Merge branch 'main' into simulation_examples_with_flower_datasets

dev/update-examples.sh

@@ -22,9 +22,14 @@ cd examples/
 for d in $(printf '%s\n' */ | sort -V); do
   example=${d%/}
   # For each example, copy the README into the source of the Example docs
-  [[ $example = doc ]] || cp $example/README.md $ROOT/examples/doc/source/$example.md 2>&1 >/dev/null
+  [[ $example != doc ]] && cp $example/README.md $ROOT/examples/doc/source/$example.md 2>&1 >/dev/null
+  # For each example, copy all images of the _static folder into the examples
+  # docs static folder
+  [[ $example != doc ]] && [ -d "$example/_static" ] && {
+    cp $example/_static/**.{jpg,png,jpeg} $ROOT/examples/doc/source/_static/ 2>/dev/null || true
+  }
   # For each example, insert the name of the example into the index file
-  [[ $example = doc ]] || (echo $INSERT_LINE; echo a; echo $example; echo .; echo wq) | ed $INDEX 2>&1 >/dev/null
+  [[ $example != doc ]] && (echo $INSERT_LINE; echo a; echo $example; echo .; echo wq) | ed $INDEX 2>&1 >/dev/null
 done
 
 echo "\`\`\`" >> $INDEX

doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po

@@ -3,59 +3,60 @@
 # 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"
+"PO-Revision-Date: 2023-11-25 19:00+0000\n"
+"Last-Translator: Yan Gao <[email protected]>\n"
+"Language-Team: Chinese (Simplified) <https://hosted.weblate.org/projects/"
+"flower-docs/framework/zh_Hans/>\n"
 "Language: zh_Hans\n"
-"Language-Team: zh_Hans <[email protected]>\n"
-"Plural-Forms: nplurals=1; plural=0;\n"
 "MIME-Version: 1.0\n"
 "Content-Type: text/plain; charset=utf-8\n"
 "Content-Transfer-Encoding: 8bit\n"
+"Plural-Forms: nplurals=1; plural=0;\n"
+"X-Generator: Weblate 5.2.1-rc\n"
 "Generated-By: Babel 2.13.1\n"
 
 #: ../../source/contributor-explanation-architecture.rst:2
 msgid "Flower Architecture"
-msgstr ""
+msgstr "Flower的架构"
 
 #: ../../source/contributor-explanation-architecture.rst:5
 msgid "Edge Client Engine"
-msgstr ""
+msgstr "边缘客户端引擎"
 
 #: ../../source/contributor-explanation-architecture.rst:7
 msgid ""
 "`Flower <https://flower.dev>`_ core framework architecture with Edge "
 "Client Engine"
-msgstr ""
+msgstr "具有边缘客户端引擎的`Flower <https://flower.dev>`核心架构"
 
 #: ../../source/contributor-explanation-architecture.rst:13
 msgid "Virtual Client Engine"
-msgstr ""
+msgstr "虚拟客户端引擎"
 
 #: ../../source/contributor-explanation-architecture.rst:15
 msgid ""
 "`Flower <https://flower.dev>`_ core framework architecture with Virtual "
 "Client Engine"
-msgstr ""
+msgstr "具有虚拟客户端引擎的`Flower <https://flower.dev>`核心架构"
 
 #: ../../source/contributor-explanation-architecture.rst:21
 msgid "Virtual Client Engine and Edge Client Engine in the same workload"
-msgstr ""
+msgstr "可同步进行的虚拟客户端引擎和边缘客户端引擎"
 
 #: ../../source/contributor-explanation-architecture.rst:23
 msgid ""
 "`Flower <https://flower.dev>`_ core framework architecture with both "
 "Virtual Client Engine and Edge Client Engine"
-msgstr ""
+msgstr "具有虚拟客户端引擎和边缘客户端引擎的`Flower <https://flower.dev>`核心架构"
 
 #: ../../source/contributor-how-create-new-messages.rst:2
 msgid "Creating New Messages"
-msgstr ""
+msgstr "创建新信息"
 
 #: ../../source/contributor-how-create-new-messages.rst:4
 msgid ""
@@ -13605,4 +13606,3 @@ msgid ""
 "pytorch.html>`__ shows how to build a simple federated learning system "
 "with PyTorch and Flower."
 msgstr ""
-

doc/source/tutorial-quickstart-xgboost.rst

@@ -2,13 +2,16 @@
 
 
 Quickstart XGBoost
-==================
+=====================
 
 .. meta::
    :description: Check out this Federated Learning quickstart tutorial for using Flower with XGBoost to train classification models on trees.
 
