "
+"network, it eventually needs to turn these parameters into ``bytes``. "
+"Turning parameters (e.g., NumPy ``ndarray``'s) into raw bytes is called "
+"serialization. Turning raw bytes into something more useful (like NumPy "
+"``ndarray``'s) is called deserialization. Flower needs to do both: it "
+"needs to serialize parameters on the server-side and send them to the "
+"client, the client needs to deserialize them to use them for local "
+"training, and then serialize the updated parameters again to send them "
+"back to the server, which (finally!) deserializes them again in order to "
+"aggregate them with the updates received from other clients."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:495
+msgid ""
+"The only *real* difference between Client and NumPyClient is that "
+"NumPyClient takes care of serialization and deserialization for you. It "
+"can do so because it expects you to return parameters as NumPy ndarray's,"
+" and it knows how to handle these. This makes working with machine "
+"learning libraries that have good NumPy support (most of them) a breeze."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:497
+msgid ""
+"In terms of API, there's one major difference: all methods in Client take"
+" exactly one argument (e.g., ``FitIns`` in ``Client.fit``) and return "
+"exactly one value (e.g., ``FitRes`` in ``Client.fit``). The methods in "
+"``NumPyClient`` on the other hand have multiple arguments (e.g., "
+"``parameters`` and ``config`` in ``NumPyClient.fit``) and multiple return"
+" values (e.g., ``parameters``, ``num_example``, and ``metrics`` in "
+"``NumPyClient.fit``) if there are multiple things to handle. These "
+"``*Ins`` and ``*Res`` objects in ``Client`` wrap all the individual "
+"values you're used to from ``NumPyClient``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:510
+msgid "Step 3: Custom serialization"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:512
+msgid ""
+"Here we will explore how to implement custom serialization with a simple "
+"example."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:514
+msgid ""
+"But first what is serialization? Serialization is just the process of "
+"converting an object into raw bytes, and equally as important, "
+"deserialization is the process of converting raw bytes back into an "
+"object. This is very useful for network communication. Indeed, without "
+"serialization, you could not just a Python object through the internet."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:516
+msgid ""
+"Federated Learning relies heavily on internet communication for training "
+"by sending Python objects back and forth between the clients and the "
+"server. This means that serialization is an essential part of Federated "
+"Learning."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:518
+msgid ""
+"In the following section, we will write a basic example where instead of "
+"sending a serialized version of our ``ndarray``\\ s containing our "
+"parameters, we will first convert the ``ndarray`` into sparse matrices, "
+"before sending them. This technique can be used to save bandwidth, as in "
+"certain cases where the weights of a model are sparse (containing many 0 "
+"entries), converting them to a sparse matrix can greatly improve their "
+"bytesize."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:521
+msgid "Our custom serialization/deserialization functions"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:523
+msgid ""
+"This is where the real serialization/deserialization will happen, "
+"especially in ``ndarray_to_sparse_bytes`` for serialization and "
+"``sparse_bytes_to_ndarray`` for deserialization."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:525
+msgid ""
+"Note that we imported the ``scipy.sparse`` library in order to convert "
+"our arrays."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:613
+msgid "Client-side"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:615
+msgid ""
+"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``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:617
+msgid ""
+"Indeed, in ``get_parameters`` we need to serialize the parameters we got "
+"from our network using our custom ``ndarrays_to_sparse_parameters`` "
+"defined above."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:619
+msgid ""
+"In ``fit``, we first need to deserialize the parameters coming from the "
+"server using our custom ``sparse_parameters_to_ndarrays`` and then we "
+"need to serialize our local results with "
+"``ndarrays_to_sparse_parameters``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:621
+msgid ""
+"In ``evaluate``, we will only need to deserialize the global parameters "
+"with our custom function."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:725
+msgid "Server-side"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:727
+msgid ""
+"For this example, we will just use ``FedAvg`` as a strategy. To change "
+"the serialization and deserialization here, we only need to reimplement "
+"the ``evaluate`` and ``aggregate_fit`` functions of ``FedAvg``. The other"
+" functions of the strategy will be inherited from the super class "
+"``FedAvg``."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:729
+msgid "As you can see only one line as change in ``evaluate``:"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:735
+msgid ""
+"And for ``aggregate_fit``, we will first deserialize every result we "
+"received:"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:744
+msgid "And then serialize the aggregated result:"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:903
+msgid "We can now run our custom serialization example!"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:934
+msgid ""
+"In this part of the tutorial, we've seen how we can build clients by "
+"subclassing either ``NumPyClient`` or ``Client``. ``NumPyClient`` is a "
+"convenience abstraction that makes it easier to work with machine "
+"learning libraries that have good NumPy interoperability. ``Client`` is a"
+" more flexible abstraction that allows us to do things that are not "
+"possible in ``NumPyClient``. In order to do so, it requires us to handle "
+"parameter serialization and deserialization ourselves."
