diff --git a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
index 0263e9bf3224..50125eeb379f 100644
--- a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
@@ -8,7 +8,7 @@ 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: 2023-12-14 22:10+0000\n"
+"PO-Revision-Date: 2023-12-18 20:08+0000\n"
 "Last-Translator: Yan Gao <y.gaogy@gmail.com>\n"
 "Language-Team: Chinese (Simplified) <https://hosted.weblate.org/projects/"
 "flower-docs/framework/zh_Hans/>\n"
@@ -11210,42 +11210,53 @@ msgid ""
 "Expose Flower version through `flwr.__version__` "
 "([#952](https://github.com/adap/flower/pull/952))"
 msgstr ""
+"通过 `flwr.__version__` 公开 Flower 版本 ([#952](https://github.com/adap/"
+"flower/pull/952))"
 
 #: ../../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 ""
+"app.py \"中的 \"start_server \"现在会返回一个 \"History \"对象"
+"，其中包含训练中的指标（[#974](https://github.com/adap/flower/pull/974)）"
 
 #: ../../source/ref-changelog.md:649
 msgid ""
 "Make `max_workers` (used by `ThreadPoolExecutor`) configurable "
 "([#978](https://github.com/adap/flower/pull/978))"
 msgstr ""
+"使 \"max_workers\"（由 \"ThreadPoolExecutor \"使用"
+"）可配置（[#978](https://github.com/adap/flower/pull/978)）"
 
 #: ../../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 ""
+"在所有代码示例中，将服务器启动后的休眠时间延长至三秒（[#1086](https://github."
+"com/adap/flower/pull/1086)）"
 
 #: ../../source/ref-changelog.md:651
 msgid ""
 "Added a new FAQ section to the documentation "
 "([#948](https://github.com/adap/flower/pull/948))"
-msgstr ""
+msgstr "在文档中添加了新的常见问题部分 ([#948](https://github.com/adap/flower/pull/"
+"948))"
 
 #: ../../source/ref-changelog.md:652
 msgid ""
 "And many more under-the-hood changes, library updates, documentation "
 "changes, and tooling improvements!"
-msgstr ""
+msgstr "还有更多底层更改、库更新、文档更改和工具改进！"
 
 #: ../../source/ref-changelog.md:656
 msgid ""
 "**Removed** `flwr_example` **and** `flwr_experimental` **from release "
 "build** ([#869](https://github.com/adap/flower/pull/869))"
 msgstr ""
+"**从发布版中删除**`flwr_example`**和**`flwr_experimental`** "
+"([#869](https://github.com/adap/flower/pull/869))"
 
 #: ../../source/ref-changelog.md:658
 msgid ""
@@ -11255,10 +11266,14 @@ msgid ""
 "tensorflow`, `http-logger`, `ops`) are now no-op and will be removed in "
 "an upcoming release."
 msgstr ""
+"自 Flower 0.12.0 起，软件包 `flwr_example` 和 `flwr_experimental` 已被弃用，"
+"它们不再包含在 Flower 的发布版本中。相关的额外包（`baseline`, `examples-"
+"pytorch`, `examples-tensorflow`, `http-logger`, "
+"`ops`）现在已不再使用，并将在即将发布的版本中移除。"
 
 #: ../../source/ref-changelog.md:660
 msgid "v0.17.0 (2021-09-24)"
-msgstr ""
+msgstr "v0.17.0 (2021-09-24)"
 
 #: ../../source/ref-changelog.md:664
 msgid ""
@@ -11267,6 +11282,9 @@ msgid ""
 "[#790](https://github.com/adap/flower/pull/790) "
 "[#791](https://github.com/adap/flower/pull/791))"
 msgstr ""
+"**实验性虚拟客户端引擎** ([#781](https://github.com/adap/flower/pull/781) "
+"[#790](https://github.com/adap/flower/pull/790) [#791](https://github.com/"
+"adap/flower/pull/791))"
 
 #: ../../source/ref-changelog.md:666
 msgid ""
@@ -11277,6 +11295,11 @@ msgid ""
 "The easiest way to test the new functionality is to look at the two new "
 "code examples called `quickstart_simulation` and `simulation_pytorch`."
 msgstr ""
+"Flower 的目标之一是实现大规模研究。这一版本首次（试验性地）展示了代号为 "
+"\"虚拟客户端引擎 \"的重要新功能"
+"。虚拟客户端可以在单台机器或计算集群上对大量客户端进行仿真。"
+"测试新功能的最简单方法是查看名为 \"quickstart_simulation \"和 "
+"\"simulation_pytorch \"的两个新代码示例。"
 
 #: ../../source/ref-changelog.md:668
 msgid ""
@@ -11285,6 +11308,9 @@ msgid ""
 "known caveats. However, those who are curious are encouraged to try it "
 "out and share their thoughts."
 msgstr ""
+"该功能仍处于试验阶段，因此无法保证 API 的稳定性。此外，它还没有完全准备好进入"
+"黄金时间，并有一些已知的注意事项。不过，我们鼓励好奇的用户尝试使用并分享他们"
+"的想法。"
 
 #: ../../source/ref-changelog.md:670
 msgid ""
@@ -11292,78 +11318,95 @@ msgid ""
 "([#828](https://github.com/adap/flower/pull/828) "
 "[#822](https://github.com/adap/flower/pull/822))"
 msgstr ""
+"**新的内置策略**（[#828](https://github.com/adap/flower/pull/828) "
+"[#822](https://github.com/adap/flower/pull/822)"
 
 #: ../../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 ""
+msgstr "FedYogi - 在服务器端使用 Yogi 的联合学习策略。基于 https://arxiv.org/abs/"
+"2003.00295 实现"
 
 #: ../../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 ""
+msgstr "FedAdam - 在服务器端使用 Adam 的联邦学习策略。基于 https://arxiv.org/abs/"
+"2003.00295 实现"
 
 #: ../../source/ref-changelog.md:675
 msgid ""
 "**New PyTorch Lightning code example** "
 "([#617](https://github.com/adap/flower/pull/617))"
 msgstr ""
+"**新的 PyTorch Lightning 代码示例** ([#617](https://github.com/adap/flower/"
+"pull/617))"
 
 #: ../../source/ref-changelog.md:677
 msgid ""
 "**New Variational Auto-Encoder code example** "
 "([#752](https://github.com/adap/flower/pull/752))"
-msgstr ""
+msgstr "**新的变分自动编码器代码示例** ([#752](https://github.com/adap/flower/pull/"
+"752))"
 
 #: ../../source/ref-changelog.md:679
 msgid ""
 "**New scikit-learn code example** "
 "([#748](https://github.com/adap/flower/pull/748))"
 msgstr ""
+"**新的 scikit-learn 代码示例** ([#748](https://github.com/adap/flower/pull/"
+"748))"
 
 #: ../../source/ref-changelog.md:681
 msgid ""
 "**New experimental TensorBoard strategy** "
 "([#789](https://github.com/adap/flower/pull/789))"
 msgstr ""
+"**新的实验性 TensorBoard 策略**（[#789](https://github.com/adap/flower/pull/"
+"789)）"
 
 #: ../../source/ref-changelog.md:685
 msgid ""
 "Improved advanced TensorFlow code example "
 "([#769](https://github.com/adap/flower/pull/769))"
-msgstr ""
+msgstr "改进的高级 TensorFlow 代码示例（[#769](https://github.com/adap/flower/pull/"
+"769)"
 
 #: ../../source/ref-changelog.md:686
 msgid ""
 "Warning when `min_available_clients` is misconfigured "
 "([#830](https://github.com/adap/flower/pull/830))"
 msgstr ""
+"当 `min_available_clients` 配置错误时发出警告 ([#830](https://github.com/"
+"adap/flower/pull/830))"
 
 #: ../../source/ref-changelog.md:687
 msgid ""
 "Improved gRPC server docs "
 "([#841](https://github.com/adap/flower/pull/841))"
-msgstr ""
+msgstr "改进了 gRPC 服务器文档（[#841](https://github.com/adap/flower/pull/841)）"
 
 #: ../../source/ref-changelog.md:688
 msgid ""
 "Improved error message in `NumPyClient` "
 "([#851](https://github.com/adap/flower/pull/851))"
 msgstr ""
+"改进了 `NumPyClient` 中的错误信息 ([#851](https://github.com/adap/flower/"
+"pull/851))"
 
 #: ../../source/ref-changelog.md:689
 msgid ""
 "Improved PyTorch quickstart code example "
 "([#852](https://github.com/adap/flower/pull/852))"
-msgstr ""
+msgstr "改进的 PyTorch 快速启动代码示例 ([#852](https://github.com/adap/flower/pull/"
+"852))"
 
 #: ../../source/ref-changelog.md:693
 msgid ""
 "**Disabled final distributed evaluation** "
 "([#800](https://github.com/adap/flower/pull/800))"
-msgstr ""
+msgstr "**禁用最终分布式评价** ([#800](https://github.com/adap/flower/pull/800))"
 
 #: ../../source/ref-changelog.md:695
 msgid ""
@@ -11372,12 +11415,15 @@ msgid ""
 "server-side evaluation). The prior behaviour can be enabled by passing "
 "`force_final_distributed_eval=True` to `start_server`."
 msgstr ""
+"之前的行为是在所有连接的客户端上执行最后一轮分布式评估，而这通常是不需要的（"
+"例如，在使用服务器端评估时）。可以通过向 `start_server` 传递 "
+"`force_final_distributed_eval=True` 来启用之前的行为。"
 
 #: ../../source/ref-changelog.md:697
 msgid ""
 "**Renamed q-FedAvg strategy** "
 "([#802](https://github.com/adap/flower/pull/802))"
-msgstr ""
+msgstr "**更名为 q-FedAvg 策略** ([#802](https://github.com/adap/flower/pull/802))"
 
 #: ../../source/ref-changelog.md:699
 msgid ""
@@ -11387,6 +11433,10 @@ msgid ""
 "deprecated) `QffedAvg` class is still available for compatibility reasons"
 " (it will be removed in a future release)."
 msgstr ""
+"名为 `QffedAvg` 的策略已更名为 "
+"`QFedAvg`，以更好地反映原始论文中给出的符号（q-FFL 是优化目标，q-FedAvg "
+"是建议的求解器）。请注意，出于兼容性原因，原始（现已废弃）的 `QffedAvg` "
+"类仍然可用（它将在未来的版本中移除）。"
 
 #: ../../source/ref-changelog.md:701
 msgid ""
@@ -11394,6 +11444,8 @@ msgid ""
 "`simulation_pytorch_legacy` "
 "([#791](https://github.com/adap/flower/pull/791))"
 msgstr ""
+"**删除并重命名代码示例**`simulation_pytorch`**为**`simulation_pytorch_legacy`"
+" ([#791](https://github.com/adap/flower/pull/791))"
 
 #: ../../source/ref-changelog.md:703
 msgid ""
@@ -11403,30 +11455,35 @@ msgid ""
 " existing example was kept for reference purposes, but it might be "
 "removed in the future."
 msgstr ""
+"该示例已被新示例取代。新示例基于试验性虚拟客户端引擎，它将成为在 Flower 中进"
+"行大多数类型大规模模拟的新的默认方式。现有示例将作为参考保留，但将来可能会删"
+"除。"
 
 #: ../../source/ref-changelog.md:705
 msgid "v0.16.0 (2021-05-11)"
-msgstr ""
+msgstr "v0.16.0 (2021-05-11)"
 
 #: ../../source/ref-changelog.md:709
 msgid ""
 "**New built-in strategies** "
 "([#549](https://github.com/adap/flower/pull/549))"
-msgstr ""
+msgstr "**新的内置策略** ([#549](https://github.com/adap/flower/pull/549))"
 
 #: ../../source/ref-changelog.md:711
 msgid "(abstract) FedOpt"
-msgstr ""
+msgstr "（摘要） FedOpt"
 
 #: ../../source/ref-changelog.md:712
+#, fuzzy
 msgid "FedAdagrad"
-msgstr ""
+msgstr "FedAdagrad"
 
 #: ../../source/ref-changelog.md:714
 msgid ""
 "**Custom metrics for server and strategies** "
 "([#717](https://github.com/adap/flower/pull/717))"
-msgstr ""
+msgstr "**服务器和策略的自定义指标** ([#717](https://github.com/adap/flower/pull/"
+"717))"
 
 #: ../../source/ref-changelog.md:716
 msgid ""
@@ -11436,6 +11493,9 @@ msgid ""
 "dictionary containing custom metrics from client to server. As of this "
 "release, custom metrics replace task-specific metrics on the server."
 msgstr ""
+"Flower 服务器现在完全与任务无关，所有剩余的任务特定度量（如 \"准确度\""
+"）都已被自定义度量字典取代。Flower 0.15 引入了从客户端向服务器传递包含自定义"
+"指标的字典的功能。从本版本开始，自定义指标将取代服务器上的特定任务指标。"
 
 #: ../../source/ref-changelog.md:718
 msgid ""
@@ -11446,6 +11506,10 @@ msgid ""
 "even return *aggregated* metrics dictionaries for the server to keep "
 "track of."
 msgstr ""
+"自定义度量字典现在可在两个面向用户的 API 中使用：它们可从策略方法 "
+"`aggregate_fit`/`aggregate_evaluate` 返回，还可使传递给内置策略（通过 "
+"`eval_fn`）的评估函数返回两个以上的评估度量。策略甚至可以返回 *aggregated* "
+"指标字典，以便服务器跟踪。"
 
 #: ../../source/ref-changelog.md:720
 msgid ""
@@ -11454,18 +11518,22 @@ msgid ""
 "returning an empty `{}`), server-side evaluation functions should migrate"
 " from `return loss, accuracy` to `return loss, {\"accuracy\": accuracy}`."
 msgstr ""
+"Stratey 实现应将其 `aggregate_fit` 和 `aggregate_evaluate` "
+"方法迁移到新的返回类型（例如，只需返回空的 `{}`），服务器端评估函数应从 `"
+"return loss, accuracy` 迁移到 `return loss, {\"accuracy\": accuracy}`。"
 
 #: ../../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 ""
+msgstr "Flower 0.15 "
+"风格的返回类型已被弃用（但仍受支持），兼容性将在未来的版本中移除。"
 
 #: ../../source/ref-changelog.md:724
 msgid ""
 "**Migration warnings for deprecated functionality** "
 "([#690](https://github.com/adap/flower/pull/690))"
-msgstr ""
+msgstr "** 过时功能的迁移警告** ([#690](https://github.com/adap/flower/pull/690))"
 
 #: ../../source/ref-changelog.md:726
 msgid ""
@@ -11475,6 +11543,11 @@ msgid ""
 "new warning messages often provide details on how to migrate to more "
 "recent APIs, thus easing the transition from one release to another."
 msgstr ""
+"Flower "
+"早期版本通常会迁移到新的应用程序接口，同时保持与旧版应用程序接口的兼容。"
+"如果检测到使用了过时的 API，本版本将引入详细的警告信息。"
+"新的警告信息通常会详细说明如何迁移到更新的 "
+"API，从而简化从一个版本到另一个版本的过渡。"
 
 #: ../../source/ref-changelog.md:728
 msgid ""
@@ -11483,10 +11556,13 @@ msgid ""
 "[#692](https://github.com/adap/flower/pull/692) "
 "[#713](https://github.com/adap/flower/pull/713))"
 msgstr ""
+"改进了文档和文档说明 ([#691](https://github.com/adap/flower/pull/691) "
+"[#692](https://github.com/adap/flower/pull/692) [#713](https://github.com/"
+"adap/flower/pull/713))"
 
 #: ../../source/ref-changelog.md:730
 msgid "MXNet example and documentation"
-msgstr ""
+msgstr "MXNet 示例和文档"
 
 #: ../../source/ref-changelog.md:732
 msgid ""
@@ -11495,12 +11571,15 @@ msgid ""
 "[#702](https://github.com/adap/flower/pull/702) "
 "[#705](https://github.com/adap/flower/pull/705))"
 msgstr ""
+"PyTorch 示例中的 FedBN 实现： 从集中到联合 ([#696](https://github.com/adap/"
+"flower/pull/696) [#702](https://github.com/adap/flower/pull/702) "
+"[#705](https://github.com/adap/flower/pull/705))"
 
 #: ../../source/ref-changelog.md:736
 msgid ""
 "**Serialization-agnostic server** "
 "([#721](https://github.com/adap/flower/pull/721))"
-msgstr ""
+msgstr "**序列化无关服务器** ([#721](https://github.com/adap/flower/pull/721))"
 
 #: ../../source/ref-changelog.md:738
 msgid ""
@@ -11512,6 +11591,10 @@ msgid ""
 "these byte arrays should be interpreted (e.g., for "
 "serialization/deserialization)."
 msgstr ""
+"Flower 服务器现在完全不依赖序列化。之前使用的 `Weights` 类（以反序列化的 "
+"NumPy ndarrays 表示参数）已被 `Parameters` 类取代（例如在 `Strategy`中）。"
+"参数 \"对象与序列化完全无关，它以字节数组的形式表示参数，\"tensor_type "
+"\"属性表示如何解释这些字节数组（例如，用于序列化/反序列化）。"
 
