January 2023
Intermediate to advanced
288 pages
6h 6m
Chinese
Content preview from 移动端AI与ML应用开发：基于iOS和Android
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移动应用程序的道德、公平和隐私
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察者判断计算中是否使用了特定信息。例如，在一个被训练从人口统计推断工资的模型
上，如果攻击者知道一个人在数据集中，那么他们可能知道这个人的人口统计，将其输
入模型，并假设他们的工资在训练集中，期望他们的薪水非常准确。如果模型是使用健
康指标创建的，攻击者可能知道他们的邻居在训练集中，就可以使用部分数据来获取有
关目标的更多数据。
考虑到这一点，TensorFlow Privacy（
https://oreil.ly/anZhq
）为使用差分隐私的训练模型
提供了优化器的实现。
联邦学习
也许移动开发人员最感兴趣但尚未广泛使用的是联邦学习。在这种情况下，你可以根据
用户的使用方式不断地更新和改进模型。因此，用户会与你共享他们的个人数据，以帮
助你改进模型。一种这样的用途是让他们的键盘自动预测他们正在输入的单词。每个人
都是不同的，所以如果我开始输入“ anti”，我可能在输入“ antibiotic”，或者我可能在
输入“ antidisestablishmentarianism ”，键盘应该足够智能，可以根据我以前的用法提供
建议。考虑到这一点，联邦学习技术应运而生。这里对隐私的影响是显而易见的
—
你
希望能够提供一种方法，让你的用户可以以一种不会被滥用的方式与你分享非常私人的
信息
—
例如他们输入的字词。
正如我所提到的，这还不能作为开源 API 供你在应用程序中使用，但你可以使用
TensorFlow Federated 模拟如何做到这一点。
TensorFlow Federated ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.