Trustworthy Efficient AI for Cloud-Edge Computing

Informations projet

MANOLO

N° de convention de subvention: 101135782

DOI

10.3030/101135782

Date de signature de la CE 23 Octobre 2023

Date de début 1 Janvier 2024

Date de fin 31 Decembre 2026

Financé au titre de

• Digital, Industry and Space

Coût total

€ 8 605 772,50

Contribution de l’UE

€ 8 605 772,50

Coordonné par

UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN
Ireland

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Documents, reports (10)

Data Inspection and Generation v.1

Report on the data infrastructure and preparation for training and testing

Data Management Plan v.2

Procedures set for data collection, processing, monitoring, dissemination, and ethics compliance

MANOLO Architecture, and Benchmarking framework v.2

High-level system architecture and benchmarking framework designs

Trustworthy Efficient AI for the Cloud-edge Deep Dive

Detailed mapping of Human-centred Needs and Requirements

Dissemination & Communication Plan & Activities v.2

Dissemination and Communication planning and outcomes.

MANOLO Architecture, and Benchmarking framework v.1

High-level system architecture and benchmarking framework designs

Use case designs and deployment plans

Detail plans for the preparation, deployment and assessment of the MANOLO use cases

Trustworthy Efficiency-Performance Assessment v.1

Report on the MANOLO assessment framework

Data Management Plan v.1

Procedures set for data collection, processing, monitoring, dissemination, and ethics compliance

Dissemination & Communication Plan & Activities v.1

Dissemination and Communication planning and outcomes.

Other (1)

Cloud-edge resources, infrastructure, and AI models mapping v.1

Report on requirements of the hyper distributed training model, and parameters abstracted from the cloud-edge system infrastructure

Publications

Conference proceedings (11)

Virtual worlds, real risks: exploring user safety in the metaverse under the Digital Services Act

Auteurs: Noémie Krack, Jean De Meyere
Publié dans: International Congress Towards a Reponsible Development of the Metaverse, 2024
Éditeur: Zenodo
DOI: 10.5281/ZENODO.13944299

On the Reliability of Artificial Intelligence Systems

Auteurs: Konstantopoulos, Stasinos
Publié dans: Proceedings of the 13th Hellenic Conference on Artificial Intelligence, 2024
Éditeur: Association of Computing Machinery
DOI: 10.5281/ZENODO.7733680

Towards Smart Microfarming in an Urban Computing Continuum

Auteurs: Marla Grunewald, Mounir Bensalem, Jasenka Dizdarević, Admela Jukan
Publié dans: 2024 IEEE Latin-American Conference on Communications (LATINCOM), 2025, ISSN 2689-7563
Éditeur: IEEE
DOI: 10.1109/LATINCOM62985.2024.10770673

PETRA: Parallel End-to-end Training with Reversible Architectures

Auteurs: Stéphane Rivaud, Louis Fournier, Thomas Pumir, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
Publié dans: ICLR 2025 International Conference on Learning Representations, 2025
Éditeur: openreview.net
DOI: 10.48550/ARXIV.2406.02052

Human-Aware Design for Transferring Knowledge During Human-AI Co-Learning

Auteurs: Koutrintzes, Dimitrios; Spatharis, Christos; Dagioglou, Maria
Publié dans: 27th European Conference on Artificial Intelligence, 2024, ISSN 1613-0073
Éditeur: CEUR
DOI: 10.5281/ZENODO.14772017

Lecture Notes in Computer Science

Auteurs: Alexandros Nousias, Maria Dagioglou, Georgios Petasis
Publié dans: Lecture Notes in Computer Science, Value Engineering in Artificial Intelligence, 2025, ISSN 0302-9743
Éditeur: Springer Verlag
DOI: 10.1007/978-3-031-85463-7_15

Comparing Prior and Learned Time Representations in Transformer Models of Timeseries

Auteurs: Natalia Koliou; Tatiana Boura; Stasinos Konstantopoulos; Georgios Meramveliotakis; George Kosmadakis
Publié dans: Proceedings of the 13th Hellenic Conference on Artificial Intelligence, 2024
Éditeur: Association for Computing Machinery
DOI: 10.48550/ARXIV.2411.12476

Effective ML Model Versioning in Edge Networks

Auteurs: Fin Gentzen, Mounir Bensalem, Admela Jukan
Publié dans: 2025 International Wireless Communications and Mobile Computing (IWCMC), 2025
Éditeur: IEEE
DOI: 10.1109/IWCMC65282.2025.11059571

Growth strategies for arbitrary DAG neural architectures

Auteurs: Stella Douka, Manon Verbockhaven, Théo Rudkiewicz, Stéphane Rivaud, François P. Landes, Sylvain Chevallier, Guillaume Charpiat
Publié dans: ESANN 2025 - 33th European Symposium on Artificial Neural Networks European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2025
Éditeur: i6doc.com
DOI: 10.48550/ARXIV.2501.12690

$$ Xpression $$: A Unifying Metric to Optimize Compression and Explainability Robustness of AI Models

Auteurs: Eric Arazo, Hristo Stoev, Cristian Bosch, Andrés L. Suárez-Cetrulo, Ricardo Simón-Carbajo
Publié dans: Communications in Computer and Information Science, Explainable Artificial Intelligence, 2024, ISSN 1865-0937
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-63787-2_19

Explainable Artificial Intelligence

Publié dans: Communications in Computer and Information Science, 2024
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-63787-2

Other (1)

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts

Auteurs: spis, M., Cajas Ordónez, S. A., Suárez-Cetrulo, A. L., and Simón Carbajo, R.
Publié dans: arXiv e-prints, 2025
Éditeur: arXiv.org
DOI: 10.48550/ARXIV.2507.18464