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

TL;DR

This paper presents the TEACHING project, which aims to develop trustworthy, human-centered autonomous cyber-physical applications by leveraging physiological, emotional, and cognitive user states through a distributed, federated learning system.

Contribution

It introduces a novel human-centered approach integrating federated learning with dependability, security, and privacy methods for next-generation autonomous applications.

Findings

Proposes a human-centered AI framework for autonomous systems.

Identifies key AI research challenges in trustworthiness and privacy.

Discusses design strategies for reliable, secure, and adaptive applications.

Abstract

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