 #: ../../source/ref-changelog.md:740
 msgid ""
@@ -11522,6 +11605,10 @@ msgid ""
 "[#721](https://github.com/adap/flower/pull/721) to see how strategies can"
 " easily migrate to the new format."
 msgstr ""
+"内置策略通过在内部处理序列化和反序列化到/从 \"权重 \"来实现这种方法"
+"。自定义/第三方策略实现应更新为稍有改动的策略方法定义。策略作者可查阅 PR "
+"[#721](https://github.com/adap/flower/pull/721) "
+"以了解如何将策略轻松迁移到新格式。"
 
 #: ../../source/ref-changelog.md:742
 msgid ""
@@ -11529,16 +11616,18 @@ msgid ""
 "`flwr.server.Server.evaluate_round` instead "
 "([#717](https://github.com/adap/flower/pull/717))"
 msgstr ""
+"已弃用 `flwr.server.Server.evaluate`，改用 `flwr.server.Server."
+"evaluate_round`（[#717](https://github.com/adap/flower/pull/717)"
 
 #: ../../source/ref-changelog.md:744
 msgid "v0.15.0 (2021-03-12)"
-msgstr ""
+msgstr "v0.15.0 (2021-03-12)"
 
 #: ../../source/ref-changelog.md:748
 msgid ""
 "**Server-side parameter initialization** "
 "([#658](https://github.com/adap/flower/pull/658))"
-msgstr ""
+msgstr "**服务器端参数初始化** ([#658](https://github.com/adap/flower/pull/658))"
 
 #: ../../source/ref-changelog.md:750
 msgid ""
@@ -11546,6 +11635,8 @@ msgid ""
 "parameter initialization works via a new `Strategy` method called "
 "`initialize_parameters`."
 msgstr ""
+"现在可以在服务器端初始化模型参数。服务器端参数初始化通过名为 "
+"\"initialize_parameters \"的新 \"Strategy \"方法进行。"
 
 #: ../../source/ref-changelog.md:752
 msgid ""
@@ -11554,6 +11645,8 @@ msgid ""
 "will provide these initial parameters to the server on startup and then "
 "delete them to free the memory afterwards."
 msgstr ""
+"内置策略支持名为 \"initial_parameters \"的新构造函数参数，用于设置初始参数。"
+"内置策略会在启动时向服务器提供这些初始参数，然后删除它们以释放内存。"
 
 #: ../../source/ref-changelog.md:771
 msgid ""
@@ -11561,21 +11654,24 @@ msgid ""
 "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 ""
+msgstr "如果没有向策略提供初始参数，服务器将继续使用当前行为（即向其中一个已连接的客"
+"户端询问参数，并将这些参数用作初始全局参数）。"
 
 #: ../../source/ref-changelog.md:773
 msgid "Deprecations"
-msgstr ""
+msgstr "停用"
 
 #: ../../source/ref-changelog.md:775
 msgid ""
 "Deprecate `flwr.server.strategy.DefaultStrategy` (migrate to "
 "`flwr.server.strategy.FedAvg`, which is equivalent)"
 msgstr ""
+"停用 `flwr.server.strategy.DefaultStrategy`（迁移到等价的 `flwr.server."
+"strategy.FedAvg`）"
 
 #: ../../source/ref-changelog.md:777
 msgid "v0.14.0 (2021-02-18)"
-msgstr ""
+msgstr "v0.14.0 (2021-02-18)"
 
 #: ../../source/ref-changelog.md:781
 msgid ""
@@ -11584,6 +11680,9 @@ msgid ""
 "[#572](https://github.com/adap/flower/pull/572) "
 "[#633](https://github.com/adap/flower/pull/633))"
 msgstr ""
+"**通用** `Client.fit` **和** `Client.evaluate` **返回值** "
+"([#610](https://github.com/adap/flower/pull/610) [#572](https://github.com/"
+"adap/flower/pull/572) [#633](https://github.com/adap/flower/pull/633))"
 
 #: ../../source/ref-changelog.md:783
 msgid ""
@@ -11592,6 +11691,9 @@ msgid ""
 "This means one can return almost arbitrary values from `fit`/`evaluate` "
 "and make use of them on the server side!"
 msgstr ""
+"客户端现在可以返回一个额外的字典，将 `str` 键映射为以下类型的值： "
+"bool`、`bytes`、`float`、`int`、`str`。这意味着我们可以从 `fit`/`evaluate` "
+"返回几乎任意的值，并在服务器端使用它们！"
 
 #: ../../source/ref-changelog.md:785
 msgid ""
@@ -11600,6 +11702,9 @@ msgid ""
 "dict)` representing the loss, number of examples, and a dictionary "
 "holding arbitrary problem-specific values like accuracy."
 msgstr ""
+"这一改进还使 `fit` 和 `evaluate` 之间的返回类型更加一致：`evaluate` "
+"现在应返回一个元组`(float, int, "
+"dict)`，代表损失、示例数和一个包含特定问题任意值（如准确度）的字典。"
 
 #: ../../source/ref-changelog.md:787
 msgid ""
@@ -11610,18 +11715,26 @@ msgid ""
 "`evaluate`: `float, int, Dict[str, Scalar]`). See the example below for "
 "details."
 msgstr ""
+"如果你想知道：此功能与现有项目兼容，额外的字典返回值是可选的。不过，新代码应"
+"迁移到新的返回类型，以便与即将发布的 Flower 版本兼容（`fit`: `List[np."
+"ndarray], int, Dict[str, Scalar]`，`evaluate`: `float, int, Dict[str, "
+"Scalar]`）。详见下面的示例。"
 
 #: ../../source/ref-changelog.md:789
 msgid ""
 "*Code example:* note the additional dictionary return values in both "
 "`FlwrClient.fit` and `FlwrClient.evaluate`:"
-msgstr ""
+msgstr "*代码示例：* 注意 `FlwrClient.fit` 和 `FlwrClient.evaluate` "
+"中的附加字典返回值："
 
 #: ../../source/ref-changelog.md:804
 msgid ""
 "**Generalized** `config` **argument in** `Client.fit` **and** "
 "`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))"
 msgstr ""
+"**在**`Client.fit` **和**`Client."
+"evaluate`中泛化**`config`参数（[#595](https://github.com/adap/flower/pull/"
+"595)）"
 
 #: ../../source/ref-changelog.md:806
 msgid ""
@@ -11630,6 +11743,9 @@ msgid ""
 "generalizes this to enable values of the following types: `bool`, "
 "`bytes`, `float`, `int`, `str`."
 msgstr ""
+"配置 \"参数曾是 \"字典[str, str]\"类型"
+"，这意味着字典值应是字符串。新版本将其扩展为以下类型的值： "
+"bool`、`bytes`、`float`、`int`、`str`。"
 
 #: ../../source/ref-changelog.md:808
 msgid ""
@@ -11637,50 +11753,55 @@ msgid ""
 "using the `config` dictionary. Yay, no more `str(epochs)` on the server-"
 "side and `int(config[\"epochs\"])` on the client side!"
 msgstr ""
+"这意味着现在可以使用 `config` 字典向 `fit`/`evaluate` 传递几乎任意的值。耶，"
+"服务器端不再需要 `str(epochs)`，客户端不再需要 `int(config[\"epochs\"])`！"
 
 #: ../../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 ""
+"*代码示例：* 注意 `config` 字典现在在 `Client.fit` 和 `Client.evaluate` "
+"中都包含非 `str` 值："
 
 #: ../../source/ref-changelog.md:827
 msgid "v0.13.0 (2021-01-08)"
-msgstr ""
+msgstr "v0.13.0 (2021-01-08)"
 
 #: ../../source/ref-changelog.md:831
 msgid ""
 "New example: PyTorch From Centralized To Federated "
 "([#549](https://github.com/adap/flower/pull/549))"
-msgstr ""
+msgstr "新示例： PyTorch 从集中到联合 ([#549](https://github.com/adap/flower/pull/"
+"549))"
 
 #: ../../source/ref-changelog.md:832
 msgid "Improved documentation"
-msgstr ""
+msgstr "改进文件"
 
 #: ../../source/ref-changelog.md:833
 msgid "New documentation theme ([#551](https://github.com/adap/flower/pull/551))"
-msgstr ""
+msgstr "新文档主题 ([#551](https://github.com/adap/flower/pull/551))"
 
 #: ../../source/ref-changelog.md:834
 msgid "New API reference ([#554](https://github.com/adap/flower/pull/554))"
-msgstr ""
+msgstr "新的 API 参考 ([#554](https://github.com/adap/flower/pull/554))"
 
 #: ../../source/ref-changelog.md:835
 msgid ""
 "Updated examples documentation "
 "([#549](https://github.com/adap/flower/pull/549))"
-msgstr ""
+msgstr "更新了示例文档 ([#549](https://github.com/adap/flower/pull/549))"
 
 #: ../../source/ref-changelog.md:836
 msgid ""
 "Removed obsolete documentation "
 "([#548](https://github.com/adap/flower/pull/548))"
-msgstr ""
+msgstr "删除了过时的文档 ([#548](https://github.com/adap/flower/pull/548))"
 
 #: ../../source/ref-changelog.md:838
 msgid "Bugfix:"
-msgstr ""
+msgstr "错误修正："
 
 #: ../../source/ref-changelog.md:840
 msgid ""
@@ -11689,20 +11810,24 @@ msgid ""
 "([#553](https://github.com/adap/flower/pull/553) "
 "[#540](https://github.com/adap/flower/issues/540))."
 msgstr ""
+"Server.fit \"完成后不会断开客户端连接，现在断开客户端连接是在 \"flwr."
+"server.start_server \"中处理的（[#553](https://github.com/adap/flower/pull/"
+"553) [#540](https://github.com/adap/flower/issues/540)）。"
 
 #: ../../source/ref-changelog.md:842
+#, fuzzy
 msgid "v0.12.0 (2020-12-07)"
-msgstr ""
+msgstr "v0.12.0 (2020-12-07)"
 
 #: ../../source/ref-changelog.md:844 ../../source/ref-changelog.md:860
 msgid "Important changes:"
-msgstr ""
+msgstr "重要变更："
 
 #: ../../source/ref-changelog.md:846
 msgid ""
 "Added an example for embedded devices "
 "([#507](https://github.com/adap/flower/pull/507))"
-msgstr ""
+msgstr "添加了嵌入式设备示例 ([#507](https://github.com/adap/flower/pull/507))"
 
 #: ../../source/ref-changelog.md:847
 msgid ""
@@ -11710,6 +11835,9 @@ msgid ""
 "([#504](https://github.com/adap/flower/pull/504) "
 "[#508](https://github.com/adap/flower/pull/508))"
 msgstr ""
+"添加了一个新的 NumPyClient（除现有的 KerasClient "
+"之外）（[#504](https://github.com/adap/flower/pull/504) [#508](https://github"
+".com/adap/flower/pull/508)"
 
 #: ../../source/ref-changelog.md:848
 msgid ""
@@ -11718,14 +11846,17 @@ msgid ""
 "([#494](https://github.com/adap/flower/pull/494) "
 "[#512](https://github.com/adap/flower/pull/512))"
 msgstr ""
+"弃用 `flwr_example` 软件包，并开始将示例迁移到顶层的 `examples` 目录 "
+"([#494](https://github.com/adap/flower/pull/494) [#512](https://github.com/"
+"adap/flower/pull/512))"
 
 #: ../../source/ref-changelog.md:850
 msgid "v0.11.0 (2020-11-30)"
-msgstr ""
+msgstr "v0.11.0 (2020-11-30)"
 
 #: ../../source/ref-changelog.md:852
 msgid "Incompatible changes:"
-msgstr ""
+msgstr "不兼容的更改："
 
 #: ../../source/ref-changelog.md:854
 msgid ""
@@ -11736,22 +11867,26 @@ msgid ""
 "which is why we're removing it from the four methods in Strategy. To "
 "migrate rename the following `Strategy` methods accordingly:"
 msgstr ""
+"重命名了策略方法（[#486](https://github.com/adap/flower/pull/486)），以统一 "
+"\"花朵 \"公共 API 的命名。其他公共方法/函数（例如 `Client` 中的每个方法，"
+"以及 `Strategy.evaluate`）不使用 `on_` 前缀，这就是我们从 Strategy "
+"中的四个方法中移除它的原因。迁移时，请相应地重命名以下 `Strategy` 方法："
 
 #: ../../source/ref-changelog.md:855
 msgid "`on_configure_evaluate` => `configure_evaluate`"
-msgstr ""
+msgstr "`on_configure_evaluate` => `configure_evaluate`"
 
 #: ../../source/ref-changelog.md:856
 msgid "`on_aggregate_evaluate` => `aggregate_evaluate`"
-msgstr ""
+msgstr "`on_aggregate_evaluate` => `aggregate_evaluate`"
 
 #: ../../source/ref-changelog.md:857
 msgid "`on_configure_fit` => `configure_fit`"
-msgstr ""
+msgstr "`on_configure_fit` => `configure_fit`"
 
 #: ../../source/ref-changelog.md:858
 msgid "`on_aggregate_fit` => `aggregate_fit`"
-msgstr ""
+msgstr "`on_aggregate_fit` => `aggregate_fit`"
 
 #: ../../source/ref-changelog.md:862
 msgid ""
@@ -11759,34 +11894,42 @@ msgid ""
 "([#479](https://github.com/adap/flower/pull/479)). To migrate use "
 "`FedAvg` instead."
 msgstr ""
+"已废弃的 `DefaultStrategy` ([#479](https://github.com/adap/flower/pull/479)) "
+"。迁移时请使用 `FedAvg`。"
 
 #: ../../source/ref-changelog.md:863
 msgid ""
 "Simplified examples and baselines "
 "([#484](https://github.com/adap/flower/pull/484))."
-msgstr ""
+msgstr "简化示例和基线（[#484](https://github.com/adap/flower/pull/484)）。"
 
 #: ../../source/ref-changelog.md:864
 msgid ""
 "Removed presently unused `on_conclude_round` from strategy interface "
 "([#483](https://github.com/adap/flower/pull/483))."
 msgstr ""
+"删除了策略界面中目前未使用的 \"on_conclude_round\"（[#483](https://github."
+"com/adap/flower/pull/483)）。"
 
 #: ../../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 ""
+"将最小 Python 版本设为 3.6.1，而不是 3.6.9 ([#471](https://github.com/adap/"
+"flower/pull/471))."
 
 #: ../../source/ref-changelog.md:866
 msgid ""
 "Improved `Strategy` docstrings "
 "([#470](https://github.com/adap/flower/pull/470))."
 msgstr ""
+"改进了 `Strategy` docstrings（[#470](https://github.com/adap/flower/pull/"
+"470)）。"
 
 #: ../../source/ref-example-projects.rst:2
 msgid "Example projects"
-msgstr ""
+msgstr "项目实例"
 
 #: ../../source/ref-example-projects.rst:4
 msgid ""
@@ -11796,6 +11939,9 @@ msgid ""
 "frameworks such as `PyTorch <https://pytorch.org/>`_ or `TensorFlow "
 "<https://www.tensorflow.org/>`_."
 msgstr ""
+"Flower 附带了许多使用示例。这些示例演示了如何使用 Flower "
+"联合不同类型的现有机器学习管道，通常是利用流行的机器学习框架，如 `PyTorch "
+"<https://pytorch.org/>`_ 或 `TensorFlow <https://www.tensorflow.org/>`_。"
 
 #: ../../source/ref-example-projects.rst:11
 msgid ""
@@ -11804,20 +11950,25 @@ msgid ""
 "to make them easier to use. All new examples are based in the directory "
 "`examples <https://github.com/adap/flower/tree/main/examples>`_."
 msgstr ""
+"Flower 的使用示例曾与 Flower 捆绑在一个名为 ``flwr_example`` "
+"的软件包中。我们正在将这些示例迁移到独立项目中，以使它们更易于使用。"
+"所有新示例都位于目录 `examples <https://github.com/adap/flower/tree/main/"
+"examples>`_。"
 
 #: ../../source/ref-example-projects.rst:16
 msgid "The following examples are available as standalone projects."
-msgstr ""
+msgstr "以下示例可作为独立项目使用。"
 
 #: ../../source/ref-example-projects.rst:20
 msgid "Quickstart TensorFlow/Keras"
-msgstr ""
+msgstr "快速入门 TensorFlow/Keras"
 
 #: ../../source/ref-example-projects.rst:22
 msgid ""
 "The TensorFlow/Keras quickstart example shows CIFAR-10 image "
 "classification with MobileNetV2:"
-msgstr ""
+msgstr "TensorFlow/Keras 快速入门示例展示了使用 MobileNetV2 进行的 CIFAR-10 "
+"图像分类："
 
 #: ../../source/ref-example-projects.rst:25
 msgid ""
@@ -11825,51 +11976,65 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/quickstart-"
 "tensorflow>`_"
 msgstr ""
+"`Quickstart TensorFlow (Code) <https://github.com/adap/flower/tree/main/"
+"examples/quickstart-tensorflow>`_"
 
