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机器学习 Machine Learning (查看源代码)

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==== 联合学习 Federated learning ====

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'''联合学习 Federated Learning'''是一种新的训练机器学习模型的方法，它分散了训练的过程，允许用户不需要将他们的数据发送到一个集中的服务器这样的做法来维护他们的隐私。通过将模型的训练过程分散到许多设备上，提升了算法效率。例如，谷歌董事会使用联合机器学习刚发来训练用户手机上的搜索查询预测模型，而不必将每个人地搜索信息发送回谷歌。<ref>{{Cite web|url=http://ai.googleblog.com/2017/04/federated-learning-collaborative.html|title=Federated Learning: Collaborative Machine Learning without Centralized Training Data|website=Google AI Blog|language=en|access-date=2019-06-08}}</ref>

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~~{{Main|Federated learning}}~~

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Federated learning is a new approach to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, [[Gboard]] uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to [[Google]].<~~ref~~>{{Cite web|url=http://ai.googleblog.com/2017/04/federated-learning-collaborative.html|title=Federated Learning: Collaborative Machine Learning without Centralized Training Data|website=Google AI Blog|language=en|access-date=2019-06-08}}</ref>

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Federated learning is a new approach to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.

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'''联合学习 Federated Learning'''是一种新的训练机器学习模型的方法，它分散了训练的过程，允许用户不需要将他们的数据发送到一个集中的服务器这样的做法来维护他们的隐私。通过将模型的训练过程分散到许多设备上，提升了算法效率。例如，谷歌董事会使用联合机器学习刚发来训练用户手机上的搜索查询预测模型，而不必将每个人地搜索信息发送回谷歌。

==应用==

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