TY - GEN
T1 - TEACHING-Trustworthy autonomous cyber-physical applications through human-centred intelligence
AU - Bacciu, Davide
AU - Akarmazyan, Siranush
AU - Armengaud, Eric
AU - Bacco, Manlio
AU - Bravos, George
AU - Calandra, Calogero
AU - Carlini, Emanuele
AU - Carta, Antonio
AU - Cassara, Pietro
AU - Coppola, Massimo
AU - Davalas, Charalampos
AU - Dazzi, Patrizio
AU - Degennaro, Maria Carmela
AU - Di Sarli, Daniele
AU - Dobaj, Juergen
AU - Gallicchio, Claudio
AU - Girbal, Sylvain
AU - Gotta, Alberto
AU - Groppo, Riccardo
AU - Lomonaco, Vincenzo
AU - Macher, Georg
AU - Mazzei, Daniele
AU - Mencagli, Gabriele
AU - Michail, Dimitrios
AU - Micheli, Alessio
AU - Peroglio, Roberta
AU - Petroni, Salvatore
AU - Potenza, Rosaria
AU - Pourdanesh, Farank
AU - Sardianos, Christos
AU - Tserpes, Konstantinos
AU - Tagliabo, Fulvio
AU - Valtl, Jakob
AU - Varlamis, Iraklis
AU - Veledar, Omar
N1 - Funding Information:
This research was supported by TEACHING, a project funded by the EU Horizon 2020 research and innovation programme under GA n. 871385
Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/23
Y1 - 2021/8/23
N2 - 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
AB - 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
KW - cyber-physical systems
KW - distributed neural networks
KW - edge artificial intelligence
KW - human-centred artificial intelligence
KW - ubiquitous and pervasive computing
UR - http://www.scopus.com/inward/record.url?scp=85115391627&partnerID=8YFLogxK
U2 - 10.1109/COINS51742.2021.9524099
DO - 10.1109/COINS51742.2021.9524099
M3 - Conference paper
AN - SCOPUS:85115391627
T3 - 2021 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2021
BT - 2021 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2021
PB - Institute of Electrical and Electronics Engineers
T2 - 2021 IEEE International Conference on Omni-Layer Intelligent Systems
Y2 - 23 August 2021 through 26 August 2021
ER -