# Federated Transformed Learning for a Circular, Secure, and Tiny AI

arXiv: https://arxiv.org/abs/2311.14371 · HTML: https://mlsanity.com/paper/2311.14371/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 24 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Functional Regularisation for Continual Learning with Gaussian Processes](https://mlsanity.com/paper/1901.11356/) (2019)
- [Approximate Inference Turns Deep Networks into Gaussian Processes](https://mlsanity.com/paper/1906.01930/) (2019)
- [High-Dimensional Robust Mean Estimation via Gradient Descent](https://mlsanity.com/paper/2005.01378/) (2020)
- [A Complementary Learning Systems Approach to Temporal Difference Learning](https://mlsanity.com/paper/1905.02636/) (2019)
- [On the Need for Topology-Aware Generative Models for Manifold-Based Defenses](https://mlsanity.com/paper/1909.03334/) (2019)
- [Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge](https://mlsanity.com/paper/1910.05054/) (2019)
- [Explainable Artificial Intelligence (XAI) for 6G: Improving Trust between Human and Machine](https://mlsanity.com/paper/1911.04542/) (2019)

## Abstract (arXiv)

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.