 #: ../../source/ref-example-projects.rst:26
+#, fuzzy
 msgid ""
 "`Quickstart TensorFlow (Tutorial) <https://flower.dev/docs/framework"
 "/tutorial-quickstart-tensorflow.html>`_"
 msgstr ""
+"`Quickstart TensorFlow (Tutorial) <https://flower.dev/docs/framework/"
+"tutorial-quickstart-tensorflow.html>`_"
 
 #: ../../source/ref-example-projects.rst:27
+#, fuzzy
 msgid ""
 "`Quickstart TensorFlow (Blog Post) <https://flower.dev/blog/2020-12-11"
 "-federated-learning-in-less-than-20-lines-of-code>`_"
 msgstr ""
+"`Quickstart TensorFlow (Blog Post) <https://flower.dev/blog/2020-12-11"
+"-federated-learning-in-less-than-20-lines-of-code>`_"
 
 #: ../../source/ref-example-projects.rst:31
 #: ../../source/tutorial-quickstart-pytorch.rst:5
 msgid "Quickstart PyTorch"
-msgstr ""
+msgstr "快速入门 PyTorch"
 
 #: ../../source/ref-example-projects.rst:33
 msgid ""
 "The PyTorch quickstart example shows CIFAR-10 image classification with a"
 " simple Convolutional Neural Network:"
-msgstr ""
+msgstr "PyTorch 快速入门范例展示了使用简单卷积神经网络进行 CIFAR-10 图像分类的情况："
 
 #: ../../source/ref-example-projects.rst:36
+#, fuzzy
 msgid ""
 "`Quickstart PyTorch (Code) "
 "<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch>`_"
 msgstr ""
+"`Quickstart PyTorch (Code) <https://github.com/adap/flower/tree/main/"
+"examples/quickstart-pytorch>`_"
 
 #: ../../source/ref-example-projects.rst:37
+#, fuzzy
 msgid ""
 "`Quickstart PyTorch (Tutorial) <https://flower.dev/docs/framework"
 "/tutorial-quickstart-pytorch.html>`_"
 msgstr ""
+"`Quickstart PyTorch (Tutorial) <https://flower.dev/docs/framework/tutorial-"
+"quickstart-pytorch.html>`_"
 
 #: ../../source/ref-example-projects.rst:41
 msgid "PyTorch: From Centralized To Federated"
-msgstr ""
+msgstr "PyTorch： 从集中到联合"
 
 #: ../../source/ref-example-projects.rst:43
 msgid ""
 "This example shows how a regular PyTorch project can be federated using "
 "Flower:"
-msgstr ""
+msgstr "本例展示了如何使用 Flower 联合一个普通的 PyTorch 项目："
 
 #: ../../source/ref-example-projects.rst:45
 msgid ""
@@ -11877,6 +12042,8 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/pytorch-from-"
 "centralized-to-federated>`_"
 msgstr ""
+"PyTorch： 从集中到联合（代码） <https://github.com/adap/flower/tree/main/"
+"examples/pytorch-from-centralized-to-federated>`_"
 
 #: ../../source/ref-example-projects.rst:46
 msgid ""
@@ -11884,32 +12051,39 @@ msgid ""
 "<https://flower.dev/docs/framework/example-pytorch-from-centralized-to-"
 "federated.html>`_"
 msgstr ""
+"PyTorch： 从集中到联合（教程） <https://flower.dev/docs/framework/example-"
+"pytorch-from-centralized-to-federated.html>`_"
 
 #: ../../source/ref-example-projects.rst:50
 msgid "Federated Learning on Raspberry Pi and Nvidia Jetson"
-msgstr ""
+msgstr "树莓派和 Nvidia Jetson 上的联合学习"
 
 #: ../../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 ""
+msgstr "本示例展示了如何利用 Flower 建立一个跨 Raspberry Pi 和 Nvidia Jetson "
+"运行的联合学习系统："
 
 #: ../../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 ""
+"树莓派和 Nvidia Jetson 上的联合学习（代码） <https://github.com/adap/flower/"
+"tree/main/examples/embedded-devices>`_"
 
 #: ../../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 ""
+"树莓派和 Nvidia Jetson 上的联合学习（博文） <https://flower.dev/blog/2020-12-"
+"16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 
 #: ../../source/ref-example-projects.rst:60
 msgid "Legacy Examples (`flwr_example`)"
-msgstr ""
+msgstr "传统示例 (`flwr_example`)"
 
 #: ../../source/ref-example-projects.rst:63
 msgid ""
@@ -11917,10 +12091,12 @@ msgid ""
 "in the future. New examples are provided as standalone projects in "
 "`examples <https://github.com/adap/flower/tree/main/examples>`_."
 msgstr ""
+"在 `flwr_example` 中的使用示例已被弃用，今后将被移除。新示例将作为独立项目在 "
+"`examples <https://github.com/adap/flower/tree/main/examples>`_ 中提供。"
 
 #: ../../source/ref-example-projects.rst:69
 msgid "Extra Dependencies"
-msgstr ""
+msgstr "额外依赖"
 
 #: ../../source/ref-example-projects.rst:71
 msgid ""
@@ -11929,38 +12105,44 @@ msgid ""
 "frameworks, so additional dependencies need to be installed before an "
 "example can be run."
 msgstr ""
+"Flower 核心框架只保留了最低限度的依赖项。"
+"这些示例在不同机器学习框架的背景下演示了 "
+"Flower，因此在运行示例之前需要安装额外的依赖项。"
 
 #: ../../source/ref-example-projects.rst:75
 msgid "For PyTorch examples::"
-msgstr ""
+msgstr "PyTorch 示例：："
 
 #: ../../source/ref-example-projects.rst:79
 msgid "For TensorFlow examples::"
-msgstr ""
+msgstr "TensorFlow 示例：："
 
 #: ../../source/ref-example-projects.rst:83
 msgid "For both PyTorch and TensorFlow examples::"
-msgstr ""
+msgstr "PyTorch 和 TensorFlow 示例：："
 
 #: ../../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 ""
+"请参阅 :code:`pyproject.toml`，了解可能的 extras 的完整列表（章节 "
+":code:`[tool.poems.extras]`）。"
 
 #: ../../source/ref-example-projects.rst:92
 msgid "PyTorch Examples"
-msgstr ""
+msgstr "PyTorch 示例"
 
 #: ../../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 ""
+msgstr "我们的 PyTorch 示例基于 PyTorch 1."
+"7。它们应该也能在其他版本中使用。到目前为止，我们提供了以下示例。"
 
 #: ../../source/ref-example-projects.rst:98
 msgid "CIFAR-10 Image Classification"
-msgstr ""
+msgstr "CIFAR-10 图像分类"
 
 #: ../../source/ref-example-projects.rst:100
 msgid ""
@@ -11969,34 +12151,37 @@ msgid ""
 "to train a simple CNN classifier in a federated learning setup with two "
 "clients."
 msgstr ""
+"CIFAR-10 和 CIFAR-100 <https://www.cs.toronto.edu/~kriz/cifar.html>``_ "
+"是流行的 RGB 图像数据集。Flower CIFAR-10 示例使用 PyTorch "
+"在有两个客户端的联合学习设置中训练一个简单的 CNN 分类器。"
 
 #: ../../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 ""
+msgstr "首先，启动 Flower 服务器："
 
 #: ../../source/ref-example-projects.rst:106
 msgid "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh"
-msgstr ""
+msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh"
 
 #: ../../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 ""
+msgstr "然后，在新的终端窗口中启动两个客户端："
 
 #: ../../source/ref-example-projects.rst:110
 msgid "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh"
-msgstr ""
+msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh"
 
 #: ../../source/ref-example-projects.rst:112
 msgid "For more details, see :code:`src/py/flwr_example/pytorch_cifar`."
-msgstr ""
+msgstr "更多详情，请参阅 :code:`src/py/flwr_example/pytorch_cifar`。"
 
 #: ../../source/ref-example-projects.rst:115
 msgid "ImageNet-2012 Image Classification"
-msgstr ""
+msgstr "ImageNet-2012 图像分类"
 
 #: ../../source/ref-example-projects.rst:117
 msgid ""
@@ -12004,32 +12189,36 @@ msgid ""
 " vision datasets. The Flower ImageNet example uses PyTorch to train a "
 "ResNet-18 classifier in a federated learning setup with ten clients."
 msgstr ""
+"ImageNet-2012 <http://www.image-net.org/>`_ 是主要的计算机视觉数据集之一。"
+"Flower ImageNet 示例使用 PyTorch 在有十个客户端的联合学习设置中训练 ResNet-"
+"18 分类器。"
 
 #: ../../source/ref-example-projects.rst:123
 msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh"
-msgstr ""
+msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh"
 
 #: ../../source/ref-example-projects.rst:127
 msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh"
-msgstr ""
+msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh"
 
 #: ../../source/ref-example-projects.rst:129
 msgid "For more details, see :code:`src/py/flwr_example/pytorch_imagenet`."
-msgstr ""
+msgstr "更多详情，请参阅 :code:`src/py/flwr_example/pytorch_imagenet`。"
 
 #: ../../source/ref-example-projects.rst:133
 msgid "TensorFlow Examples"
-msgstr ""
+msgstr "TensorFlow 示例"
 
 #: ../../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 ""
+msgstr "我们的 TensorFlow 示例基于 TensorFlow 2.0 "
+"或更新版本。到目前为止，我们提供了以下示例。"
 
 #: ../../source/ref-example-projects.rst:139
 msgid "Fashion-MNIST Image Classification"
-msgstr ""
+msgstr "时尚-MNIST 图像分类"
 
 #: ../../source/ref-example-projects.rst:141
 msgid ""
@@ -12039,36 +12228,40 @@ msgid ""
 " Fashion-MNIST and trains a simple image classification model over those "
 "partitions."
 msgstr ""
+"`Fashion-MNIST <https://github.com/zalandoresearch/fashion-mnist>`_ "
+"经常被用作机器学习的 \"你好，世界！\"。我们遵循这一传统，提供了一个从时尚-"
+"MNIST 中随机抽样本地数据集的示例，并在这些分区上训练一个简单的图像分类模型。"
 
 #: ../../source/ref-example-projects.rst:148
 msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh"
-msgstr ""
+msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh"
 
 #: ../../source/ref-example-projects.rst:152
 msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh"
-msgstr ""
+msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh"
 
 #: ../../source/ref-example-projects.rst:154
 msgid ""
 "For more details, see "
 ":code:`src/py/flwr_example/tensorflow_fashion_mnist`."
-msgstr ""
+msgstr "更多详情，请参阅 :code:`src/py/flwr_example/tensorflow_fashion_mnist`。"
 
 #: ../../source/ref-faq.rst:4
 msgid ""
 "This page collects answers to commonly asked questions about Federated "
 "Learning with Flower."
-msgstr ""
+msgstr "本页收集了有关 \"Flower 联合学习 \"常见问题的答案。"
 
 #: ../../source/ref-faq.rst
 msgid ":fa:`eye,mr-1` Can Flower run on Juptyter Notebooks / Google Colab?"
-msgstr ""
+msgstr ":fa:`eye,mr-1` Flower 可以在 Juptyter Notebooks / Google Colab 上运行吗？"
 
 #: ../../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 ""
+msgstr "是的，它可以！Flower 甚至还进行了一些底层优化，使其在 Colab "
+"上运行得更好。下面是一个快速启动示例："
 
 #: ../../source/ref-faq.rst:10
 msgid ""
@@ -12076,6 +12269,8 @@ msgid ""
 "<https://colab.research.google.com/github/adap/flower/blob/main/examples"
 "/simulation-pytorch/sim.ipynb>`_"
 msgstr ""
+"`Flower 模拟 PyTorch <https://colab.research.google.com/github/adap/flower/"
+"blob/main/examples/simulation-pytorch/sim.ipynb>`_"
 
 #: ../../source/ref-faq.rst:11
 msgid ""
@@ -12083,10 +12278,12 @@ msgid ""
 "<https://colab.research.google.com/github/adap/flower/blob/main/examples"
 "/simulation-tensorflow/sim.ipynb>`_"
 msgstr ""
+"`Flower模拟TensorFlow/Keras <https://colab.research.google.com/github/adap/"
+"flower/blob/main/examples/simulation-tensorflow/sim.ipynb>`_"
 
 #: ../../source/ref-faq.rst
 msgid ":fa:`eye,mr-1` How can I run Federated Learning on a Raspberry Pi?"
-msgstr ""
+msgstr ":fa:`eye,mr-1` 如何在 Raspberry Pi 上运行联合学习？"
 
 #: ../../source/ref-faq.rst:15
 msgid ""
@@ -12095,10 +12292,14 @@ msgid ""
 " and the corresponding `GitHub code example "
 "<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_."
 msgstr ""
+"请点击此处查看有关嵌入式设备联合学习的 \"博文\"<https://flower.dev/blog/2020-"
+"12-16-running_federated_learning_applications_on_embedded_devices_with_flower"
+">`_和相应的 \"GitHub 代码示例\"<https://github.com/adap/flower/tree/main/"
+"examples/embedded-devices>`_。"
 
 #: ../../source/ref-faq.rst
 msgid ":fa:`eye,mr-1` Does Flower support federated learning on Android devices?"
-msgstr ""
+msgstr ":fa:`eye,mr-1` Flower 是否支持安卓设备上的联合学习？"
 
 #: ../../source/ref-faq.rst:19
 msgid ""
@@ -12106,44 +12307,55 @@ msgid ""
 "<https://flower.dev/blog/2021-12-15-federated-learning-on-android-"
 "devices-with-flower>`_ or check out the code examples:"
 msgstr ""
+"是的，确实如此。请查看我们的 \"博客文章 <https://flower.dev/blog/2021-12-15"
+"-federated-learning-on-android-devices-with-flower>`_\" 或查看代码示例："
 
 #: ../../source/ref-faq.rst:21
 msgid ""
 "`Android Kotlin example <https://flower.dev/docs/examples/android-"
 "kotlin.html>`_"
 msgstr ""
+"`Android Kotlin 示例 <https://flower.dev/docs/examples/android-kotlin.html>`_"
 
 #: ../../source/ref-faq.rst:22
 msgid "`Android Java example <https://flower.dev/docs/examples/android.html>`_"
-msgstr ""
+msgstr "Android Java 示例 <https://flower.dev/docs/examples/android.html>`_"
 
 #: ../../source/ref-faq.rst
 msgid ":fa:`eye,mr-1` Can I combine federated learning with blockchain?"
-msgstr ""
+msgstr ":fa:`eye,mr-1` 我可以将联合学习与区块链结合起来吗？"
 
 #: ../../source/ref-faq.rst:26
 msgid ""
 "Yes, of course. A list of available examples using Flower within a "
 "blockchain environment is available here:"
-msgstr ""
+msgstr "当然可以。有关在区块链环境中使用 Flower 的可用示例列表，请点击此处："
 
 #: ../../source/ref-faq.rst:28
 msgid ""
 "`Flower meets Nevermined GitHub Repository <https://github.com"
 "/nevermined-io/fl-demo/tree/master/image-classification-flower>`_."
 msgstr ""
+"`Flower meets Nevermined GitHub Repository <https://github.com/nevermined-io/"
+"fl-demo/tree/master/image-classification-flower>`_."
 
 #: ../../source/ref-faq.rst:29
+#, fuzzy
 msgid ""
 "`Flower meets Nevermined YouTube video "
 "<https://www.youtube.com/watch?v=A0A9hSlPhKI>`_."
 msgstr ""
+"`Flower meets Nevermined YouTube video <https://www.youtube.com/"
+"watch?v=A0A9hSlPhKI>`_."
 
 #: ../../source/ref-faq.rst:30
+#, fuzzy
 msgid ""
 "`Flower meets KOSMoS <https://www.kosmos-bmbf.de/wp-"
 "content/uploads/sites/13/2021/05/Talk-Flower-Summit-2021.pdf>`_."
 msgstr ""
+"`Flower meets KOSMoS <https://www.kosmos-bmbf.de/wp-content/uploads/sites/13/"
+"2021/05/Talk-Flower-Summit-2021.pdf>`_."
 
 #: ../../source/ref-faq.rst:31
 msgid ""
@@ -12151,16 +12363,22 @@ msgid ""
 "learning-same-mask-different-faces-imen-"
 "ayari/?trackingId=971oIlxLQ9%2BA9RB0IQ73XQ%3D%3D>`_ ."
 msgstr ""
+"\"Flower 与 Talan 的邂逅 \"博文 <https://www.linkedin.com/pulse/"
+"federated-learning-same-mask-different-faces-imen-ayari/"
+"?trackingId=971oIlxLQ9%2BA9RB0IQ73XQ%3D%3D>`_ 。"
 
 #: ../../source/ref-faq.rst:32
+#, fuzzy
 msgid ""
 "`Flower meets Talan GitHub Repository "
 "<https://gitlab.com/Talan_Innovation_Factory/food-waste-prevention>`_ ."
 msgstr ""
+"Flower 与 Talan 的 GitHub 代码库 <https://gitlab.com/"
+"Talan_Innovation_Factory/food-waste-prevention>`_ ."
 