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:952
+msgid ""
+"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:"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:954
+msgid "`Read Flower Docs <https://flower.dev/docs/>`__"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:955
+msgid ""
+"`Check out Flower Code Examples "
+"<https://github.com/adap/flower/tree/main/examples>`__"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:956
+msgid ""
+"`Use Flower Baselines for your research "
+"<https://flower.dev/docs/baselines/>`__"
+msgstr ""
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:957
+msgid ""
+"`Watch Flower Summit 2023 videos <https://flower.dev/conf/flower-"
+"summit-2023/>`__"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:9
+msgid "Get started with Flower"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:11
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:11
+msgid "Welcome to the Flower federated learning tutorial!"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:13
+msgid ""
+"In this notebook, we'll build a federated learning system using Flower "
+"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."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:17
+msgid "Let's get stated!"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:31
+msgid ""
+"Before we begin with any actual code, let's make sure that we have "
+"everything we need."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:45
+msgid ""
+"Next, we install the necessary packages for PyTorch (``torch`` and "
+"``torchvision``) and Flower (``flwr``):"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:117
+msgid "Loading the data"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:119
+msgid ""
+"Federated learning can be applied to many different types of tasks across"
+" different domains. In this tutorial, we introduce federated learning by "
+"training a simple convolutional neural network (CNN) on the popular "
+"CIFAR-10 dataset. CIFAR-10 can be used to train image classifiers that "
+"distinguish between images from ten different classes:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:150
+msgid ""
+"We simulate having multiple datasets from multiple organizations (also "
+"called the \"cross-silo\" setting in federated learning) by splitting the"
+" original CIFAR-10 dataset into multiple partitions. Each partition will "
+"represent the data from a single organization. We're doing this purely "
+"for experimentation purposes, in the real world there's no need for data "
+"splitting because each organization already has their own data (so the "
+"data is naturally partitioned)."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:152
+msgid ""
+"Each organization will act as a client in the federated learning system. "
+"So having ten organizations participate in a federation means having ten "
+"clients connected to the federated learning server:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:172
+msgid ""
+"Let's now load the CIFAR-10 training and test set, partition them into "
+"ten smaller datasets (each split into training and validation set), and "
+"wrap the resulting partitions by creating a PyTorch ``DataLoader`` for "
+"each of them:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:222
+msgid ""
+"We now have a list of ten training sets and ten validation sets "
+"(``trainloaders`` and ``valloaders``) representing the data of ten "
+"different organizations. Each ``trainloader``/``valloader`` pair contains"
+" 4500 training examples and 500 validation examples. There's also a "
+"single ``testloader`` (we did not split the test set). Again, this is "
+"only necessary for building research or educational systems, actual "
+"federated learning systems have their data naturally distributed across "
+"multiple partitions."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:225
+msgid ""
+"Let's take a look at the first batch of images and labels in the first "
+"training set (i.e., ``trainloaders[0]``) before we move on:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:264
+msgid ""
+"The output above shows a random batch of images from the first "
+"``trainloader`` in our list of ten ``trainloaders``. It also prints the "
+"labels associated with each image (i.e., one of the ten possible labels "
+"we've seen above). If you run the cell again, you should see another "
+"batch of images."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:276
+msgid "Step 1: Centralized Training with PyTorch"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:287
+msgid ""
+"Next, we're going to use PyTorch to define a simple convolutional neural "
+"network. This introduction assumes basic familiarity with PyTorch, so it "
+"doesn't cover the PyTorch-related aspects in full detail. If you want to "
+"dive deeper into PyTorch, we recommend `DEEP LEARNING WITH PYTORCH: A 60 "
+"MINUTE BLITZ "
+"<https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html>`__."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:299
+msgid "Defining the model"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:301
+msgid ""
+"We use the simple CNN described in the `PyTorch tutorial "
+"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#define-a"
+"-convolutional-neural-network>`__:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:338
+msgid "Let's continue with the usual training and test functions:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:398
+msgid "Training the model"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:400
+msgid ""
+"We now have all the basic building blocks we need: a dataset, a model, a "
+"training function, and a test function. Let's put them together to train "
+"the model on the dataset of one of our organizations "
+"(``trainloaders[0]``). This simulates the reality of most machine "
+"learning projects today: each organization has their own data and trains "
+"models only on this internal data:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:430
+msgid ""
+"Training the simple CNN on our CIFAR-10 split for 5 epochs should result "
+"in a test set accuracy of about 41%, which is not good, but at the same "
+"time, it doesn't really matter for the purposes of this tutorial. The "
+"intent was just to show a simplistic centralized training pipeline that "
+"sets the stage for what comes next - federated learning!"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:442
+msgid "Step 2: Federated Learning with Flower"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:444
+msgid ""
+"Step 1 demonstrated a simple centralized training pipeline. All data was "
+"in one place (i.e., a single ``trainloader`` and a single ``valloader``)."
