TensorFlow Federated
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TensorFlow Federated (TFF) 平台包含两层:
- 联合学习 (FL):将现有 Keras 或非 Keras 机器学习模型插入 TFF 框架的高级接口。无须学习联合学习算法的详细内容,您就可以执行基本任务,如联合训练或评估。
- Federated Core (FC):通过将 TensorFlow 与强类型函数式编程环境中的分布式通信算子相结合,可简明表示自定义联合算法的底层接口。
首先从 TFF 教程开始。这些教程利用实际示例来引导您学习主要 TFF 概念和 API。此外,请务必按照安装说明配置要与 TFF 一起使用的环境。
更详细的指南(请参阅本页的左侧边栏)随后会提供有关重要主题的参考信息。
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最后更新时间 (UTC):2022-06-07。
[null,null,["最后更新时间 (UTC):2022-06-07。"],[],[],null,["# TensorFlow Federated\n\n\u003cbr /\u003e\n\nThe TensorFlow Federated (TFF) platform consists of two layers:\n\n- [Federated Learning (FL)](/federated/federated_learning), high-level interfaces to plug existing Keras or non-Keras machine learning models into the TFF framework. You can perform basic tasks, such as federated training or evaluation, without having to study the details of federated learning algorithms.\n- [Federated Core (FC)](/federated/federated_core), lower-level interfaces to concisely express custom federated algorithms by combining TensorFlow with distributed communication operators within a strongly-typed functional programming environment.\n\nStart with the [TFF tutorials](/federated/tutorials/tutorials_overview) that walk you\nthrough the main TFF concepts and APIs using practical examples. Make sure to\nfollow the [installation instructions](/federated/install) to configure your environment\nfor use with TFF.\n\nThe more detailed guides (see the left sidebar of this page) then provide\nreference information on important topics."]]