 #: ../../source/ref-telemetry.md:1
 msgid "Telemetry"
-msgstr ""
+msgstr "遥测功能"
 
 #: ../../source/ref-telemetry.md:3
 msgid ""
@@ -12169,6 +12387,8 @@ msgid ""
 "Flower team to understand how Flower is used and what challenges users "
 "might face."
 msgstr ""
+"Flower 开源项目收集**匿名**使用指标，以便在充分知情的情况下做出改进 Flower "
+"的决定。这样做能让 Flower 团队了解 Flower 的使用情况以及用户可能面临的挑战。"
 
 #: ../../source/ref-telemetry.md:5
 msgid ""
@@ -12176,20 +12396,23 @@ msgid ""
 " Staying true to this statement, Flower makes it easy to disable "
 "telemetry for users that do not want to share anonymous usage metrics."
 msgstr ""
+"**Flower 是一个用于协作式人工智能和数据科学的友好框架。** Flower "
+"遵循这一声明，让不想分享匿名使用指标的用户可以轻松禁用遥测技术。"
 
 #: ../../source/ref-telemetry.md:7
 msgid "Principles"
-msgstr ""
+msgstr "原则"
 
 #: ../../source/ref-telemetry.md:9
 msgid "We follow strong principles guarding anonymous usage metrics collection:"
-msgstr ""
+msgstr "我们遵循严格的匿名使用指标收集原则："
 
 #: ../../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 ""
+msgstr "**可选：** 您始终可以禁用遥测功能；请继续阅读\"[如何退出](#how-to-opt-out)\""
+"。"
 
 #: ../../source/ref-telemetry.md:12
 msgid ""
@@ -12198,6 +12421,8 @@ msgid ""
 "metrics](#collected-metrics)” to understand what metrics are being "
 "reported."
 msgstr ""
+"**匿名：** 报告的使用指标是匿名的，不包含任何个人身份信息 (PII)。请参阅\""
+"[收集的指标](#collected-metrics) \"了解报告的指标。"
 
 #: ../../source/ref-telemetry.md:13
 msgid ""
@@ -12205,17 +12430,20 @@ msgid ""
 "reported; see the section “[How to inspect what is being reported](#how-"
 "to-inspect-what-is-being-reported)”"
 msgstr ""
+"**透明：** 您可以轻松查看正在报告的匿名指标；请参阅\"[如何查看正在报告的指标"
+"]（#how-to-inspect-what-is-being-reported）\"部分"
 
 #: ../../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 ""
+msgstr "**欢迎反馈：** 如果您有反馈意见，可以随时联系我们；详情请参见\"[如何联系我们"
+"](#how-to-contact-us) \"部分。"
 
 #: ../../source/ref-telemetry.md:16
 msgid "How to opt-out"
-msgstr ""
+msgstr "如何退订"
 
 #: ../../source/ref-telemetry.md:18
 msgid ""
@@ -12224,6 +12452,9 @@ msgid ""
 "`FLWR_TELEMETRY_ENABLED=0`. Assuming you are starting a Flower server or "
 "client, simply do so by prepending your command as in:"
 msgstr ""
+"Flower 启动时，会检查环境变量 `FLWR_TELEMETRY_ENABLED` 是否存在。通过设置 "
+"`FLWR_TELEMETRY_ENABLED=0` 可以轻松禁用遥测功能。假设你启动的是 Flower "
+"服务器或客户端，只需在命令前添加以下内容即可："
 
 #: ../../source/ref-telemetry.md:24
 msgid ""
@@ -12231,14 +12462,16 @@ msgid ""
 " `.bashrc` (or whatever configuration file applies to your environment) "
 "to disable Flower telemetry permanently."
 msgstr ""
+"或者，你也可以在 `.bashrc`（或任何适用于你的环境的配置文件）中导出 "
+"`FLWR_TELEMETRY_ENABLED=0` 来永久禁用 Flower telemetry。"
 
 #: ../../source/ref-telemetry.md:26
 msgid "Collected metrics"
-msgstr ""
+msgstr "收集的指标"
 
 #: ../../source/ref-telemetry.md:28
 msgid "Flower telemetry collects the following metrics:"
-msgstr ""
+msgstr "Flower 遥测技术收集以下指标："
 
 #: ../../source/ref-telemetry.md:30
 msgid ""
@@ -12246,13 +12479,15 @@ msgid ""
 "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 ""
+msgstr "**了解目前使用的 Flower 版本。这有助于我们决定是否应该投入精力为旧版本的 "
+"Flower 发布补丁版本，还是利用带宽来构建新功能。"
 
 #: ../../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 ""
+msgstr "**操作系统**使我们能够回答以下问题： *我们应该为 Linux、macOS 还是 Windows "
+"创建更多指南？*"
 
 #: ../../source/ref-telemetry.md:34
 msgid ""
@@ -12261,20 +12496,24 @@ msgid ""
 "Python or stop supporting them and start taking advantage of new Python "
 "features."
 msgstr ""
+"**例如，了解 Python 版本有助于我们决定是否应该投入精力支持旧版本的 Python，"
+"还是停止支持这些版本并开始利用新的 Python 功能。"
 
 #: ../../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 ""
+msgstr "**硬件属性** 了解 Flower "
+"的硬件使用环境，有助于决定我们是否应在支持低资源环境等方面投入更多精力。"
 
 #: ../../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 ""
+msgstr "** 执行模式** 了解 Flower "
+"的启动执行模式，能让我们了解某些功能的使用率，并据此更好地确定优先级。"
 
 #: ../../source/ref-telemetry.md:40
 msgid ""
@@ -12283,6 +12522,8 @@ msgid ""
 "types not only start Flower workloads but also successfully complete "
 "them."
 msgstr ""
+"**每次 Flower 工作负载启动时，Flower 遥测都会随机分配一个内存集群 ID。这样，"
+"我们就能了解哪些设备类型不仅启动了 Flower 工作负载，而且还成功完成了它们。"
 
 #: ../../source/ref-telemetry.md:42
 msgid ""
@@ -12295,6 +12536,10 @@ msgid ""
 "in order to reproduce the issue, multiple workloads must be started at "
 "the same time."
 msgstr ""
+"**Source.** Flower 遥测会在第一次生成遥测事件时，尝试在 `~/.flwr/source` "
+"中存储一个随机源 ID。源 ID 对于识别问题是否反复出现或问题是否由多个集群同时运"
+"行触发（这在模拟中经常发生）非常重要。例如，如果设备同时运行多个工作负载并导"
+"致问题，那么为了重现问题，必须同时启动多个工作负载。"
 
 #: ../../source/ref-telemetry.md:44
 msgid ""
@@ -12303,6 +12548,9 @@ msgid ""
 "request mentioning the source ID to `telemetry@flower.dev`. All events "
 "related to that source ID will then be permanently deleted."
 msgstr ""
+"您可以随时删除源 ID。如果您希望删除特定源 ID 下记录的所有事件，可以向 "
+"`telemetry@flower.dev` 发送删除请求，并提及该源 ID。届时，与该源 ID "
+"相关的所有事件都将被永久删除。"
 
 #: ../../source/ref-telemetry.md:46
 msgid ""
@@ -12312,17 +12560,21 @@ msgid ""
 "any changes to the metrics collected and publish changes in the "
 "changelog."
 msgstr ""
+"我们不会收集任何个人身份信息。如果您认为所收集的任何指标可能以任何方式被滥用"
+"，请[与我们联系]（#how-to-contact-us）。我们将更新本页面，以反映对所收集指标"
+"的任何更改，并在更新日志中公布更改内容。"
 
 #: ../../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 ""
+msgstr "如果您认为其他指标有助于我们更好地指导决策，请告诉我们！我们将仔细审查这些指"
+"标；如果我们确信它们不会损害用户隐私，我们可能会添加这些指标。"
 
 #: ../../source/ref-telemetry.md:50
 msgid "How to inspect what is being reported"
-msgstr ""
+msgstr "如何检查报告中的内容"
 
 #: ../../source/ref-telemetry.md:52
 msgid ""
@@ -12333,16 +12585,20 @@ msgid ""
 "`FLWR_TELEMETRY_ENABLED` so that you can inspect the telemetry feature "
 "without sending any metrics."
 msgstr ""
+"我们希望能让您轻松查看所报告的匿名使用指标。通过设置环境变量 "
+"`FLWR_TELEMETRY_LOGGING=1` 可以查看所有报告的遥测信息。日志记录默认为禁用。"
+"您可以不使用 `FLWR_TELEMETRY_ENABLED` "
+"而单独使用日志记录，这样就可以在不发送任何指标的情况下检查遥测功能。"
 
 #: ../../source/ref-telemetry.md:58
 msgid ""
 "The inspect Flower telemetry without sending any anonymous usage metrics,"
 " use both environment variables:"
-msgstr ""
+msgstr "在不发送任何匿名使用指标的情况下检查 Flower 遥测，可使用这两个环境变量："
 
 #: ../../source/ref-telemetry.md:64
 msgid "How to contact us"
-msgstr ""
+msgstr "如何联系我们"
 
 #: ../../source/ref-telemetry.md:66
 msgid ""
@@ -12351,22 +12607,26 @@ msgid ""
 "[Slack](https://flower.dev/join-slack/) (channel `#telemetry`) or email "
 "(`telemetry@flower.dev`)."
 msgstr ""
+"我们希望听到您的意见。如果您对如何改进我们处理匿名使用指标的方式有任何反馈或"
+"想法，请通过 [Slack](https://flower.dev/join-slack/) （频道 `#telemetry`）"
+"或电子邮件 (`telemetry@flower.dev`)与我们联系。"
 
 #: ../../source/tutorial-quickstart-android.rst:-1
 msgid ""
 "Read this Federated Learning quickstart tutorial for creating an Android "
 "app using Flower."
-msgstr ""
+msgstr "阅读本 Federated Learning 快速入门教程，了解如何使用 Flower 创建 Android "
+"应用程序。"
 
 #: ../../source/tutorial-quickstart-android.rst:5
 msgid "Quickstart Android"
-msgstr ""
+msgstr "快速入门 Android"
 
 #: ../../source/tutorial-quickstart-android.rst:10
 msgid ""
 "Let's build a federated learning system using TFLite and Flower on "
 "Android!"
-msgstr ""
+msgstr "让我们在 Android 上使用 TFLite 和 Flower 构建一个联合学习系统！"
 
 #: ../../source/tutorial-quickstart-android.rst:12
 msgid ""
@@ -12374,20 +12634,24 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/android>`_ to learn "
 "more."
 msgstr ""
+"请参阅 \"完整代码示例 <https://github.com/adap/flower/tree/main/examples/"
+"android>`_\" 了解更多信息。"
 
 #: ../../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 ""
+"查看此 Federated Learning 快速入门教程，了解如何使用 Flower 和 FastAI 在 "
+"CIFAR-10 上训练视觉模型。"
 
 #: ../../source/tutorial-quickstart-fastai.rst:5
 msgid "Quickstart fastai"
-msgstr ""
+msgstr "快速入门 fastai"
 
 #: ../../source/tutorial-quickstart-fastai.rst:10
 msgid "Let's build a federated learning system using fastai and Flower!"
-msgstr ""
+msgstr "让我们用 fastai 和 Flower 建立一个联合学习系统！"
 
 #: ../../source/tutorial-quickstart-fastai.rst:12
 msgid ""
@@ -12395,22 +12659,28 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/quickstart-fastai>`_ "
 "to learn more."
 msgstr ""
+"请参阅 \"完整代码示例 <https://github.com/adap/flower/tree/main/examples/"
+"quickstart-fastai>`_\" 了解更多信息。"
 
 #: ../../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 ""
+"查看此 Federating Learning 快速入门教程，了解如何使用 Flower 和 HuggingFace "
+"Transformers 来微调 LLM。"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:5
+#, fuzzy
 msgid "Quickstart 🤗 Transformers"
-msgstr ""
+msgstr "快速入门 🤗 变形金刚"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:10
+#, fuzzy
 msgid ""
 "Let's build a federated learning system using Hugging Face Transformers "
 "and Flower!"
-msgstr ""
+msgstr "让我们用 \"抱抱脸变形金刚 \"和 \"Flower \"建立一个联合学习系统！"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:12
 msgid ""
@@ -12420,10 +12690,14 @@ msgid ""
 " over a dataset of IMDB ratings. The end goal is to detect if a movie "
 "rating is positive or negative."
 msgstr ""
+"我们将利用 \"拥抱面孔 \"技术，使用 Flower "
+"在多个客户端上联合训练语言模型。更具体地说，我们将对预先训练好的 Transformer "
+"模型（distilBERT）进行微调，以便在 IMDB "
+"评分数据集上进行序列分类。最终目标是检测电影评分是正面还是负面。"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:18
 msgid "Dependencies"
-msgstr ""
+msgstr "依赖关系"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:20
 msgid ""
@@ -12432,14 +12706,18 @@ msgid ""
 ":code:`torch`, and :code:`transformers`. This can be done using "
 ":code:`pip`:"
 msgstr ""
+"要学习本教程，您需要安装以下软件包： :code:`datasets`、 :code:`evaluate`、 "
+":code:`flwr`、 :code:`torch`和 :code:`transformers`。这可以通过 :code:`pip` "
+"来完成："
 
 #: ../../source/tutorial-quickstart-huggingface.rst:30
+#, fuzzy
 msgid "Standard Hugging Face workflow"
-msgstr ""
+msgstr "标准 Hugging Face 工作流程"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:33
 msgid "Handling the data"
-msgstr ""
+msgstr "处理数据"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:35
 msgid ""
@@ -12447,10 +12725,13 @@ msgid ""
 "library. We then need to tokenize the data and create :code:`PyTorch` "
 "dataloaders, this is all done in the :code:`load_data` function:"
 msgstr ""
+"为了获取 IMDB 数据集，我们将使用 Hugging Face 的 :code:`datasets` "
+"库。然后，我们需要对数据进行标记化，并创建 :code:`PyTorch` 数据加载器，"
+"这些都将在 :code:`load_data` 函数中完成："
 
 #: ../../source/tutorial-quickstart-huggingface.rst:81
 msgid "Training and testing the model"
-msgstr ""
+msgstr "训练和测试模型"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:83
 msgid ""
@@ -12458,24 +12739,28 @@ msgid ""
 "take care of the training and testing. This is very similar to any "
 ":code:`PyTorch` training or testing loop:"
 msgstr ""
+"有了创建 trainloader 和 testloader 的方法后，我们就可以进行训练和测试了。"
+"这与任何 :code:`PyTorch` 训练或测试循环都非常相似："
 
 #: ../../source/tutorial-quickstart-huggingface.rst:121
 msgid "Creating the model itself"
-msgstr ""
+msgstr "创建模型本身"
 
 #: ../../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 ""
+"要创建模型本身，我们只需使用 Hugging Face 的 "
+":code:`AutoModelForSequenceClassification` 加载预训练的 distillBERT 模型："
 
 #: ../../source/tutorial-quickstart-huggingface.rst:136
 msgid "Federating the example"
-msgstr ""
+msgstr "将示例联合起来"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:139
 msgid "Creating the IMDBClient"
-msgstr ""
+msgstr "创建 IMDBClient"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:141
 msgid ""
@@ -12483,6 +12768,9 @@ msgid ""
 "Flower client class (inheriting from :code:`flwr.client.NumPyClient`). "
 "This is very easy, as our model is a standard :code:`PyTorch` model:"
 msgstr ""
+"要将我们的示例联合到多个客户端，我们首先需要编写 Flower 客户端类（继承自 "
+":code:`flwr.client.NumPyClient`）。这很容易，因为我们的模型是一个标准的 "
+":code:`PyTorch` 模型："
 
 #: ../../source/tutorial-quickstart-huggingface.rst:169
 msgid ""
@@ -12493,10 +12781,13 @@ msgid ""
 ":code:`evaluate` function tests the model locally and returns the "
 "relevant metrics."
 msgstr ""
+":code:`get_parameters` 函数允许服务器获取客户端的参数。相反，:code:`set_param"
+"eters`函数允许服务器将其参数发送给客户端。最后，:code:`fit`函数在本地为客户端"
+"训练模型，:code:`evaluate`函数在本地测试模型并返回相关指标。"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:175
 msgid "Starting the server"
-msgstr ""
+msgstr "启动服务器"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:177
 msgid ""
@@ -12507,6 +12798,10 @@ msgid ""
 "all the clients' weights at each round) and then using the "
 ":code:`flwr.server.start_server` function:"
 msgstr ""
+"现在我们有了实例化客户端的方法，我们需要创建服务器，以便汇总结果。使用 "
+"Flower，首先选择一个策略（这里我们使用 "
+":code:`FedAvg`，它将把全局权重定义为每轮所有客户端权重的平均值），然后使用 "
+":code:`flwr.server.start_server`函数，就可以非常轻松地完成这项工作："
 
 #: ../../source/tutorial-quickstart-huggingface.rst:205
 msgid ""
@@ -12514,20 +12809,22 @@ msgid ""
 "aggregate the metrics distributed amongst the clients (basically this "
 "allows us to display a nice average accuracy and loss for every round)."
 msgstr ""
+"使用 :code:`weighted_average` 函数是为了提供一种方法来汇总分布在客户端的指标"
+"（基本上，这可以让我们显示每一轮的平均精度和损失）。"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:209
 msgid "Putting everything together"
-msgstr ""
+msgstr "把所有东西放在一起"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:211
 msgid "We can now start client instances using:"
-msgstr ""
+msgstr "现在我们可以使用："
 
 #: ../../source/tutorial-quickstart-huggingface.rst:221
 msgid ""
 "And they will be able to connect to the server and start the federated "
 "training."
-msgstr ""
+msgstr "他们就能连接到服务器，开始联合培训。"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:223
 msgid ""
@@ -12537,35 +12834,43 @@ msgid ""
 "huggingface](https://github.com/adap/flower/tree/main/examples"
 "/quickstart-huggingface)."
 msgstr ""
+"如果您想查看所有内容，请查看完整的代码示例： [https://github.com/adap/flower/"
+"tree/main/examples/quickstart-huggingface](https://github.com/adap/flower/"
+"tree/main/examples/quickstart-huggingface)."
 