+" Next, we'll simulate a situation where we have multiple datasets in "
+"multiple organizations and where we train a model over these "
+"organizations using federated learning."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:456
+msgid "Updating model parameters"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:458
+msgid ""
+"In federated learning, the server sends the global model parameters to "
+"the client, and the client updates the local model with the parameters "
+"received from the server. It then trains the model on the local data "
+"(which changes the model parameters locally) and sends the "
+"updated/changed model parameters back to the server (or, alternatively, "
+"it sends just the gradients back to the server, not the full model "
+"parameters)."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:460
+msgid ""
+"We need two helper functions to update the local model with parameters "
+"received from the server and to get the updated model parameters from the"
+" local model: ``set_parameters`` and ``get_parameters``. The following "
+"two functions do just that for the PyTorch model above."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:462
+msgid ""
+"The details of how this works are not really important here (feel free to"
+" consult the PyTorch documentation if you want to learn more). In "
+"essence, we use ``state_dict`` to access PyTorch model parameter tensors."
+" The parameter tensors are then converted to/from a list of NumPy "
+"ndarray's (which Flower knows how to serialize/deserialize):"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:490
+msgid "Implementing a Flower client"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:492
+msgid ""
+"With that out of the way, let's move on to the interesting part. "
+"Federated learning systems consist of a server and multiple clients. In "
+"Flower, we create clients by implementing subclasses of "
+"``flwr.client.Client`` or ``flwr.client.NumPyClient``. We use "
+"``NumPyClient`` in this tutorial because it is easier to implement and "
+"requires us to write less boilerplate."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:494
+msgid ""
+"To implement the Flower client, we create a subclass of "
+"``flwr.client.NumPyClient`` and implement the three methods "
+"``get_parameters``, ``fit``, and ``evaluate``:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:496
+msgid "``get_parameters``: Return the current local model parameters"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:497
+msgid ""
+"``fit``: Receive model parameters from the server, train the model "
+"parameters on the local data, and return the (updated) model parameters "
+"to the server"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:498
+msgid ""
+"``evaluate``: Receive model parameters from the server, evaluate the "
+"model parameters on the local data, and return the evaluation result to "
+"the server"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:500
+msgid ""
+"We mentioned that our clients will use the previously defined PyTorch "
+"components for model training and evaluation. Let's see a simple Flower "
+"client implementation that brings everything together:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:537
+msgid ""
+"Our class ``FlowerClient`` defines how local training/evaluation will be "
+"performed and allows Flower to call the local training/evaluation through"
+" ``fit`` and ``evaluate``. Each instance of ``FlowerClient`` represents a"
+" *single client* in our federated learning system. Federated learning "
+"systems have multiple clients (otherwise, there's not much to federate), "
+"so each client will be represented by its own instance of "
+"``FlowerClient``. If we have, for example, three clients in our workload,"
+" then we'd have three instances of ``FlowerClient``. Flower calls "
+"``FlowerClient.fit`` on the respective instance when the server selects a"
+" particular client for training (and ``FlowerClient.evaluate`` for "
+"evaluation)."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:541
+msgid "Using the Virtual Client Engine"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:543
+msgid ""
+"In this notebook, we want to simulate a federated learning system with 10"
+" clients on a single machine. This means that the server and all 10 "
+"clients will live on a single machine and share resources such as CPU, "
+"GPU, and memory. Having 10 clients would mean having 10 instances of "
+"``FlowerClient`` in memory. Doing this on a single machine can quickly "
+"exhaust the available memory resources, even if only a subset of these "
+"clients participates in a single round of federated learning."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:545
+msgid ""
+"In addition to the regular capabilities where server and clients run on "
+"multiple machines, Flower, therefore, provides special simulation "
+"capabilities that create ``FlowerClient`` instances only when they are "
+"actually necessary for training or evaluation. To enable the Flower "
+"framework to create clients when necessary, we need to implement a "
+"function called ``client_fn`` that creates a ``FlowerClient`` instance on"
+" demand. Flower calls ``client_fn`` whenever it needs an instance of one "
+"particular client to call ``fit`` or ``evaluate`` (those instances are "
+"usually discarded after use, so they should not keep any local state). "
+"Clients are identified by a client ID, or short ``cid``. The ``cid`` can "
+"be used, for example, to load different local data partitions for "
+"different clients, as can be seen below:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:580