+..  youtube:: AY1vpXUpesc
+   :width: 100%
+
 Federated XGBoost
--------------
+-------------------
 
 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.
@@ -17,7 +20,7 @@ In XGBoost, trees are constructed concurrently, unlike the sequential approach t
 Often, for tabular data on medium-sized datasets with fewer than 10k training examples, XGBoost surpasses the results of deep learning techniques.
 
 Why federated XGBoost?
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~
 
 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.
 
@@ -31,7 +34,7 @@ and then we dive into a more complex example (`full code xgboost-comprehensive <
 
 
 Environment Setup
--------------
+--------------------
 
 First of all, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_.
 
@@ -49,7 +52,7 @@ Since we want to use :code:`xgboost` package to build up XGBoost trees, let's go
 
 
 Flower Client
--------------
+-----------------
 
 *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.
@@ -81,7 +84,7 @@ In a file called :code:`client.py`, import xgboost, Flower, Flower Datasets and
     from flwr_datasets.partitioner import IidPartitioner
 
 Dataset partition and hyper-parameter selection
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Prior to local training, we require loading the HIGGS dataset from Flower Datasets and conduct data partitioning for FL:
 
@@ -175,7 +178,7 @@ We use AUC as evaluation metric.
 
 
 Flower client definition for XGBoost
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 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`.
@@ -303,7 +306,7 @@ clients running on different machines, all that needs to change is the
 
 
 Flower Server
--------------
+------------------
 
 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.
@@ -348,7 +351,7 @@ Then, we start the server:
     )
 
 Tree-based bagging aggregation
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 You must be curious about how bagging aggregation works. Let's look into the details.
 
@@ -517,7 +520,7 @@ followed by the serialisation, and sending back to each client.
 
 
 Launch Federated XGBoost!
----------------------------
+-------------------------------
 
 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
@@ -588,15 +591,15 @@ The full `source code <https://github.com/adap/flower/blob/main/examples/xgboost
 
 
 Comprehensive Federated XGBoost
----------------------------
+-----------------------------------
 
 Now that you have known how federated XGBoost work with Flower, it's time to run some more comprehensive experiments by customising the experimental settings.
 In the xgboost-comprehensive example (`full code <https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive>`_),
 we provide more options to define various experimental setups, including data partitioning and centralised/distributed evaluation.
 Let's take a look!
 
 Customised data partitioning
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 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`.
@@ -629,7 +632,7 @@ Currently, we provide four supported partitioner type to simulate the uniformity
 
 
 Customised centralised/distributed evaluation
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 To facilitate centralised evaluation, we define a function in :code:`server.py`:
 
@@ -670,7 +673,7 @@ As for distributed evaluation on the clients, it's same as the quick-start examp
 overriding the :code:`evaluate()` method insides the :code:`XgbClient` class in :code:`client.py`.
 
 Arguments parser
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~
 
 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:
@@ -761,7 +764,7 @@ 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`.
 
 Example commands
-~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~
 
 To run a centralised evaluated experiment on 5 clients with exponential distribution for 50 rounds,
 we first start the server as below:
@@ -776,4 +779,4 @@ Then, on each client terminal, we start the clients:
 
     $ python3 clients.py --num-partitions=5 --partitioner-type=exponential --node-id=NODE_ID
 
-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`.
+The full `code <https://github.com/adap/flower/blob/main/examples/xgboost-comprehensive/>`_ for this comprehensive example can be found in :code:`examples/xgboost-comprehensive`.

examples/doc/source/_static/.gitignore

@@ -0,0 +1,5 @@
+*
+!custom.css
+!favicon.ico
+!flower-logo.png
+!tmux_jtop_view.gif