 #: ../../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 ""
+msgstr "当然，这只是一个非常基本的示例，还可以添加或修改很多内容，"
+"只是为了展示我们可以如何简单地使用 Flower 联合 \"拥抱的脸 \"工作流程。"
 
 #: ../../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 ""
+msgstr "请注意，在本例中我们使用了 :code:`PyTorch`，但也完全可以使用 "
+":code:`TensorFlow`。"
 
 #: ../../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 ""
+"阅读本 Federated Learning 快速入门教程，了解如何使用 Flower 创建 iOS "
+"应用程序，并在 MNIST 上训练神经网络。"
 
 #: ../../source/tutorial-quickstart-ios.rst:5
 msgid "Quickstart iOS"
-msgstr ""
+msgstr "快速入门 iOS"
 
 #: ../../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 ""
+msgstr "在本教程中，我们将学习如何在 iOS 设备上使用 Flower 和 CoreML 在 MNIST "
+"上训练神经网络。"
 
 #: ../../source/tutorial-quickstart-ios.rst:12
 msgid ""
@@ -12574,12 +12879,16 @@ msgid ""
 "<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 ""
+"首先，为了运行 Flower Python 服务器，建议创建一个虚拟环境，并在 `virtualenv "
+"<https://flower.dev/docs/recommended-env-setup.html>`_ 中运行一切。对于在 "
+"iOS 中实现 Flower 客户端，建议使用 Xcode 作为我们的集成开发环境。"
 
 #: ../../source/tutorial-quickstart-ios.rst:15
 msgid ""
 "Our example consists of one Python *server* and two iPhone *clients* that"
 " all have the same model."
-msgstr ""
+msgstr "我们的示例包括一个 Python 服务器*和两个 iPhone "
+"客户端*，它们都具有相同的模型。"
 
 #: ../../source/tutorial-quickstart-ios.rst:17
 msgid ""
@@ -12589,17 +12898,22 @@ msgid ""
 "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 ""
+"*客户*负责根据其本地数据集为模型生成单独的权重更新。然后，这些更新会被发送到*"
+"服务器，由*服务器汇总后生成一个更好的模型。最后，*服务器*将改进后的模型发送回"
+"每个*客户端*。一个完整的权重更新周期称为一个*轮*。"
 
 #: ../../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 ""
+msgstr "现在我们已经有了一个大致的概念，让我们开始设置 Flower 服务器环境吧。首先，"
+"我们需要安装 Flower。你可以使用 pip 来安装："
 
 #: ../../source/tutorial-quickstart-ios.rst:27
+#, fuzzy
 msgid "Or Poetry:"
-msgstr ""
+msgstr "或诗歌："
 
 #: ../../source/tutorial-quickstart-ios.rst:36
 msgid ""
@@ -12609,6 +12923,9 @@ msgid ""
 "Flower client with CoreML, that has been implemented and stored inside "
 "the Swift SDK. The client implementation can be seen below:"
 msgstr ""
+"现在我们已经安装了所有依赖项，让我们使用 CoreML 作为本地训练管道和 MNIST "
+"作为数据集，运行一个简单的分布式训练。为了简单起见，我们将使用 CoreML 的完整 "
+"Flower 客户端，该客户端已在 Swift SDK 中实现并存储。客户端实现如下："
 
 #: ../../source/tutorial-quickstart-ios.rst:72
 msgid ""
@@ -12620,10 +12937,16 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/ios>`_ to learn more "
 "about the app."
 msgstr ""
+"让我们在 Xcode 中创建一个新的应用程序项目，并在项目中添加 :code:`flwr` "
+"作为依赖关系。对于我们的应用程序，我们将在 :code:`FLiOSModel.swift` "
+"中存储应用程序的逻辑，在 :code:`ContentView.swift` 中存储 UI "
+"元素。在本快速入门中，我们将更多地关注 :code:`FLiOSModel.swift`。请参阅 "
+"\"完整代码示例 <https://github.com/adap/flower/tree/main/examples/ios>`_\" "
+"以了解更多有关应用程序的信息。"
 
 #: ../../source/tutorial-quickstart-ios.rst:75
 msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:"
-msgstr ""
+msgstr "在 :code:`FLiOSModel.swift` 中导入 Flower 和 CoreML 相关软件包："
 
 #: ../../source/tutorial-quickstart-ios.rst:83
 msgid ""
@@ -12635,6 +12958,11 @@ msgid ""
 "into :code:`MLBatchProvider` object. The preprocessing is done inside "
 ":code:`DataLoader.swift`."
 msgstr ""
+"然后通过拖放将 mlmodel 添加到项目中，在部署到 iOS 设备时，mlmodel "
+"将被捆绑到应用程序中。我们需要传递 url 以访问 mlmodel 并运行 CoreML "
+"机器学习进程，可通过调用函数 :code:`Bundle.main.url` 获取。对于 MNIST "
+"数据集，我们需要将其预处理为 :code:`MLBatchProvider` 对象。预处理在 "
+":code:`DataLoader.swift` 中完成。"
 
 #: ../../source/tutorial-quickstart-ios.rst:99
 msgid ""
@@ -12645,18 +12973,23 @@ msgid ""
 "which are written as proto files. The implementation can be seen in "
 ":code:`MLModelInspect`."
 msgstr ""
+"由于 CoreML 不允许在训练前查看模型参数，而在训练过程中或训练后访问模型参数只"
+"能通过指定层名来完成，因此我们需要事先通过查看模型规范（写成 proto "
+"文件）来了解这些信息。具体实现可参见 :code:`MLModelInspect`。"
 
 #: ../../source/tutorial-quickstart-ios.rst:102
 msgid ""
 "After we have all of the necessary informations, let's create our Flower "
 "client."
-msgstr ""
+msgstr "获得所有必要信息后，让我们创建 Flower 客户端。"
 
 #: ../../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 ""
+"然后启动 Flower gRPC 客户端，并通过将 Flower 客户端传递给函数 "
+":code:`startFlwrGRPC` 来开始与服务器通信。"
 
 #: ../../source/tutorial-quickstart-ios.rst:124
 msgid ""
@@ -12667,6 +13000,10 @@ msgid ""
 "in the application before clicking the start button to start the "
 "federated learning process."
 msgstr ""
+"这就是客户端。我们只需实现 :code:`Client` 或调用提供的 :code:`MLFlwrClient` "
+"并调用 :code:`startFlwrGRPC()`。属性 :code:`hostname` 和 :code:`port` 会告诉"
+"客户端要连接到哪个服务器。这可以通过在应用程序中输入主机名和端口来实现，然后"
+"再点击开始按钮启动联合学习进程。"
 
 #: ../../source/tutorial-quickstart-ios.rst:131
 #: ../../source/tutorial-quickstart-mxnet.rst:226
@@ -12677,6 +13014,9 @@ msgid ""
 "configuration possibilities at their default values. In a file named "
 ":code:`server.py`, import Flower and start the server:"
 msgstr ""
+"对于简单的工作负载，我们可以启动 Flower "
+"服务器，并将所有配置选项保留为默认值。在名为 :code:`server.py` 的文件中，"
+"导入 Flower 并启动服务器："
 
 #: ../../source/tutorial-quickstart-ios.rst:142
 #: ../../source/tutorial-quickstart-mxnet.rst:237
@@ -12684,7 +13024,7 @@ msgstr ""
 #: ../../source/tutorial-quickstart-scikitlearn.rst:215
 #: ../../source/tutorial-quickstart-tensorflow.rst:112
 msgid "Train the model, federated!"
-msgstr ""
+msgstr "联合训练模型！"
 
 #: ../../source/tutorial-quickstart-ios.rst:144
 #: ../../source/tutorial-quickstart-pytorch.rst:218
@@ -12694,7 +13034,8 @@ 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 ""
+msgstr "客户端和服务器都已准备就绪，我们现在可以运行一切，看看联合学习的实际效果。FL "
+"系统通常有一个服务器和多个客户端。因此，我们必须先启动服务器："
 
 #: ../../source/tutorial-quickstart-ios.rst:152
 msgid ""
@@ -12705,6 +13046,11 @@ msgid ""
 "<https://developer.apple.com/documentation/xcode/running-your-app-in-"
 "simulator-or-on-a-device>`_."
 msgstr ""
+"服务器运行后，我们就可以在不同的终端启动客户端。通过 Xcode 构建并运行客户端，"
+"一个通过 Xcode 模拟器，另一个通过部署到 iPhone。"
+"要了解更多有关如何将应用程序部署到 iPhone 或模拟器的信息，请访问 `此处 "
+"<https://developer.apple.com/documentation/xcode/running-your-app-in-"
+"simulator-or-on-a-device>`_。"
 
 #: ../../source/tutorial-quickstart-ios.rst:156
 msgid ""
@@ -12713,32 +13059,39 @@ msgid ""
 "<https://github.com/adap/flower/blob/main/examples/ios>`_ for this "
 "example can be found in :code:`examples/ios`."
 msgstr ""
+"恭喜您 您已经成功地在 ios 设备中构建并运行了第一个联合学习系统。"
+"本示例的完整源代码 <https://github.com/adap/flower/blob/main/examples/ios>`_ "
+"可在 :code:`examples/ios` 中找到。"
 
 #: ../../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 ""
+"查看此 Federated Learning 快速入门教程，了解如何使用 Flower 和 Jax 在 scikit-"
+"learn 数据集上训练线性回归模型。"
 
 #: ../../source/tutorial-quickstart-jax.rst:5
 msgid "Quickstart JAX"
-msgstr ""
+msgstr "快速入门 JAX"
 
 #: ../../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 ""
+msgstr "查看此 Federated Learning 快速入门教程，了解如何使用 Flower 和 MXNet 在 "
+"MNIST 上训练序列模型。"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:5
 msgid "Quickstart MXNet"
-msgstr ""
+msgstr "快速入门 MXNet"
 
 #: ../../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 ""
+msgstr "在本教程中，我们将学习如何使用 Flower 和 MXNet 在 MNIST 上训练 "
+":code:`Sequential` 模型。"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:12
 #: ../../source/tutorial-quickstart-scikitlearn.rst:12
@@ -12747,6 +13100,8 @@ msgid ""
 "within this `virtualenv <https://flower.dev/docs/recommended-env-"
 "setup.html>`_."
 msgstr ""
+"建议创建一个虚拟环境，并在此 `virtualenv <https://flower.dev/docs/"
+"recommended-env-setup.html>`_ 中运行所有内容。"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:16
 #: ../../source/tutorial-quickstart-scikitlearn.rst:16
@@ -12758,17 +13113,21 @@ msgid ""
 "model back to each *client*. A complete cycle of parameters updates is "
 "called a *round*."
 msgstr ""
+"*客户*负责根据其本地数据集为模型生成单独的模型参数更新。然后，这些更新将被发"
+"送到*服务器，由*服务器汇总后生成一个更新的全局模型。最后，*服务器*将这一改进"
+"版模型发回给每个*客户端*。一个完整的参数更新周期称为*轮*。"
 
 #: ../../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 ""
+msgstr "现在，我们已经有了一个大致的概念，让我们开始吧。首先，我们需要安装 "
+"Flower。运行："
 
 #: ../../source/tutorial-quickstart-mxnet.rst:26
 msgid "Since we want to use MXNet, let's go ahead and install it:"
-msgstr ""
+msgstr "既然我们要使用 MXNet，那就继续安装吧："
 
 #: ../../source/tutorial-quickstart-mxnet.rst:36
 msgid ""
@@ -12778,16 +13137,20 @@ msgid ""
 "Digit Recognition tutorial "
 "<https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_."
 msgstr ""
+"现在，我们已经安装了所有依赖项，让我们用两个客户端和一个服务器来运行一个简单"
+"的分布式训练。我们的训练程序和网络架构基于 MXNet 的 \"手写数字识别教程 "
+"<https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/"
+"mnist.html>`_\"。"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:38
 msgid ""
 "In a file called :code:`client.py`, import Flower and MXNet related "
 "packages:"
-msgstr ""
+msgstr "在名为 :code:`client.py` 的文件中，导入 Flower 和 MXNet 相关软件包："
 
 #: ../../source/tutorial-quickstart-mxnet.rst:53
 msgid "In addition, define the device allocation in MXNet with:"
-msgstr ""
+msgstr "此外，还可以在 MXNet 中定义设备分配："
 
 #: ../../source/tutorial-quickstart-mxnet.rst:59
 msgid ""
@@ -12795,28 +13158,33 @@ msgid ""
 "handwritten digits for machine learning. The MXNet utility "
 ":code:`mx.test_utils.get_mnist()` downloads the training and test data."
 msgstr ""
+"我们使用 MXNet 加载 MNIST，这是一个用于机器学习的流行手写数字图像分类数据集。"
+"MXNet 工具 :code:`mx.test_utils.get_mnist()` 会下载训练和测试数据。"
 
 #: ../../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 ""
+msgstr "用 MXNet "
+"定义训练和损失。我们在数据集上循环训练模型，测量相应的损失，并对其进行优化。"
 
 #: ../../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 ""
+msgstr "接下来，我们定义机器学习模型的验证。我们在测试集上循环，测量测试集上的损失和"
+"准确率。"
 
 #: ../../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 ""
+msgstr "在定义了 MXNet 机器学习模型的训练和测试后，我们使用这些函数实现了 Flower "
+"客户端。"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:137
 msgid "Our Flower clients will use a simple :code:`Sequential` model:"
-msgstr ""
+msgstr "我们的 Flower 客户端将使用简单的 :code:`Sequential` 模型："
 
 #: ../../source/tutorial-quickstart-mxnet.rst:156
 msgid ""
@@ -12824,6 +13192,9 @@ msgid ""
 " propagation to initialize the model and model parameters with "
 ":code:`model(init)`. Next, we implement a Flower client."
 msgstr ""
+"使用 :code:`load_data()` 加载数据集后，我们会执行一次前向传播，使用 "
+":code:`model(init)` 初始化模型和模型参数。接下来，我们实现一个 Flower "
+"客户端。"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:158
 #: ../../source/tutorial-quickstart-pytorch.rst:144
@@ -12835,6 +13206,10 @@ msgid ""
 "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 ""
+"Flower 服务器通过一个名为 :code:`Client` 的接口与客户端交互。当服务器选择一个"
+"特定的客户端进行训练时，它会通过网络发送训练指令。客户端接收到这些指令后，"
+"会调用 :code:`Client` "
+"方法之一来运行你的代码（即训练我们之前定义的神经网络）。"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:164
 msgid ""
@@ -12844,18 +13219,21 @@ msgid ""
 "defining the following methods (:code:`set_parameters` is optional "
 "though):"
 msgstr ""
+"Flower 提供了一个名为 :code:`NumPyClient` 的便利类，当您的工作负载使用 MXNet "
+"时，它可以让您更轻松地实现 :code:`Client` 接口。实现 :code:`NumPyClient` "
+"通常意味着定义以下方法（:code:`set_parameters` 是可选的）："
 
 #: ../../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 ""
+msgstr "以 NumPy ndarrays 列表形式返回模型权重"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:171
 #: ../../source/tutorial-quickstart-pytorch.rst:157
 #: ../../source/tutorial-quickstart-scikitlearn.rst:111
 msgid ":code:`set_parameters` (optional)"
-msgstr ""
+msgstr ":code:`set_parameters` （可选）"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:172
 #: ../../source/tutorial-quickstart-pytorch.rst:158
@@ -12863,41 +13241,42 @@ msgstr ""
 msgid ""
 "update the local model weights with the parameters received from the "
 "server"
-msgstr ""
+msgstr "用从服务器接收到的参数更新本地模型权重"
 
 #: ../../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 ""
+msgstr "设置本地模型权重"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:175
 #: ../../source/tutorial-quickstart-pytorch.rst:161
 #: ../../source/tutorial-quickstart-scikitlearn.rst:115
 msgid "train the local model"
-msgstr ""
+msgstr "训练本地模型"
 
 #: ../../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 ""
+msgstr "接收更新的本地模型权重"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:178
 #: ../../source/tutorial-quickstart-pytorch.rst:164
 #: ../../source/tutorial-quickstart-scikitlearn.rst:118
 msgid "test the local model"
-msgstr ""
+msgstr "测试本地模型"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:180
 msgid "They can be implemented in the following way:"
-msgstr ""
+msgstr "它们可以通过以下方式实现："
 