+msgid "Starting the training"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:582
+msgid ""
+"We now have the class ``FlowerClient`` which defines client-side "
+"training/evaluation and ``client_fn`` which allows Flower to create "
+"``FlowerClient`` instances whenever it needs to call ``fit`` or "
+"``evaluate`` on one particular client. The last step is to start the "
+"actual simulation using ``flwr.simulation.start_simulation``."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:584
+msgid ""
+"The function ``start_simulation`` accepts a number of arguments, amongst "
+"them the ``client_fn`` used to create ``FlowerClient`` instances, the "
+"number of clients to simulate (``num_clients``), the number of federated "
+"learning rounds (``num_rounds``), and the strategy. The strategy "
+"encapsulates the federated learning approach/algorithm, for example, "
+"*Federated Averaging* (FedAvg)."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:586
+msgid ""
+"Flower has 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 - "
+"starts the simulation:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:628
+msgid "Behind the scenes"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:630
+msgid "So how does this work? How does Flower execute this simulation?"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:632
+#, python-format
+msgid ""
+"When we call ``start_simulation``, we tell Flower that there are 10 "
+"clients (``num_clients=10``). Flower then goes ahead an asks the "
+"``FedAvg`` strategy to select clients. ``FedAvg`` knows that it should "
+"select 100% of the available clients (``fraction_fit=1.0``), so it goes "
+"ahead and selects 10 random clients (i.e., 100% of 10)."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:634
+msgid ""
+"Flower then asks the selected 10 clients to train the model. When the "
+"server receives the model parameter updates from the clients, it hands "
+"those updates over to the strategy (*FedAvg*) for aggregation. The "
+"strategy aggregates those updates and returns the new global model, which"
+" then gets used in the next round of federated learning."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:646
+msgid "Where's the accuracy?"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:648
+msgid ""
+"You may have noticed that all metrics except for ``losses_distributed`` "
+"are empty. Where did the ``{\"accuracy\": float(accuracy)}`` go?"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:650
+msgid ""
+"Flower can automatically aggregate losses returned by individual clients,"
+" but it cannot do the same for metrics in the generic metrics dictionary "
+"(the one with the ``accuracy`` key). Metrics dictionaries can contain "
+"very different kinds of metrics and even key/value pairs that are not "
+"metrics at all, so the framework does not (and can not) know how to "
+"handle these automatically."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:652
+msgid ""
+"As users, we need to tell the framework how to handle/aggregate these "
+"custom metrics, and we do so by passing metric aggregation functions to "
+"the strategy. The strategy will then call these functions whenever it "
+"receives fit or evaluate metrics from clients. The two possible functions"
+" are ``fit_metrics_aggregation_fn`` and "
+"``evaluate_metrics_aggregation_fn``."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:654
+msgid ""
+"Let's create a simple weighted averaging function to aggregate the "
+"``accuracy`` metric we return from ``evaluate``:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:680
+msgid ""
+"The only thing left to do is to tell the strategy to call this function "
+"whenever it receives evaluation metric dictionaries from the clients:"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:717
+msgid ""
+"We now have a full system that performs federated training and federated "
+"evaluation. It uses the ``weighted_average`` function to aggregate custom"
+" evaluation metrics and calculates a single ``accuracy`` metric across "
+"all clients on the server side."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:719
+msgid ""
+"The other two categories of metrics (``losses_centralized`` and "
+"``metrics_centralized``) are still empty because they only apply when "
+"centralized evaluation is being used. Part two of the Flower tutorial "
+"will cover centralized evaluation."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:731
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:351
+msgid "Final remarks"
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:733
+msgid ""
+"Congratulations, you just trained a convolutional neural network, "
+"federated over 10 clients! With that, you understand the basics of "
+"federated learning with Flower. The same approach you've seen can be used"
+" with other machine learning frameworks (not just PyTorch) and tasks (not"
+" just CIFAR-10 images classification), for example NLP with Hugging Face "
+"Transformers or speech with SpeechBrain."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:735
+msgid ""
+"In the next notebook, we're going to cover some more advanced concepts. "
+"Want to customize your strategy? Initialize parameters on the server "
+"side? Or evaluate the aggregated model on the server side? We'll cover "
+"all this and more in the next tutorial."
+msgstr ""
+
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:753
+msgid ""
+"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."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:9
+msgid "Use a federated learning strategy"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:11
+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-"
+"with-flower-pytorch.html>`__)."
