# TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence

arXiv: https://arxiv.org/abs/2107.06543 · HTML: https://mlsanity.com/paper/2107.06543/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 14 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Zero-Shot Learning Through Cross-Modal Transfer](https://mlsanity.com/paper/1301.3666/) (2013)
- [A Gentle Introduction to Deep Learning for Graphs](https://mlsanity.com/paper/1912.12693/) (2019)
- [Continual Learning for Recurrent Neural Networks: an Empirical Evaluation](https://mlsanity.com/paper/2103.07492/) (2021)
- [Continual Learning with Gated Incremental Memories for sequential data processing](https://mlsanity.com/paper/2004.04077/) (2020)
- [Continual Learning with Echo State Networks](https://mlsanity.com/paper/2105.07674/) (2021)

## Built on by (most cited first, 4 of 4)

- [Federated Feature Selection for Cyber-Physical Systems of Systems](https://mlsanity.com/paper/2109.11323/) (2021)
- [AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving](https://mlsanity.com/paper/2202.01645/) (2022)
- [Federated Adaptation of Reservoirs via Intrinsic Plasticity](https://mlsanity.com/paper/2206.11087/) (2022)
- [Graph Neural Networks and Reinforcement Learning for Proactive Application Image Placement](https://mlsanity.com/paper/2407.00007/) (2024)

## Abstract (arXiv)

This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum. TEACHING puts forward a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications. It does so by building a distributed, embedded and federated learning system complemented by methods and tools to enforce its dependability, security and privacy preservation. The paper discusses the main concepts of the TEACHING approach and singles out the main AI-related research challenges associated with it. Further, we provide a discussion of the design choices for the TEACHING system to tackle the aforementioned challenges