Federated Transformed Learning for a Circular, Secure, and Tiny AI
Weisi Guo Schyler Sun Bin Li Sam Blakeman
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.