+msgstr ""
+
+#: ../../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/>`__)."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:17
+msgid "Let's move beyond FedAvg with Flower strategies!"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:309
+msgid "Strategy customization"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:311
+msgid ""
+"So far, everything should look familiar if you've worked through the "
+"introductory notebook. With that, we're ready to introduce a number of "
+"new features."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:323
+msgid "Server-side parameter **initialization**"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:325
+msgid ""
+"Flower, by default, initializes the global model by asking one random "
+"client for the initial parameters. In many cases, we want more control "
+"over parameter initialization though. Flower therefore allows you to "
+"directly pass the initial parameters to the Strategy:"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:370
+msgid ""
+"Passing ``initial_parameters`` to the ``FedAvg`` strategy prevents Flower"
+" from asking one of the clients for the initial parameters. If we look "
+"closely, we can see that the logs do not show any calls to the "
+"``FlowerClient.get_parameters`` method."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:382
+msgid "Starting with a customized strategy"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:384
+msgid ""
+"We've seen the function ``start_simulation`` before. It accepts a number "
+"of arguments, amongst them the ``client_fn`` used to create "
+"``FlowerClient`` instances, the number of clients to simulate "
+"``num_clients``, the number of rounds ``num_rounds``, and the strategy."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:386
+msgid ""
+"The strategy encapsulates the federated learning approach/algorithm, for "
+"example, ``FedAvg`` or ``FedAdagrad``. Let's try to use a different "
+"strategy this time:"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:424
+msgid "Server-side parameter **evaluation**"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:426
+msgid ""
+"Flower can evaluate the aggregated model on the server-side or on the "
+"client-side. Client-side and server-side evaluation are similar in some "
+"ways, but different in others."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:428
+msgid ""
+"**Centralized Evaluation** (or *server-side evaluation*) is conceptually "
+"simple: it works the same way that evaluation in centralized machine "
+"learning does. If there is a server-side dataset that can be used for "
+"evaluation purposes, then that's great. We can evaluate the newly "
+"aggregated model after each round of training without having to send the "
+"model to clients. We're also fortunate in the sense that our entire "
+"evaluation dataset is available at all times."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:430
+msgid ""
+"**Federated Evaluation** (or *client-side evaluation*) is more complex, "
+"but also more powerful: it doesn't require a centralized dataset and "
+"allows us to evaluate models over a larger set of data, which often "
+"yields more realistic evaluation results. In fact, many scenarios require"
+" us to use **Federated Evaluation** if we want to get representative "
+"evaluation results at all. But this power comes at a cost: once we start "
+"to evaluate on the client side, we should be aware that our evaluation "
+"dataset can change over consecutive rounds of learning if those clients "
+"are not always available. Moreover, the dataset held by each client can "
+"also change over consecutive rounds. This can lead to evaluation results "
+"that are not stable, so even if we would not change the model, we'd see "
+"our evaluation results fluctuate over consecutive rounds."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:433
+msgid ""
+"We've seen how federated evaluation works on the client side (i.e., by "
+"implementing the ``evaluate`` method in ``FlowerClient``). Now let's see "
+"how we can evaluate aggregated model parameters on the server-side:"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:490
+msgid "Sending/receiving arbitrary values to/from clients"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:492
+msgid ""
+"In some situations, we want to configure client-side execution (training,"
+" evaluation) from the server-side. One example for that is the server "
+"asking the clients to train for a certain number of local epochs. Flower "
+"provides a way to send configuration values from the server to the "
+"clients using a dictionary. Let's look at an example where the clients "
+"receive values from the server through the ``config`` parameter in "
+"``fit`` (``config`` is also available in ``evaluate``). The ``fit`` "
+"method receives the configuration dictionary through the ``config`` "
+"parameter and can then read values from this dictionary. In this example,"
+" it reads ``server_round`` and ``local_epochs`` and uses those values to "
+"improve the logging and configure the number of local training epochs:"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:546
+msgid ""
+"So how can we send this config dictionary from server to clients? The "
+"built-in Flower Strategies provide way to do this, and it works similarly"
+" to the way server-side evaluation works. We provide a function to the "
+"strategy, and the strategy calls this function for every round of "
+"federated learning:"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:576
+msgid ""
+"Next, we'll just pass this function to the FedAvg strategy before "
+"starting the simulation:"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:613
+msgid ""
+"As we can see, the client logs now include the current round of federated"
+" learning (which they read from the ``config`` dictionary). We can also "
+"configure local training to run for one epoch during the first and second"
+" round of federated learning, and then for two epochs during the third "
+"round."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:615
+msgid ""
+"Clients can also return arbitrary values to the server. To do so, they "
+"return a dictionary from ``fit`` and/or ``evaluate``. We have seen and "
+"used this concept throughout this notebook without mentioning it "
+"explicitly: our ``FlowerClient`` returns a dictionary containing a custom"
+" key/value pair as the third return value in ``evaluate``."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:627
+msgid "Scaling federated learning"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:629
+msgid ""
+"As a last step in this notebook, let's see how we can use Flower to "
+"experiment with a large number of clients."
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:651
+#, python-format
+msgid ""
+"We now have 1000 partitions, each holding 45 training and 5 validation "
+"examples. Given that the number of training examples on each client is "
+"quite small, we should probably train the model a bit longer, so we "
+"configure the clients to perform 3 local training epochs. We should also "
+"adjust the fraction of clients selected for training during each round "
+"(we don't want all 1000 clients participating in every round), so we "
+"adjust ``fraction_fit`` to ``0.05``, which means that only 5% of "
+"available clients (so 50 clients) will be selected for training each "
+"round:"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:699
+msgid ""
+"In this notebook, we've seen how we can gradually enhance our system by "
+"customizing the strategy, initializing parameters on the server side, "
+"choosing a different strategy, and evaluating models on the server-side. "
+"That's quite a bit of flexibility with so little code, right?"
+msgstr ""
+
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:701
+msgid ""
+"In the later sections, we've seen how we can communicate arbitrary values"
+" between server and clients to fully customize client-side execution. "
+"With that capability, we built a large-scale Federated Learning "
+"simulation using the Flower Virtual Client Engine and ran an experiment "
+"involving 1000 clients in the same workload - all in a Jupyter Notebook!"