 #: ../../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 ""
+msgstr "现在我们可以创建一个 :code:`MNISTClient` "
+"类的实例，并添加一行来实际运行该客户端："
 
 #: ../../source/tutorial-quickstart-mxnet.rst:217
 #: ../../source/tutorial-quickstart-scikitlearn.rst:150
@@ -12911,6 +13290,12 @@ msgid ""
 "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 ""
+"这就是客户端。我们只需实现 :code:`Client` 或 :code:`NumPyClient` 并调用 "
+":code:`fl.client.start_client()` 或 :code:`fl.client.start_numpy_client()`。"
+"字符串 :code:`\"0.0.0.0:8080\"`会告诉客户端要连接的服务器。在本例中，我们可以"
+"在同一台机器上运行服务器和客户端，因此我们使用 :code:`\"0.0.0.0:8080\""
+"`。如果我们运行的是真正的联合工作负载，服务器和客户端运行在不同的机器上，"
+"那么需要改变的只是传递给客户端的 :code:`server_address`。"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:239
 msgid ""
@@ -12918,6 +13303,8 @@ msgid ""
 "federated learning in action. Federated learning systems usually have a "
 "server and multiple clients. We therefore have to start the server first:"
 msgstr ""
+"客户端和服务器都准备就绪后，我们现在就可以运行一切，看看联合学习的运行情况。"
+"联合学习系统通常有一个服务器和多个客户端。因此，我们必须先启动服务器："
 
 #: ../../source/tutorial-quickstart-mxnet.rst:247
 #: ../../source/tutorial-quickstart-pytorch.rst:226
@@ -12927,7 +13314,8 @@ msgstr ""
 msgid ""
 "Once the server is running we can start the clients in different "
 "terminals. Open a new terminal and start the first client:"
-msgstr ""
+msgstr "服务器运行后，我们就可以在不同终端启动客户端了。打开一个新终端，启动第一个客"
+"户端："
 
 #: ../../source/tutorial-quickstart-mxnet.rst:254
 #: ../../source/tutorial-quickstart-pytorch.rst:233
@@ -12935,7 +13323,7 @@ msgstr ""
 #: ../../source/tutorial-quickstart-tensorflow.rst:129
 #: ../../source/tutorial-quickstart-xgboost.rst:537
 msgid "Open another terminal and start the second client:"
-msgstr ""
+msgstr "打开另一台终端，启动第二个客户端："
 
 #: ../../source/tutorial-quickstart-mxnet.rst:260
 #: ../../source/tutorial-quickstart-pytorch.rst:239
@@ -12945,7 +13333,8 @@ 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 ""
+msgstr "每个客户端都有自己的数据集。现在你应该看到第一个终端（启动服务器的终端）的训"
+"练效果了："
 
 #: ../../source/tutorial-quickstart-mxnet.rst:292
 msgid ""
@@ -12955,20 +13344,25 @@ msgid ""
 "mxnet/client.py>`_ for this example can be found in :code:`examples"
 "/quickstart-mxnet`."
 msgstr ""
+"恭喜您！您已经成功构建并运行了第一个联合学习系统。本示例的完整源代码 "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-mxnet/client."
+"py>`_ 可在 :code:`examples/quickstart-mxnet` 中找到。"
 
 #: ../../source/tutorial-quickstart-pandas.rst:-1
 msgid ""
 "Check out this Federated Learning quickstart tutorial for using Flower "
 "with Pandas to perform Federated Analytics."
 msgstr ""
+"查看此 Federated Learning 快速入门教程，了解如何使用 Flower 和 Pandas 执行 "
+"Federated Analytics。"
 
 #: ../../source/tutorial-quickstart-pandas.rst:5
 msgid "Quickstart Pandas"
-msgstr ""
+msgstr "快速入门熊猫"
 
 #: ../../source/tutorial-quickstart-pandas.rst:10
 msgid "Let's build a federated analytics system using Pandas and Flower!"
-msgstr ""
+msgstr "让我们使用 Pandas 和 Flower 建立一个联合分析系统！"
 
 #: ../../source/tutorial-quickstart-pandas.rst:12
 msgid ""
@@ -12976,18 +13370,23 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/quickstart-pandas>`_ "
 "to learn more."
 msgstr ""
+"请参阅 \"完整代码示例 <https://github.com/adap/flower/tree/main/examples/"
+"quickstart-pandas>`_\" 了解更多信息。"
 
 #: ../../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 ""
+"查看此 Federated Learning 快速入门教程，了解如何使用 Flower 和 PyTorch 在 "
+"MNIST 上训练 CNN 模型。"
 
 #: ../../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 ""
+msgstr "在本教程中，我们将学习如何使用 Flower 和 PyTorch 在 CIFAR10 "
+"上训练卷积神经网络。"
 
 #: ../../source/tutorial-quickstart-pytorch.rst:15
 #: ../../source/tutorial-quickstart-xgboost.rst:36
@@ -12996,12 +13395,15 @@ msgid ""
 "everything within a `virtualenv <https://flower.dev/docs/recommended-env-"
 "setup.html>`_."
 msgstr ""
+"首先，建议创建一个虚拟环境，并在 `virtualenv <https://flower.dev/docs/"
+"recommended-env-setup.html>`_ 中运行一切。"
 
 #: ../../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 ""
+msgstr "既然我们想用 PyTorch 解决计算机视觉任务，那就继续安装 PyTorch 和 "
+"**torchvision** 库吧："
 
 #: ../../source/tutorial-quickstart-pytorch.rst:39
 msgid ""
@@ -13011,16 +13413,20 @@ msgid ""
 "with PyTorch "
 "<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_."
 msgstr ""
+"现在我们已经安装了所有的依赖项，让我们用两个客户端和一个服务器来运行一个简单"
+"的分布式训练。我们的训练过程和网络架构基于 PyTorch 的《Deep Learning with "
+"PyTorch <https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial."
+"html>`_》。"
 
 #: ../../source/tutorial-quickstart-pytorch.rst:41
 msgid ""
 "In a file called :code:`client.py`, import Flower and PyTorch related "
 "packages:"
-msgstr ""
+msgstr "在名为 :code:`client.py` 的文件中，导入 Flower 和 PyTorch 相关软件包："
 
 #: ../../source/tutorial-quickstart-pytorch.rst:56
 msgid "In addition, we define the device allocation in PyTorch with:"
-msgstr ""
+msgstr "此外，我们还在 PyTorch 中定义了设备分配："
 
 #: ../../source/tutorial-quickstart-pytorch.rst:62
 msgid ""
@@ -13028,37 +13434,42 @@ msgid ""
 "dataset for machine learning. The PyTorch :code:`DataLoader()` downloads "
 "the training and test data that are then normalized."
 msgstr ""
+"我们使用 PyTorch 来加载 "
+"CIFAR10，这是一个用于机器学习的流行彩色图像分类数据集。PyTorch "
+":code:`DataLoader()`下载训练数据和测试数据，然后进行归一化处理。"
 
 #: ../../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 ""
+msgstr "使用 PyTorch 定义损失和优化器。数据集的训练是通过循环数据集、测量相应的损失并"
+"对其进行优化来完成的。"
 
 #: ../../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 ""
+msgstr "然后定义机器学习网络的验证。我们在测试集上循环，测量测试集的损失和准确率。"
 
 #: ../../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 ""
+msgstr "在定义了 PyTorch 机器学习模型的训练和测试之后，我们将这些功能用于 Flower "
+"客户端。"
 
 #: ../../source/tutorial-quickstart-pytorch.rst:115
 msgid ""
 "The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 "
 "Minute Blitz':"
-msgstr ""
+msgstr "Flower 客户端将使用一个简单的从“PyTorch： 60 分钟突击\"改编的CNN："
 
 #: ../../source/tutorial-quickstart-pytorch.rst:142
 msgid ""
 "After loading the data set with :code:`load_data()` we define the Flower "
 "interface."
-msgstr ""
+msgstr "使用 :code:`load_data()` 加载数据集后，我们定义了 Flower 接口。"
 
 #: ../../source/tutorial-quickstart-pytorch.rst:150
 msgid ""
@@ -13068,17 +13479,22 @@ msgid ""
 "defining the following methods (:code:`set_parameters` is optional "
 "though):"
 msgstr ""
+"Flower 提供了一个名为 :code:`NumPyClient` 的便利类，当您的工作负载使用 "
+"PyTorch 时，它使 :code:`Client` 接口的实现变得更容易。实现 "
+":code:`NumPyClient` 通常意味着定义以下方法（:code:`set_parameters` "
+"是可选的）："
 
 #: ../../source/tutorial-quickstart-pytorch.rst:166
 msgid "which can be implemented in the following way:"
-msgstr ""
+msgstr "可以通过以下方式实现："
 
 #: ../../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 ""
+msgstr "现在我们可以创建一个 :code:`CifarClient` "
+"类的实例，并添加一行来实际运行该客户端："
 
 #: ../../source/tutorial-quickstart-pytorch.rst:196
 #: ../../source/tutorial-quickstart-tensorflow.rst:90
@@ -13092,6 +13508,12 @@ msgid ""
 "server and clients running on different machines, all that needs to "
 "change is the :code:`server_address` we point the client at."
 msgstr ""
+"这就是客户端。我们只需实现 :code:`Client` 或 :code:`NumPyClient` 并调用 "
+":code:`fl.client.start_client()` 或 :code:`fl.client.start_numpy_client()`。"
+"字符串 :code:`\"[::]:8080\"`会告诉客户端要连接的服务器。在本例中，我们可以在"
+"同一台机器上运行服务器和客户端，因此使用 :code:`\"[::]:8080\""
+"。如果我们运行的是真正的联合工作负载，服务器和客户端运行在不同的机器上，"
+"那么需要改变的只是客户端指向的 :code:`server_address`。"
 
 #: ../../source/tutorial-quickstart-pytorch.rst:271
 msgid ""
@@ -13101,22 +13523,27 @@ msgid ""
 "pytorch/client.py>`_ for this example can be found in :code:`examples"
 "/quickstart-pytorch`."
 msgstr ""
+"恭喜您！您已经成功构建并运行了第一个联合学习系统。本示例的完整源代码 "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-pytorch/client."
+"py>`_ 可以在 :code:`examples/quickstart-pytorch` 中找到。"
 
 #: ../../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 ""
+"查看此 Federated Learning 快速入门教程，了解如何使用 Flower 和 PyTorch "
+"Lightning 在 MNIST 上训练自动编码器模型。"
 
 #: ../../source/tutorial-quickstart-pytorch-lightning.rst:5
 msgid "Quickstart PyTorch Lightning"
-msgstr ""
+msgstr "快速入门 PyTorch Lightning"
 
 #: ../../source/tutorial-quickstart-pytorch-lightning.rst:10
 msgid ""
 "Let's build a horizontal federated learning system using PyTorch "
 "Lightning and Flower!"
-msgstr ""
+msgstr "让我们使用 PyTorch Lightning 和 Flower 构建一个水平联合学习系统！"
 
 #: ../../source/tutorial-quickstart-pytorch-lightning.rst:12
 msgid ""
@@ -13124,30 +13551,36 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch-"
 "lightning>`_ to learn more."
 msgstr ""
+"请参阅 \"完整代码示例 <https://github.com/adap/flower/tree/main/examples/"
+"quickstart-pytorch-lightning>`_\" 了解更多信息。"
 
 #: ../../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 ""
+msgstr "查看此 Federated Learning 快速入门教程，了解如何使用 Flower 和 scikit-learn "
+"训练线性回归模型。"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:5
+#, fuzzy
 msgid "Quickstart scikit-learn"
-msgstr ""
+msgstr "快速入门 scikit-learn"
 
 #: ../../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 ""
+"在本教程中，我们将学习如何使用 Flower 和 scikit-learn 在 MNIST 上训练一个 "
+":code:`Logistic Regression` 模型。"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:26
 msgid "Since we want to use scikt-learn, let's go ahead and install it:"
-msgstr ""
+msgstr "既然我们要使用 scikt-learn，那就继续安装吧："
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:32
 msgid "Or simply install all dependencies using Poetry:"
-msgstr ""
+msgstr "或者直接使用 Poetry 安装所有依赖项："
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:42
 msgid ""
@@ -13158,54 +13591,64 @@ msgid ""
 ":code:`utils.py` contains different functions defining all the machine "
 "learning basics:"
 msgstr ""
+"现在我们已经安装了所有的依赖项，让我们用两个客户端和一个服务器来运行一个简单"
+"的分布式训练。不过，在设置客户端和服务器之前，我们将在 :code:`utils.py` "
+"中定义联合学习设置所需的所有功能。:code:`utils."
+"py`包含定义所有机器学习基础知识的不同函数："
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:45
 msgid ":code:`get_model_parameters()`"
-msgstr ""
+msgstr "代码：`get_model_parameters()` 获取模型参数"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:46
 msgid "Returns the paramters of a :code:`sklearn` LogisticRegression model"
-msgstr ""
+msgstr "返回 :code:`sklearn` LogisticRegression 模型的参数"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:47
+#, fuzzy
 msgid ":code:`set_model_params()`"
-msgstr ""
+msgstr "代码：`set_model_params()` 设置模型参数"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:48
+#, fuzzy
 msgid "Sets the parameters of a :code:`sklean` LogisticRegression model"
-msgstr ""
+msgstr "设置 :code:`sklean` LogisticRegression 模型的参数"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:49
+#, fuzzy
 msgid ":code:`set_initial_params()`"
-msgstr ""
+msgstr ":code:`set_initial_params()`"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:50
 msgid "Initializes the model parameters that the Flower server will ask for"
-msgstr ""
+msgstr "初始化 Flower 服务器将要求的模型参数"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:51
+#, fuzzy
 msgid ":code:`load_mnist()`"
-msgstr ""
+msgstr ":code:`load_mnist()`"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:52
 msgid "Loads the MNIST dataset using OpenML"
-msgstr ""
+msgstr "使用 OpenML 加载 MNIST 数据集"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:53
+#, fuzzy
 msgid ":code:`shuffle()`"
-msgstr ""
+msgstr ":code:`shuffle()`"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:54
 msgid "Shuffles data and its label"
-msgstr ""
+msgstr "对数据及其标签进行洗牌"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:56
+#, fuzzy
 msgid ":code:`partition()`"
-msgstr ""
+msgstr ":code:`partition()`"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:56
 msgid "Splits datasets into a number of partitions"
-msgstr ""
+msgstr "将数据集分割成多个分区"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:58
 msgid ""
@@ -13215,6 +13658,10 @@ msgid ""
 " the :code:`client.py` and imported. The :code:`client.py` also requires "
 "to import several packages such as Flower and scikit-learn:"
 msgstr ""
+"更多详情请查看 :code:`utils.py`` 这里 <https://github.com/adap/flower/blob/"
+"main/examples/sklearn-logreg-mnist/utils.py>`_。在 :code:`client.py` "
+"中使用并导入了预定义函数。:code:`client.py` 还需要导入几个软件包，如 Flower "
+"和 scikit-learn："
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:73
 msgid ""
@@ -13224,12 +13671,17 @@ msgid ""
 "and test data. The training set is split afterwards into 10 partitions "
 "with :code:`utils.partition()`."
 msgstr ""
+"我们从 `OpenML <https://www.openml.org/d/554>`_ 中加载 MNIST "
+"数据集，这是一个用于机器学习的流行手写数字图像分类数据集。实用程序 "
+":code:`utils.load_mnist()` 下载训练和测试数据。然后使用 :code:`utils."
+"partition()`将训练集分割成 10 个分区。"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:85
 msgid ""
 "Next, the logistic regression model is defined and initialized with "
 ":code:`utils.set_initial_params()`."
-msgstr ""
+msgstr "接下来，使用 :code:`utils.set_initial_params()` "
+"对逻辑回归模型进行定义和初始化。"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:97
 msgid ""
@@ -13239,6 +13691,10 @@ msgid ""
 "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 ""
+"Flower 服务器通过一个名为 :code:`Client` 的接口与客户端交互。当服务器选择一个"
+"特定的客户端进行训练时，它会通过网络发送训练指令。客户端接收到这些指令后，"
+"会调用 :code:`Client` "
+"方法之一来运行您的代码（即拟合我们之前定义的逻辑回归）。"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:103
 msgid ""
@@ -13248,20 +13704,25 @@ msgid ""
 "means defining the following methods (:code:`set_parameters` is optional "
 "though):"
 msgstr ""
+"Flower 提供了一个名为 :code:`NumPyClient` 的便利类，当你的工作负载使用 "
+"scikit-learn 时，它可以让你更容易地实现 :code:`Client` 接口。实现 "
+":code:`NumPyClient` 通常意味着定义以下方法（:code:`set_parameters` "
+"是可选的）："
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:112
 msgid "is directly imported with :code:`utils.set_model_params()`"
-msgstr ""
+msgstr "直接导入 :code:`utils.set_model_params()`"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:120
 msgid "The methods can be implemented in the following way:"
-msgstr ""
+msgstr "这些方法可以通过以下方式实现："
 
