Are privacy-preserving techniques actually being used in production ML systems? [D]
English summary
This Reddit post from r/MachineLearning discusses the real-world adoption of privacy-preserving ML techniques like differential privacy, federated learning, and on-device inference. The author asks industry practitioners whether these methods are deployed in production, what engineering challenges arise, and how privacy requirements affect model performance and infrastructure costs. It also invites stories about specific use cases where these approaches have proven valuable or where tradeoffs made adoption difficult.
Chinese summary
这篇来自 r/MachineLearning 的 Reddit 帖子讨论了差分隐私、联邦学习和设备端推理等隐私保护机器学习技术在实际中的应用。作者询问业内从业者这些方法是否已在生产环境中部署，遇到了哪些工程挑战，以及隐私要求如何影响模型性能和基础设施成本。还邀请大家分享具体用例中这些方法的价值体现以及权衡使采用变得困难的故事。
Key points
The post investigates whether privacy-preserving techniques like differential privacy, federated learning, and on-device inference are used in production ML systems.
该帖子探讨了差分隐私、联邦学习和设备端推理等隐私保护技术是否在生产级机器学习系统中被使用。
It asks about major engineering challenges when deploying these techniques in industry.
它询问在工业界部署这些技术时遇到的主要工程挑战。
It questions how privacy requirements impact model performance and infrastructure costs.
它质疑隐私要求如何影响模型性能和基础设施成本。
It invites success stories and cases where tradeoffs made adoption difficult.
该帖子邀请分享成功案例以及因权衡而使采用变得困难的情况。