Deep Learning (DL) is penetrating into a diverse range of mass mobility, smart living, and industrial applications, rapidly transforming the way we live and work. DL is at the heart of many AI implementations. A key set of challenges is to produce AI modules that are: (1) "circular" - can solve new tasks without forgetting how to solve previous ones, (2) "secure" - have immunity to adversarial data attacks, and (3) "tiny" - implementable in low power low cost embedded hardware. Clearly it is difficult to achieve all three aspects on a single horizontal layer of platforms, as the techniques require transformed deep representations that incur different computation and communication requirements. Here we set out the vision to achieve transformed DL representations across a 5G and Beyond networked architecture. We first detail the cross-sectoral motivations for each challenge area, before demonstrating recent advances in DL research that can achieve circular, secure, and tiny AI (CST-AI). Recognising the conflicting demand of each transformed deep representation, we federate their deep learning transformations and functionalities across the network to achieve connected run-time capabilities.

翻译：深度学习（DL）正渗透到大规模移动性、智能生活和工业应用的各个领域，迅速改变着我们的生活和工作方式。深度学习是众多人工智能实现的核心。其中一系列关键挑战在于制造出具备以下特性的人工智能模块：（1）“循环性”——能够解决新任务而不遗忘如何解决先前任务；（2）“安全性”——对对抗性数据攻击具有免疫力；（3）“微型化”——可在低功耗、低成本的嵌入式硬件中实现。显然，在单一水平平台层上同时实现这三个方面十分困难，因为这些技术需要变换的深度表示，从而产生不同的计算与通信需求。在此，我们提出了在5G及未来网络架构中实现变换深度学习表示的愿景。我们首先详细阐述了每个挑战领域的跨行业动因，随后展示了近期可实现循环、安全与微型人工智能（CST-AI）的深度学习研究进展。认识到每种变换深度表示之间的冲突需求，我们将它们的深度学习变换与功能在网络上进行联邦化，以实现互联的运行时能力。