Last June, our special session Trustworthy, Regulated, and Efficient AI on the Edge (TRUST-E) was successfully held as part of the 22nd International Conference on Distributed Computing and Artificial Intelligence (DCAI25).
During the session at dAIEDGE, we brought together researchers and professionals interested in the latest advances in developing reliable and efficient Artificial Intelligence applied to edge systems (Edge AI).
The in-person event took place on Wednesday, 25 June, and was chaired by Juan Manuel Núñez from the University of Salamanca (Spain). We at dAIEDGE would like to thank him once again for his excellent coordination and commitment to the success of this session.
The online part was held on Thursday, 26 June, moderated by Ricardo Alonso from the AIR Institute, to whom we also extend our sincere thanks for his dedication and support during the virtual presentations.
During the online session, highly relevant research in the field of Edge AI was presented, including:
- ‘MLOps for Edge AI: Satellite Sea Ice Detection Test Bed’ by Juan Odriozola, Giovanni Paolini, Markel Flores and Ander Garcia, presenting a test bed for sea ice detection using AI in
- ‘An approach to the study of neural networks for the optimisation of photovoltaic systems in Spain’ by Manal Jammal, Javier Parra-Domínguez and Laura Sanz-Martín, showing strategies for optimising photovoltaic systems using neural
- ‘A Comparative Study of Efficient In-Orbit Model Updating Methods’ by Pablo del Hoyo, Noelia Vallez and Oscar Deniz, comparing efficient methods for updating models in
- “Enhancing AI Benchmarking in dAIEdge-VLab with Blockchain Technology” by Raúl López Blanco, Diego Valdeolmillos Villaverde, Maïck Huguenin, Baptiste Dupertuis, Gregoire Rebstein, Margaux Divernois and Nuria Pazos Escudero, who explored the integration of blockchain to strengthen AI benchmarking in dAIEdge-
- ‘Gender permeability in Artificial Intelligence in Finance: A Systematic Review’ by Joysiane Monroy-Tepepa, Laura Sanz-Martín and Manal Jammal, conducting a systematic review of gender permeability in AI applied to finance.