Several industries in many different domains are looking at deep learning as a way to take advantage of the insights in their data, to improve their competitiveness, to open up novel business possibilities, or to resolve the problem that thought to be impossible to tackle. The large scale of the systems where deep learning is applied and the need of preserving the privacy of the used data have imposed a shift from the traditional centralized deployment to a more collaborative one. However, this has opened up several vulnerabilities caused by compromised nodes and inputs, with traditional crypto primitives and access control models exploited to offer protection means. Providing security can be costly in terms of higher energy consumption, calling for a wise use of these protection means. This paper exploits game theory to model interactions among collaborative deep learning nodes and to decide when using actions to support security enhancements.

中文翻译：

---

在对抗场景下确保工业应用中的协作深度学习

许多不同领域的几个行业都在将深度学习视为一种利用其数据洞察力，提高竞争力，开拓新的业务机会或解决被认为无法解决的问题的方法。应用深度学习的大规模系统以及对所使用数据的隐私保护的需求已迫使从传统的集中式部署转变为更具协作性的部署。但是，由于传统的密码原语和访问控制模型被利用来提供保护手段，这已经打开了由漏洞的节点和输入引起的多个漏洞。就更高的能量消耗而言，提供安全性可能是昂贵的，需要明智地使用这些保护装置。