+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-"
+"scratch-pytorch.html>`__ shows how to build a fully custom ``Strategy`` "
+"from scratch."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:9
+msgid "What is Federated Learning?"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:13
+msgid ""
+"In this tutorial, you will learn what federated learning is, build your "
+"first system in Flower, and gradually extend it. If you work through all "
+"parts of the tutorial, you will be able to build advanced federated "
+"learning systems that approach the current state of the art in the field."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:15
+msgid ""
+"🧑‍🏫 This tutorial starts at zero and expects no familiarity with "
+"federated learning. Only a basic understanding of data science and Python"
+" programming is assumed."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:17
+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 "
+"hear from you in the ``#introductions`` channel! And if anything is "
+"unclear, head over to the ``#questions`` channel."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:19
+msgid "Let's get started!"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:31
+msgid "Classic machine learning"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:33
+msgid ""
+"Before we begin to discuss federated learning, let us quickly recap how "
+"most machine learning works today."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:35
+msgid ""
+"In machine learning, we have a model, and we have data. The model could "
+"be a neural network (as depicted here), or something else, like classical"
+" linear regression."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:41
+msgid "|e1dd4b4129b040bea23a894266227080|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:109
+msgid "Model and data"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:47
+msgid ""
+"We train the model using the data to perform a useful task. A task could "
+"be to detect objects in images, transcribe an audio recording, or play a "
+"game like Go."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:53
+msgid "|c0d4cc6a442948dca8da40d2440068d9|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:111
+msgid "Train model using data"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:59
+msgid ""
+"Now, in practice, the training data we work with doesn't originate on the"
+" machine we train the model on. It gets created somewhere else."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:61
+msgid ""
+"It originates on a smartphone by the user interacting with an app, a car "
+"collecting sensor data, a laptop receiving input via the keyboard, or a "
+"smart speaker listening to someone trying to sing a song."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:67
+msgid "|174e1e4fa1f149a19bfbc8bc1126f46a|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:113
+msgid "Data on a phone"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:73
+msgid ""
+"What's also important to mention, this \"somewhere else\" is usually not "
+"just one place, it's many places. It could be several devices all running"
+" the same app. But it could also be several organizations, all generating"
+" data for the same task."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:79
+msgid "|4e021a3dc08249d2a89daa3ab03c2714|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:115
+msgid "Data is on many devices"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:85
+msgid ""
+"So to use machine learning, or any kind of data analysis, the approach "
+"that has been used in the past was to collect all data on a central "
+"server. This server can be somewhere in a data center, or somewhere in "
+"the cloud."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:91
+msgid "|e74a1d5ce7eb49688651f2167a59065b|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:117
+msgid "Central data collection"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:97
+msgid ""
+"Once all the data is collected in one place, we can finally use machine "
+"learning algorithms to train our model on the data. This is the machine "
+"learning approach that we've basically always relied on."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:103
+msgid "|eb29ec4c7aef4e93976795ed72df647e|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:119
+msgid "Central model training"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:130
+msgid "Challenges of classical machine learning"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:132
+msgid ""
+"The classic machine learning approach we've just seen can be used in some"
+" cases. Great examples include categorizing holiday photos, or analyzing "
+"web traffic. Cases, where all the data is naturally available on a "
+"centralized server."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:138
+msgid "|c2f699d8ac484f5081721a6f1511f70d|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:173
+msgid "Centralized possible"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:144
+msgid ""
+"But the approach can not be used in many other cases. Cases, where the "
+"data is not available on a centralized server, or cases where the data "
+"available on one server is not enough to train a good model."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:150
+msgid "|cf42accdacbf4e5eb4fa0503108ba7a7|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:175
+msgid "Centralized impossible"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:156
+msgid ""
+"There are many reasons why the classic centralized machine learning "
+"approach does not work for a large number of highly important real-world "
+"use cases. Those reasons include:"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:158
+msgid ""
+"**Regulations**: GDPR (Europe), CCPA (California), PIPEDA (Canada), LGPD "
+"(Brazil), PDPL (Argentina), KVKK (Turkey), POPI (South Africa), FSS "
+"(Russia), CDPR (China), PDPB (India), PIPA (Korea), APPI (Japan), PDP "
+"(Indonesia), PDPA (Singapore), APP (Australia), and other regulations "
+"protect sensitive data from being moved. In fact, those regulations "
+"sometimes even prevent single organizations from combining their own "
+"users' data for artificial intelligence training because those users live"
+" in different parts of the world, and their data is governed by different"
+" data protection regulations."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:160
+msgid ""
+"**User preference**: In addition to regulation, there are use cases where"
+" users just expect that no data leaves their device, ever. If you type "
+"your passwords and credit card info into the digital keyboard of your "
+"phone, you don't expect those passwords to end up on the server of the "
+"company that developed that keyboard, do you? In fact, that use case was "
+"the reason federated learning was invented in the first place."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:161
