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.

Convolutional neural networks (CNNs) are flexible, biologically-inspired variants of multi-layer perceptrons that have proven themselves to be exceptionally suited to discriminative vision tasks. However, relatively little is known on whether they can be made both more efficient and more accurate, by introducing suitable transformations that exploit general knowledge of the target classes. We demonstrate this functionality through pre-segmentation of input images with a fast and robust but loose segmentation step, to obtain a set of candidate objects. These objects then undergo a spatial transformation into a reduced space, retaining but a compact high-level representation of their appearance. Additional attributes may be abstracted as raw features that are incorporated after the convolutional phase of the network. Finally, we compare its performance against existing approaches on the challenging problem of detecting lesions in retinal images.