 #: ../../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 ""
+msgstr "现在我们可以创建一个 :code:`MnistClient` "
+"类的实例，并添加一行来实际运行该客户端："
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:159
 msgid ""
@@ -13269,10 +13730,12 @@ msgid ""
 "evaluation function for the server-side evaluation. First, we import "
 "again all required libraries such as Flower and scikit-learn."
 msgstr ""
+"下面的 Flower 服务器更先进一些，会返回一个用于服务器端评估的评估函数。首先，"
+"我们再次导入所有需要的库，如 Flower 和 scikit-learn。"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:162
 msgid ":code:`server.py`, import Flower and start the server:"
-msgstr ""
+msgstr ":code:`server.py`, import Flower 并启动服务器："
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:173
 msgid ""
@@ -13281,6 +13744,8 @@ msgid ""
 "function is called after each federated learning round and gives you "
 "information about loss and accuracy."
 msgstr ""
+"联合学习轮数在 :code:`fit_round()` 中设置，评估在 :code:`get_evaluate_fn()` "
+"中定义。每轮联合学习后都会调用评估函数，并提供有关损失和准确率的信息。"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:198
 msgid ""
@@ -13292,6 +13757,11 @@ msgid ""
 " :code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", "
 "strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`."
 msgstr ""
+"代码:`main`包含服务器端参数初始化 :代码:`utils.set_initial_params()`"
+"以及聚合策略 :代码:`fl.server.strategy:FedAvg()`。该策略是默认的联合平均（或 "
+"FedAvg）策略，有两个客户端，在每轮联合学习后进行评估。可以使用 :code:`fl."
+"server.start_server(server_address=\"0.0.0.0:8080\", strategy=strategy, "
+"config=fl.server.ServerConfig(num_rounds=3))` 命令启动服务器。"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:217
 msgid ""
@@ -13300,6 +13770,8 @@ msgid ""
 "server and multiple clients. We, therefore, have to start the server "
 "first:"
 msgstr ""
+"客户端和服务器都准备就绪后，我们现在就可以运行一切，看看联合学习的运行情况。"
+"联合学习系统通常有一个服务器和多个客户端。因此，我们必须先启动服务器："
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:271
 msgid ""
@@ -13309,34 +13781,39 @@ msgid ""
 "mnist>`_ for this example can be found in :code:`examples/sklearn-logreg-"
 "mnist`."
 msgstr ""
+"恭喜您！您已经成功构建并运行了第一个联合学习系统。本示例的完整源代码 "
+"<https://github.com/adap/flower/tree/main/examples/sklearn-logreg-mnist>`_ "
+"可以在 :code:`examples/sklearn-logreg-mnist` 中找到。"
 
 #: ../../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 ""
+"查看此 Federated Learning 快速入门教程，了解如何使用 Flower 和 TensorFlow 在 "
+"CIFAR-10 上训练 MobilNetV2 模型。"
 
 #: ../../source/tutorial-quickstart-tensorflow.rst:5
 msgid "Quickstart TensorFlow"
-msgstr ""
+msgstr "快速入门 TensorFlow"
 
 #: ../../source/tutorial-quickstart-tensorflow.rst:13
 msgid "Let's build a federated learning system in less than 20 lines of code!"
-msgstr ""
+msgstr "让我们用不到 20 行代码构建一个联邦学习系统！"
 
 #: ../../source/tutorial-quickstart-tensorflow.rst:15
 msgid "Before Flower can be imported we have to install it:"
-msgstr ""
+msgstr "在导入 Flower 之前，我们必须先安装它："
 
 #: ../../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 ""
+msgstr "由于我们要使用 TensorFlow (TF) 的 Keras API，因此还必须安装 TF："
 
 #: ../../source/tutorial-quickstart-tensorflow.rst:31
 msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:"
-msgstr ""
+msgstr "接下来，在名为 :code:`client.py` 的文件中导入 Flower 和 TensorFlow："
 
 #: ../../source/tutorial-quickstart-tensorflow.rst:38
 msgid ""
@@ -13346,12 +13823,16 @@ msgid ""
 "it locally, and then returns the entire training and test set as NumPy "
 "ndarrays."
 msgstr ""
+"我们使用 TF 的 Keras 实用程序加载 "
+"CIFAR10，这是一个用于机器学习的流行彩色图像分类数据集。调用 :code:`tf.keras."
+"datasets.cifar10.load_data()` 会下载 CIFAR10，将其缓存到本地，然后以 NumPy "
+"ndarrays 的形式返回整个训练集和测试集。"
 
 #: ../../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 ""
+msgstr "接下来，我们需要一个模型。在本教程中，我们使用带有 10 个输出类的 MobilNetV2："
 
 #: ../../source/tutorial-quickstart-tensorflow.rst:60
 msgid ""
@@ -13360,16 +13841,19 @@ msgid ""
 "workload uses Keras. The :code:`NumPyClient` interface defines three "
 "methods which can be implemented in the following way:"
 msgstr ""
+"Flower 提供了一个名为 :code:`NumPyClient` 的便利类，当您的工作负载使用 Keras "
+"时，该类可以更轻松地实现 :code:`Client` 接口。:code:`NumPyClient` "
+"接口定义了三个方法，可以通过以下方式实现："
 
 #: ../../source/tutorial-quickstart-tensorflow.rst:135
 msgid "Each client will have its own dataset."
-msgstr ""
+msgstr "每个客户都有自己的数据集。"
 
 #: ../../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 ""
+msgstr "现在你应该能在第一个终端（启动服务器的终端）看到训练的效果了："
 
 #: ../../source/tutorial-quickstart-tensorflow.rst:169
 msgid ""
@@ -13379,20 +13863,25 @@ msgid ""
 "tensorflow/client.py>`_ for this can be found in :code:`examples"
 "/quickstart-tensorflow/client.py`."
 msgstr ""
+"恭喜您！您已经成功构建并运行了第一个联合学习系统。完整的源代码 "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-tensorflow/"
+"client.py>`_ 可以在 :code:`examples/quickstart-tensorflow/client.py` 中找到。"
 
 #: ../../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 ""
+msgstr "查看此 Federated Learning 快速入门教程，了解如何使用 Flower 和 XGBoost "
+"在树上训练分类模型。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:5
+#, fuzzy
 msgid "Quickstart XGBoost"
-msgstr ""
+msgstr "快速入门 XGBoost"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:11
 msgid "Federated XGBoost"
-msgstr ""
+msgstr "联邦 XGBoost"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:13
 msgid ""
@@ -13403,17 +13892,22 @@ msgid ""
 "speed of machine learning models. In XGBoost, trees are constructed "
 "concurrently, unlike the sequential approach taken by GBDT."
 msgstr ""
+"EXtreme Gradient Boosting（**XGBoost**）是梯度提升决策树（**GBDT**）的一种稳"
+"健而高效的实现方法，能最大限度地提高提升树方法的计算边界。它主要用于提高机器"
+"学习模型的性能和计算速度。在 XGBoost 中，决策树是并发构建的，与 GBDT "
+"采用的顺序方法不同。"
 
 #: ../../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 ""
+msgstr "对于训练示例少于 10k 的中型数据集上的表格数据，XGBoost "
+"的结果往往超过深度学习技术。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:20
 msgid "Why federated XGBoost?"
-msgstr ""
+msgstr "为什么选择联邦 XGBoost？"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:22
 msgid ""
@@ -13421,7 +13915,8 @@ msgid ""
 "there's an increasing requirement to implement federated XGBoost systems "
 "for specialised applications, like survival analysis and financial fraud "
 "detection."
-msgstr ""
+msgstr "事实上，随着对数据隐私和分散学习的需求不断增长，越来越多的专业应用（如生存分"
+"析和金融欺诈检测）需要实施联邦 XGBoost 系统。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:24
 msgid ""
@@ -13431,6 +13926,9 @@ msgid ""
 "of XGBoost, combining it with federated learning offers a promising "
 "solution for these specific challenges."
 msgstr ""
+"联邦学习可确保原始数据保留在本地设备上，因此对于数据安全和隐私至关重要的敏感"
+"领域来说，这是一种极具吸引力的方法。鉴于 XGBoost 的稳健性和高效性，将其与联邦"
+"学习相结合为应对这些特定挑战提供了一种前景广阔的解决方案。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:27
 msgid ""
@@ -13443,22 +13941,30 @@ msgid ""
 "comprehensive <https://github.com/adap/flower/tree/main/examples/xgboost-"
 "comprehensive>`_) to run various experiments."
 msgstr ""
+"在本教程中，我们将学习如何使用 Flower 和 :code:`xgboost` 软件包在 HIGGS "
+"数据集上训练联邦 XGBoost 模型。我们将使用一个包含两个 * 客户端* 和一个 * "
+"服务器* 的简单示例 (`完整代码 xgboost-quickstart <https://github.com/adap/"
+"flower/tree/main/examples/xgboost-quickstart>`_)来演示联邦 XGBoost 如何工作，"
+"然后我们将深入到一个更复杂的示例 (`完整代码 xgboost-comprehensive "
+"<https://github.com/adap/flower/tree/main/examples/xgboost-"
+"comprehensive>`_)，以运行各种实验。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:34
 msgid "Environment Setup"
-msgstr ""
+msgstr "环境设定"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:38
 msgid ""
 "We first need to install Flower and Flower Datasets. You can do this by "
 "running :"
-msgstr ""
+msgstr "我们首先需要安装 Flower 和 Flower Datasets。您可以通过运行 ："
 
 #: ../../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 ""
+msgstr "既然我们要使用 :code:`xgboost` 软件包来构建 XGBoost 树，那就继续安装 "
+":code:`xgboost`："
 
 #: ../../source/tutorial-quickstart-xgboost.rst:54
 msgid ""
@@ -13467,22 +13973,26 @@ msgid ""
 "dependencies installed, let's run a simple distributed training with two "
 "clients and one server."
 msgstr ""
+"*客户*负责根据其本地数据集为模型生成单独的权重更新。现在我们已经安装了所有的"
+"依赖项，让我们用两个客户端和一个服务器来运行一个简单的分布式训练。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:57
 msgid ""
 "In a file called :code:`client.py`, import xgboost, Flower, Flower "
 "Datasets and other related functions:"
-msgstr ""
+msgstr "在名为 :code:`client.py` 的文件中，导入 xgboost、Flower、Flower Datasets "
+"和其他相关函数："
 
 #: ../../source/tutorial-quickstart-xgboost.rst:84
 msgid "Dataset partition and hyper-parameter selection"
-msgstr ""
+msgstr "数据集划分和超参数选择"
 
 #: ../../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 ""
+msgstr "在本地训练之前，我们需要从 Flower Datasets 加载 HIGGS 数据集，并对 FL "
+"进行数据分区："
 
 #: ../../source/tutorial-quickstart-xgboost.rst:99
 msgid ""
@@ -13490,22 +14000,27 @@ msgid ""
 "distribution (:code:`IidPartitioner(num_partitions=2)`). Then, we load "
 "the partition for the given client based on :code:`node_id`:"
 msgstr ""
+"在此示例中，我们将数据集分割成两个均匀分布的分区（:code:`IidPartitioner(num_p"
+"artitions=2)`）。然后，我们根据 :code:`node_id` 为给定客户端加载分区："
 
 #: ../../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 ""
+msgstr "然后，我们在给定的分区（客户端的本地数据）上进行训练/测试分割，并为 "
+":code:`xgboost` 软件包转换数据格式。"
 
 #: ../../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 ""
+":code:`train_test_split` 和 :code:`transform_dataset_too_dmatrix` "
+"的函数定义如下："
 
 #: ../../source/tutorial-quickstart-xgboost.rst:155
 msgid "Finally, we define the hyper-parameters used for XGBoost training."
-msgstr ""
+msgstr "最后，我们定义了用于 XGBoost 训练的超参数。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:171
 msgid ""
@@ -13514,10 +14029,13 @@ msgid ""
 "GPU by setting :code:`tree_method` to :code:`gpu_hist`. We use AUC as "
 "evaluation metric."
 msgstr ""
+"代码:`num_local_round`表示本地树提升的迭代次数。我们默认使用 CPU 进行训练。"
+"可以通过将 :code:`tree_method` 设置为 :code:`gpu_hist`，将其转换为 GPU。"
+"我们使用 AUC 作为评估指标。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:178
 msgid "Flower client definition for XGBoost"
-msgstr ""
+msgstr "用于 XGBoost 的 Flower 客户端定义"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:180
 msgid ""
@@ -13525,6 +14043,8 @@ msgid ""
 "general rule to define :code:`XgbClient` class inherited from "
 ":code:`fl.client.Client`."
 msgstr ""
+"加载数据集后，我们定义 Flower 客户端。我们按照一般规则定义从 :code:`fl.client"
+".Client` 继承而来的 :code:`XgbClient` 类。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:190
 msgid ""
@@ -13533,12 +14053,16 @@ msgid ""
 "integrated in earlier rounds and maintain other essential data structures"
 " for training."
 msgstr ""
+"代码:`self.bst`用于保存在各轮中保持一致的 Booster 对象，使其能够存储在前几轮"
+"中集成的树的预测结果，并维护其他用于训练的重要数据结构。"
 
 #: ../../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 ""
+"然后，我们在 :code:`XgbClient` 类中覆盖 :code:`get_parameters`、:code:`fit` "
+"和 :code:`evaluate` 方法如下。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:207
 msgid ""
@@ -13549,6 +14073,10 @@ msgid ""
 ":code:`get_parameters` when it is called by the server at the first "
 "round."
 msgstr ""
+"与神经网络训练不同，XGBoost 树不是从指定的随机权重开始的。在这种情况下，"
+"我们不使用 :code:`get_parameters` 和 :code:`set_parameters` 来初始化 XGBoost "
+"的模型参数。因此，当服务器在第一轮调用 :code:`get_parameters` 时，让我们在 "
+":code:`get_parameters` 中返回一个空张量。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:248
 msgid ""
@@ -13559,6 +14087,10 @@ msgid ""
 ":code:`self.bst`, and then update model weights on local training data "
 "with function :code:`local_boost` as follows:"
 msgstr ""
+"在 :code:`fit`中，第一轮我们调用 :code:`xgb.train()`来建立第一组树，返回的 "
+"Booster 对象和 config 分别存储在 :code:`self.bst` 和 :code:`self.config` "
+"中。从第二轮开始，我们将服务器发送的全局模型加载到 :code:`self.bst`，"
+"然后使用函数 :code:`local_boost`更新本地训练数据的模型权重，如下所示："
 
 #: ../../source/tutorial-quickstart-xgboost.rst:266
 msgid ""
@@ -13566,18 +14098,24 @@ msgid ""
 ":code:`self.bst.update` method. After training, the last "
 ":code:`N=num_local_round` trees will be extracted to send to the server."
 msgstr ""
+"给定 :code:`num_local_round`，我们通过调用 :code:`self.bst."
+"update`方法更新树。训练结束后，我们将提取最后一个 :code:`N=num_local_round` "
+"树并发送给服务器。"
 
 #: ../../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 ""
+"在 :code:`evaluate`中，我们调用 :code:`self.bst."
+"eval_set`函数对有效集合进行评估。将返回 AUC 值。"
 
 #: ../../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 ""
+msgstr "现在，我们可以创建一个 :code:`XgbClient` "
+"类的实例，并添加一行来实际运行该客户端："
 
 #: ../../source/tutorial-quickstart-xgboost.rst:297
 msgid ""
@@ -13589,6 +14127,12 @@ msgid ""
 "server and clients running on different machines, all that needs to "
 "change is the :code:`server_address` we point the client at."
 msgstr ""
+"这就是客户端。我们只需实现 :code:`客户端`并调用 :code:`fl.client."
+"start_client()`。字符串 :code:`\"[::]:8080\"`会告诉客户端要连接的服务器。在本"
+"例中，我们可以在同一台机器上运行服务器和客户端，因此我们使用 :code:`\"[::]:"
+"8080\""
+"`。如果我们运行的是真正的联合工作负载，服务器和客户端运行在不同的机器上，"
+"那么需要改变的只是客户端指向的 :code:`server_address`。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:308
 msgid ""
@@ -13596,16 +14140,20 @@ msgid ""
 "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 ""
+"然后，这些更新会被发送到*服务器，由*服务器汇总后生成一个更好的模型。最后，*服"
+"务器*将这个改进版的模型发回给每个*客户端*，以完成一轮完整的 FL。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:311
 msgid ""
 "In a file named :code:`server.py`, import Flower and FedXgbBagging from "
 ":code:`flwr.server.strategy`."
 msgstr ""
+"在名为 :code:`server.py` 的文件中，从 :code:`flwr.server.strategy` 导入 "
+"Flower 和 FedXgbBagging。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:313
 msgid "We first define a strategy for XGBoost bagging aggregation."
-msgstr ""
+msgstr "我们首先定义了 XGBoost 袋式聚合策略。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:336
 msgid ""
@@ -13613,20 +14161,22 @@ msgid ""
 ":code:`evaluate_metrics_aggregation` function is defined to collect and "
 "wighted average the AUC values from clients."
 msgstr ""
+"本示例使用两个客户端。我们定义了一个 :code:`evaluate_metrics_aggregation` "
+"函数，用于收集客户机的 AUC 值并求取平均值。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:339
 msgid "Then, we start the server:"
-msgstr ""
+msgstr "然后，我们启动服务器："
 