+msgid ""
+"**Data volume**: Some sensors, like cameras, produce such a high data "
+"volume that it is neither feasible nor economic to collect all the data "
+"(due to, for example, bandwidth or communication efficiency). Think about"
+" a national rail service with hundreds of train stations across the "
+"country. If each of these train stations is outfitted with a number of "
+"security cameras, the volume of raw on-device data they produce requires "
+"incredibly powerful and exceedingly expensive infrastructure to process "
+"and store. And most of the data isn't even useful."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:164
+msgid "Examples where centralized machine learning does not work include:"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:166
+msgid ""
+"Sensitive healthcare records from multiple hospitals to train cancer "
+"detection models"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:167
+msgid ""
+"Financial information from different organizations to detect financial "
+"fraud"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:168
+msgid "Location data from your electric car to make better range prediction"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:169
+msgid "End-to-end encrypted messages to train better auto-complete models"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:171
+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 "
+"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."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:186
+msgid "Federated learning"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:188
+msgid ""
+"Federated learning simply reverses this approach. It enables machine "
+"learning on distributed data by moving the training to the data, instead "
+"of moving the data to the training. Here's the single-sentence "
+"explanation:"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:190
+msgid "Central machine learning: move the data to the computation"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:191
+msgid "Federated (machine) learning: move the computation to the data"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:193
+msgid ""
+"By doing so, it enables us to use machine learning (and other data "
+"science approaches) in areas where it wasn't possible before. We can now "
+"train excellent medical AI models by enabling different hospitals to work"
+" together. We can solve financial fraud by training AI models on the data"
+" of different financial institutions. We can build novel privacy-"
+"enhancing applications (such as secure messaging) that have better built-"
+"in AI than their non-privacy-enhancing alternatives. And those are just a"
+" few of the examples that come to mind. As we deploy federated learning, "
+"we discover more and more areas that can suddenly be reinvented because "
+"they now have access to vast amounts of previously inaccessible data."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:196
+msgid ""
+"So how does federated learning work, exactly? Let's start with an "
+"intuitive explanation."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:199
+msgid "Federated learning in five steps"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:202
+msgid "Step 0: Initialize global model"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:204
+msgid ""
+"We start by initializing the model on the server. This is exactly the "
+"same in classic centralized learning: we initialize the model parameters,"
+" either randomly or from a previously saved checkpoint."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:210
+msgid "|5ec8356bc2564fa09178b1ceed5beccc|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:307
+msgid "Initialize global model"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:217
+msgid ""
+"Step 1: Send model to a number of connected organizations/devices (client"
+" nodes)"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:219
+msgid ""
+"Next, we send the parameters of the global model to the connected client "
+"nodes (think: edge devices like smartphones or servers belonging to "
+"organizations). This is to ensure that each participating node starts "
+"their local training using the same model parameters. We often use only a"
+" few of the connected nodes instead of all nodes. The reason for this is "
+"that selecting more and more client nodes has diminishing returns."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:225
+msgid "|7c9329e97bd0430bad335ab605a897a7|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:309
+msgid "Send global model"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:232
+msgid ""
+"Step 2: Train model locally on the data of each organization/device "
+"(client node)"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:234
+msgid ""
+"Now that all (selected) client nodes have the latest version of the "
+"global model parameters, they start the local training. They use their "
+"own local dataset to train their own local model. They don't train the "
+"model until full convergence, but they only train for a little while. "
+"This could be as little as one epoch on the local data, or even just a "
+"few steps (mini-batches)."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:240
+msgid "|88002bbce1094ba1a83c9151df18f707|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:311
+msgid "Train on local data"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:247
+msgid "Step 3: Return model updates back to the server"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:249
+msgid ""
+"After local training, each client node has a slightly different version "
+"of the model parameters they originally received. The parameters are all "
+"different because each client node has different examples in its local "
+"dataset. The client nodes then send those model updates back to the "
+"server. The model updates they send can either be the full model "
+"parameters or just the gradients that were accumulated during local "
+"training."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:255
+msgid "|391766aee87c482c834c93f7c22225e2|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:313
+msgid "Send model updates"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:262
+msgid "Step 4: Aggregate model updates into a new global model"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:264
+msgid ""
+"The server receives model updates from the selected client nodes. If it "
+"selected 100 client nodes, it now has 100 slightly different versions of "
+"the original global model, each trained on the local data of one client. "
+"But didn't we want to have one model that contains the learnings from the"
+" data of all 100 client nodes?"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:266
+msgid ""
+"In order to get one single model, we have to combine all the model "
+"updates we received from the client nodes. This process is called "