 #: ../../source/tutorial-quickstart-xgboost.rst:351
 msgid "Tree-based bagging aggregation"
-msgstr ""
+msgstr "基于树的袋式聚合"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:353
 msgid ""
 "You must be curious about how bagging aggregation works. Let's look into "
 "the details."
-msgstr ""
+msgstr "你一定很好奇袋式聚合是如何工作的。让我们来详细了解一下。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:355
 msgid ""
@@ -13635,12 +14185,18 @@ msgid ""
 " Then, we override the :code:`aggregate_fit`, :code:`aggregate_evaluate` "
 "and :code:`evaluate` methods as follows:"
 msgstr ""
+"在文件 :code:`flwr.server.strategy.fedxgb_bagging.py`中，我们定义了从 "
+":code:`flwr.server.strategy.FedAvg`继承的 :code:`FedXgbBagging`。然后，"
+"我们覆盖 :code:`aggregate_fit`、:code:`aggregate_evaluate` 和 "
+":code:`evaluate` 方法如下："
 
 #: ../../source/tutorial-quickstart-xgboost.rst:451
 msgid ""
 "In :code:`aggregate_fit`, we sequentially aggregate the clients' XGBoost "
 "trees by calling :code:`aggregate()` function:"
 msgstr ""
+"在 :code:`aggregate_fit` 中，我们通过调用 :code:`aggregate()` 函数，"
+"按顺序聚合客户的 XGBoost 树："
 
 #: ../../source/tutorial-quickstart-xgboost.rst:510
 msgid ""
@@ -13650,16 +14206,20 @@ msgid ""
 " After that, the trees (containing model weights) are aggregated to "
 "generate a new tree model."
 msgstr ""
+"在该函数中，我们首先通过调用 :code:`_get_tree_nums` 获取当前模型和上一个模型"
+"的树数和并行树数。然后，对获取的信息进行汇总。然后，汇总树（包含模型权重），"
+"生成新的树模型。"
 
 #: ../../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 ""
+msgstr "在遍历所有客户的模型后，会生成一个新的全局模型，然后进行序列化，并发回给每个"
+"客户。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:520
 msgid "Launch Federated XGBoost!"
-msgstr ""
+msgstr "启动联邦 XGBoost！"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:582
 msgid ""
@@ -13668,6 +14228,9 @@ msgid ""
 ":code:`metrics_distributed`. One can see that the average AUC increases "
 "over FL rounds."
 msgstr ""
+"恭喜您！您已成功构建并运行了第一个联合 XGBoost 系统。可以在 "
+":code:`metrics_distributed` 中查看 AUC 值。我们可以看到，平均 AUC 随 FL "
+"轮数的增加而增加。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:587
 msgid ""
@@ -13675,10 +14238,12 @@ msgid ""
 "/xgboost-quickstart/>`_ for this example can be found in :code:`examples"
 "/xgboost-quickstart`."
 msgstr ""
+"此示例的完整源代码 <https://github.com/adap/flower/blob/main/examples/"
+"xgboost-quickstart/>`_ 可在 :code:`examples/xgboost-quickstart` 中找到。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:591
 msgid "Comprehensive Federated XGBoost"
-msgstr ""
+msgstr "全面的联邦 XGBoost"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:593
 msgid ""
@@ -13690,10 +14255,15 @@ msgid ""
 " setups, including data partitioning and centralised/distributed "
 "evaluation. Let's take a look!"
 msgstr ""
+"既然您已经知道联合 XGBoost 如何与 Flower "
+"协同工作，那么现在就该通过自定义实验设置来运行一些更全面的实验了。在 xgboost-"
+"comprehensive 示例 (`完整代码 <https://github.com/adap/flower/tree/main/"
+"examples/xgboost-comprehensive>`_)中，我们提供了更多选项来定义各种实验设置，"
+"包括数据分区和集中/分布式评估。让我们一起来看看！"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:599
 msgid "Customised data partitioning"
-msgstr ""
+msgstr "定制数据分区"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:601
 msgid ""
@@ -13703,16 +14273,20 @@ msgid ""
 "provide four supported partitioner type to simulate the uniformity/non-"
 "uniformity in data quantity (uniform, linear, square, exponential)."
 msgstr ""
+"在 :code:`dataset.py` 中，我们有一个函数 :code:`instantiate_partitioner` "
+"来根据给定的 :code:`num_partitions` 和 :code:`partitioner_type` 来实例化数据"
+"分区器。目前，我们提供四种支持的分区器类型（均匀、线性、正方形、指数）来模拟"
+"数据量的均匀性/非均匀性。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:632
 msgid "Customised centralised/distributed evaluation"
-msgstr ""
+msgstr "定制的集中/分布式评估"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:634
 msgid ""
 "To facilitate centralised evaluation, we define a function in "
 ":code:`server.py`:"
-msgstr ""
+msgstr "为便于集中评估，我们在 :code:`server.py` 中定义了一个函数："
 
 #: ../../source/tutorial-quickstart-xgboost.rst:666
 msgid ""
@@ -13721,6 +14295,9 @@ msgid ""
 "evaluation is conducted by calling :code:`eval_set()` method, and the "
 "tested AUC value is reported."
 msgstr ""
+"此函数返回一个评估函数，该函数实例化一个 :code:`Booster` "
+"对象，并向其加载全局模型权重。评估通过调用 :code:`eval_set()` 方法进行，"
+"并报告测试的 AUC 值。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:669
 msgid ""
@@ -13728,17 +14305,20 @@ msgid ""
 "start example by overriding the :code:`evaluate()` method insides the "
 ":code:`XgbClient` class in :code:`client.py`."
 msgstr ""
+"至于客户端上的分布式评估，与快速启动示例相同，通过覆盖 :code:`client.py` 中 "
+":code:`XgbClient` 类内部的 :code:`evaluate()` 方法。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:673
 msgid "Arguments parser"
-msgstr ""
+msgstr "参数 解析器"
 
 #: ../../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 ""
+msgstr "在 :code:`utils.py` 中，我们定义了客户端和服务器端的参数解析器，允许用户指定"
+"不同的实验设置。让我们先看看服务器端："
 
 #: ../../source/tutorial-quickstart-xgboost.rst:714
 msgid ""
@@ -13748,31 +14328,36 @@ msgid ""
 "evaluation and all functionalities for client evaluation will be "
 "disabled."
 msgstr ""
+"这允许用户指定总客户数/FL 轮数/参与客户数/评估客户数以及评估方式。请注意，"
+"如果使用 :code:`--centralised-eval`，S sever "
+"将进行集中评估，客户端评估的所有功能将被禁用。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:718
 msgid "Then, the argument parser on client side:"
-msgstr ""
+msgstr "然后是客户端的参数解析器："
 
 #: ../../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 ""
+msgstr "这定义了客户端数据分区的各种选项。此外，通过设置 :code:`-centralised-"
+"eval`，客户端还可以选择在集中测试集上进行评估。"
 
 #: ../../source/tutorial-quickstart-xgboost.rst:764
 msgid "Example commands"
-msgstr ""
+msgstr "命令示例"
 
 #: ../../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 ""
+msgstr "为了在 5 个客户端上进行 50 "
+"轮指数分布的集中评估实验，我们首先启动服务器，如下所示："
 
 #: ../../source/tutorial-quickstart-xgboost.rst:773
 msgid "Then, on each client terminal, we start the clients:"
-msgstr ""
+msgstr "然后，我们在每个客户终端上启动客户机："
 
 #: ../../source/tutorial-quickstart-xgboost.rst:779
 msgid ""
@@ -13780,10 +14365,13 @@ msgid ""
 "/xgboost-comprehensive/>`_ for this comprehensive example can be found in"
 " :code:`examples/xgboost-comprehensive`."
 msgstr ""
+"此综合示例的全部源代码 <https://github.com/adap/flower/blob/main/examples/"
+"xgboost-comprehensive/>`_ 可在 :code:`examples/xgboost-comprehensive` "
+"中找到。"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:9
 msgid "Build a strategy from scratch"
-msgstr ""
+msgstr "从零开始制定策略"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:11
 msgid ""
@@ -13795,6 +14383,12 @@ msgid ""
 " (`part 2 <https://flower.dev/docs/framework/tutorial-use-a-federated-"
 "learning-strategy-pytorch.html>`__)."
 msgstr ""
+"欢迎来到 Flower 联合学习教程的第三部分。在本教程的前几部分，我们介绍了 "
+"PyTorch 和 Flower 的联合学习（`part 1 <https://flower.dev/docs/framework/"
+"tutorial-get-started-with-flower-pytorch."
+"html>`__），并学习了如何使用策略来定制服务器和客户端的执行（`part 2 "
+"<https://flower.dev/docs/framework/tutorial-use-a-federated-learning-"
+"strategy-pytorch.html>`__）。"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:13
 msgid ""
@@ -13803,6 +14397,9 @@ msgid ""
 " using `Flower <https://flower.dev/>`__ and `PyTorch "
 "<https://pytorch.org/>`__)."
 msgstr ""
+"在本笔记本中，我们将通过创建 FedAvg 的自定义版本（再次使用 `Flower "
+"<https://flower.dev/>`__ 和 `PyTorch <https://pytorch.org/"
+">`__），继续定制我们之前构建的联合学习系统。"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:15
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:16
@@ -13815,15 +14412,19 @@ msgid ""
 "you in the ``#introductions`` channel! And if anything is unclear, head "
 "over to the ``#questions`` channel."
 msgstr ""
+"`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ 并加入 Slack "
+"上的 Flower 社区，进行交流、提问并获得帮助： 加入 Slack <https://flower.dev/"
+"join-slack>`__ 🌼 我们希望在 ``#introductions`` "
+"频道听到您的声音！如果有任何不清楚的地方，请访问 ``#questions`` 频道。"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:17
 msgid "Let's build a new ``Strategy`` from scratch!"
-msgstr ""
+msgstr "让我们从头开始构建一个新的 \"策略\"！"
 
 #: ../../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 ""
+msgstr "准备工作"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:31
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:32
@@ -13831,20 +14432,20 @@ msgstr ""
 msgid ""
 "Before we begin with the actual code, let's make sure that we have "
 "everything we need."
-msgstr ""
+msgstr "在开始实际代码之前，让我们先确保我们已经准备好了所需的一切。"
 
 #: ../../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 ""
+msgstr "安装依赖项"
 
 #: ../../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 ""
+msgstr "首先，我们安装必要的软件包："
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:65
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:66
@@ -13853,7 +14454,7 @@ msgstr ""
 msgid ""
 "Now that we have all dependencies installed, we can import everything we "
 "need for this tutorial:"
-msgstr ""
+msgstr "现在我们已经安装了所有依赖项，可以导入本教程所需的所有内容："
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:101
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:102
@@ -13869,12 +14470,18 @@ msgid ""
 "has GPU acceleration enabled, you should see the output ``Training on "
 "cuda``, otherwise it'll say ``Training on cpu``."
 msgstr ""
+"可以切换到已启用 GPU 加速的运行时（在 Google Colab 上： 运行时 > "
+"更改运行时类型 > 硬件加速： GPU > 保存``）。但请注意，Google Colab "
+"并非总能提供 GPU 加速。如果在以下部分中看到与 GPU 可用性相关的错误，"
+"请考虑通过设置 ``DEVICE = torch.device(\"cpu\")`` 切回基于 CPU 的执行。"
+"如果运行时已启用 GPU 加速，你应该会看到输出``Training on cuda``，否则会显示``"
+"Training on cpu``。"
 
 #: ../../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 ""
+msgstr "数据加载"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:116
 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:116
@@ -13885,12 +14492,16 @@ msgid ""
 " ``num_clients`` which allows us to call ``load_datasets`` with different"
 " numbers of clients."
 msgstr ""
+"现在，让我们加载 CIFAR-10 训练集和测试集，将它们分割成 10 "
+"个较小的数据集（每个数据集又分为训练集和验证集），并将所有数据都封装在各自的 "
+"``DataLoader`` 中。我们引入了一个新参数 ``num_clients``，"
+"它允许我们使用不同数量的客户端调用 ``load_datasets``。"
 
 #: ../../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 ""
+msgstr "模型培训/评估"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:169
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:170
@@ -13898,12 +14509,13 @@ msgstr ""
 msgid ""
 "Let's continue with the usual model definition (including "
 "``set_parameters`` and ``get_parameters``), training and test functions:"
-msgstr ""
+msgstr "让我们继续使用常见的模型定义（包括 `set_parameters` 和 "
+"`get_parameters`）、训练和测试函数："
 
 #: ../../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 ""
+msgstr "Flower 客户端"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:260
 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:260
@@ -13913,14 +14525,18 @@ msgid ""
 "``get_parameters``, ``fit``, and ``evaluate``. Here, we also pass the "
 "``cid`` to the client and use it log additional details:"
 msgstr ""
+"为了实现 Flower 客户端，我们（再次）创建了 ``flwr.client.NumPyClient`` "
+"的子类，并实现了 ``get_parameters``、``fit`` 和 "
+"``evaluate``三个方法。在这里，我们还将 ``cid`` "
+"传递给客户端，并使用它记录其他详细信息："
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:308
 msgid "Let's test what we have so far before we continue:"
-msgstr ""
+msgstr "在继续之前，让我们先测试一下我们目前掌握的情况："
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:339
 msgid "Build a Strategy from scratch"
-msgstr ""
+msgstr "从零开始构建策略"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:341
 msgid ""
@@ -13930,18 +14546,22 @@ msgid ""
 " it is in ``FedAvg`` and then change the configuration dictionary (one of"
 " the ``FitIns`` attributes)."
 msgstr ""
+"让我们重写 ``configure_fit`` "
+"方法，使其向一部分客户的优化器传递更高的学习率（可能还有其他超参数）。"
+"我们将保持 ``FedAvg`` 中的客户端采样，然后更改配置字典（``FitIns`` "
+"属性之一）。"
 
 #: ../../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 ""
+msgstr "剩下的唯一工作就是在启动实验时使用新创建的自定义策略 ``FedCustom`` ："
 
 #: ../../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 ""
+msgstr "回顾"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:536
 msgid ""
@@ -13953,6 +14573,10 @@ msgid ""
 "functions to the constructor of your new class (``__init__``) and then "
 "call these functions whenever needed."
 msgstr ""
+"在本笔记本中，我们了解了如何实施自定义策略。自定义策略可以对客户端节点配置、"
+"结果聚合等进行细粒度控制。要定义自定义策略，只需覆盖（抽象）基类 ``Strategy``"
+" 的抽象方法即可。为使自定义策略更加强大，您可以将自定义函数传递给新类的构造函"
+"数（`__init__``），然后在需要时调用这些函数。"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:550
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:948
@@ -13963,6 +14587,8 @@ msgid ""
 "Before you continue, make sure to join the Flower community on Slack: "
 "`Join Slack <https://flower.dev/join-slack/>`__"
 msgstr ""
+"在继续之前，请务必加入 Slack 上的 Flower 社区：`Join Slack <https://flower."
+"dev/join-slack/>`__"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:552
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:950
@@ -13972,7 +14598,8 @@ msgstr ""
 msgid ""
 "There's a dedicated ``#questions`` channel if you need help, but we'd "
 "also love to hear who you are in ``#introductions``!"
-msgstr ""
+msgstr "如果您需要帮助，我们有专门的 ``#questions`` 频道，但我们也很乐意在 "
+"``#introductions`` 中了解您是谁！"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:554
 msgid ""
@@ -13981,10 +14608,13 @@ msgid ""
 "pytorch.html>`__ introduces ``Client``, the flexible API underlying "
 "``NumPyClient``."
 msgstr ""
+"Flower联邦学习教程 - 第4部分 <https://flower.dev/docs/framework/tutorial-"
+"customize-the-client-pytorch.html>`__ "
+"介绍了``Client``，它是``NumPyClient``底层的灵活应用程序接口。"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:9
 msgid "Customize the client"
-msgstr ""
+msgstr "自定义客户端"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:11
 msgid ""
@@ -13998,6 +14628,14 @@ msgid ""
 "custom strategy from scratch (`part 3 <https://flower.dev/docs/framework"
 "/tutorial-build-a-strategy-from-scratch-pytorch.html>`__)."
 msgstr ""
+"欢迎来到 Flower 联邦学习教程的第四部分。在本教程的前几部分中，我们介绍了 "
+"PyTorch 和 Flower 的联邦学习（`part 1 <https://flower.dev/docs/framework/"
+"tutorial-get-started-with-flower-pytorch."
+"html>`__），了解了如何使用策略来定制服务器和客户端的执行（`part 2 "
+"<https://flower.dev/docs/framework/tutorial-use-a-federated-learning-"
+"strategy-pytorch.html>`__），并从头开始构建了我们自己的定制策略（`part 3 "
+"<https://flower.dev/docs/framework/tutorial-build-a-strategy-from-scratch-"
+"pytorch.html>`__）。"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:14
 msgid ""