+"*aggregation*, and there are many different ways to do it. The most basic"
+" way to do it is called *Federated Averaging* (`McMahan et al., 2016 "
+"<https://arxiv.org/abs/1602.05629>`__), often abbreviated as *FedAvg*. "
+"*FedAvg* takes the 100 model updates and, as the name suggests, averages "
+"them. To be more precise, it takes the *weighted average* of the model "
+"updates, weighted by the number of examples each client used for "
+"training. The weighting is important to make sure that each data example "
+"has the same \"influence\" on the resulting global model. If one client "
+"has 10 examples, and another client has 100 examples, then - without "
+"weighting - each of the 10 examples would influence the global model ten "
+"times as much as each of the 100 examples."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:273
+msgid "|93b9a15bd27f4e91b40f642c253dfaac|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:315
+msgid "Aggregate model updates"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:280
+msgid "Step 5: Repeat steps 1 to 4 until the model converges"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:282
+msgid ""
+"Steps 1 to 4 are what we call a single round of federated learning. The "
+"global model parameters get sent to the participating client nodes (step "
+"1), the client nodes train on their local data (step 2), they send their "
+"updated models to the server (step 3), and the server then aggregates the"
+" model updates to get a new version of the global model (step 4)."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:284
+msgid ""
+"During a single round, each client node that participates in that "
+"iteration only trains for a little while. This means that after the "
+"aggregation step (step 4), we have a model that has been trained on all "
+"the data of all participating client nodes, but only for a little while. "
+"We then have to repeat this training process over and over again to "
+"eventually arrive at a fully trained model that performs well across the "
+"data of all client nodes."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:289
+msgid ""
+"Congratulations, you now understand the basics of federated learning. "
+"There's a lot more to discuss, of course, but that was federated learning"
+" in a nutshell. In later parts of this tutorial, we will go into more "
+"detail. Interesting questions include: How can we select the best client "
+"nodes that should participate in the next round? What's the best way to "
+"aggregate model updates? How can we handle failing client nodes "
+"(stragglers)?"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:294
+msgid ""
+"Just like we can train a model on the decentralized data of different "
+"client nodes, we can also evaluate the model on that data to receive "
+"valuable metrics. This is called federated evaluation, sometimes "
+"abbreviated as FE. In fact, federated evaluation is an integral part of "
+"most federated learning systems."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:297
+msgid "Federated analytics"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:299
+msgid ""
+"In many cases, machine learning isn't necessary to derive value from "
+"data. Data analysis can yield valuable insights, but again, there's often"
+" not enough data to get a clear answer. What's the average age at which "
+"people develop a certain type of health condition? Federated analytics "
+"enables such queries over multiple client nodes. It is usually used in "
+"conjunction with other privacy-enhancing technologies like secure "
+"aggregation to prevent the server from seeing the results submitted by "
+"individual client nodes."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:303
+msgid "Differential Privacy"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:305
+msgid ""
+"Differential privacy (DP) is often mentioned in the context of Federated "
+"Learning. It is a privacy-preserving method used when analyzing and "
+"sharing statistical data, ensuring the privacy of individual "
+"participants. DP achieves this by adding statistical noise to the model "
+"updates, ensuring any individual participants’ information cannot be "
+"distinguished or re-identified. This technique can be considered an "
+"optimization that provides a quantifiable privacy protection measure."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:326
+msgid "Flower"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:328
+msgid ""
+"Federated learning, federated evaluation, and federated analytics require"
+" infrastructure to move machine learning models back and forth, train and"
+" evaluate them on local data, and then aggregate the updated models. "
+"Flower provides the infrastructure to do exactly that in an easy, "
+"scalable, and secure way. In short, Flower presents a unified approach to"
+" federated learning, analytics, and evaluation. It allows the user to "
+"federate any workload, any ML framework, and any programming language."
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:334
+msgid "|a23d9638f96342ef9d25209951e2d564|"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:340
+msgid ""
+"Flower federated learning server and client nodes (car, scooter, personal"
+" computer, roomba, and phone)"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:353
+msgid ""
+"Congratulations, you just learned the basics of federated learning and "
+"how it relates to the classic (centralized) machine learning!"
+msgstr ""
+
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:355
+msgid ""
+"In the next part of this tutorial, we are going to build a first "
+"federated learning system with Flower."
+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-"
+"pytorch.html>`__ shows how to build a simple federated learning system "
+"with PyTorch and Flower."
+msgstr ""
+
diff --git a/doc/locales/zh_Hans/LC_MESSAGES/sphinx.po b/doc/locales/zh_Hans/LC_MESSAGES/sphinx.po
new file mode 100644
index 000000000000..98f300b872e3
--- /dev/null
+++ b/doc/locales/zh_Hans/LC_MESSAGES/sphinx.po
@@ -0,0 +1,37 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2022 Flower Labs GmbH
+# This file is distributed under the same license as the Flower package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2023.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: Flower main\n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2023-11-23 18:31+0100\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language: zh_Hans\n"
+"Language-Team: zh_Hans <LL@li.org>\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"
+"Generated-By: Babel 2.13.1\n"
+
+#: ../../source/_templates/base.html:18
+msgid "About these documents"
+msgstr ""
+
+#: ../../source/_templates/base.html:21
+msgid "Index"
+msgstr ""
+
+#: ../../source/_templates/base.html:24
+msgid "Search"
+msgstr ""
+
+#: ../../source/_templates/base.html:27
+msgid "Copyright"
+msgstr ""
+