federated learning; artificial intelligence; eGovernment; data sharing challenges
Résumé :
[en] To address global problems, intergovernmental collaboration is needed. Modern solutions to these problems often include data-driven methods like artificial intelligence (AI), which require large amounts of data to perform well. However, data sharing between governments is limited. A possible solution is federated learning (FL), a decentralised AI method created to utilise personal information on edge devices. Instead of sharing data, governments can build their own models and just share the model parameters with a centralised server aggregating all parameters, resulting in a superior overall model. By conducting a structured literature review, we show how major intergovernmental data sharing challenges like disincentives, legal and ethical issues as well as technical constraints can be solved through FL. Enhanced AI while maintaining privacy through FL thus allows governments to collaboratively address global problems, which will positively impact governments and citizens.
Centre de recherche :
- Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations Digital Society Initiative > University of Zurich
Disciplines :
Sciences économiques & de gestion: Multidisciplinaire, généralités & autres Ingénierie, informatique & technologie: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
Sprenkamp, Kilian; University of Zurich > Department of Informatics
DELGADO FERNANDEZ, Joaquin ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
Eckhardt, Sven; University of Zurich > Department of Informatics
Zavolokina, Liudmila; University of Zurich > Department of Informatics
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Federated Learning as a Solution for Problems Related to Intergovernmental Data Sharing
Date de publication/diffusion :
03 janvier 2023
Nom de la manifestation :
56th Hawaii International Conference on System Sciences
Organisateur de la manifestation :
University of Hawaii
Lieu de la manifestation :
Maui, Hawaii, Etats-Unis
Date de la manifestation :
from 03-01-23 to 06-01-23
Manifestation à portée :
International
Titre de l'ouvrage principal :
Proceedings of the 56th Hawaii International Conference on System Sciences
ISBN/EAN :
978-0-9981331-6-4
Pagination :
10
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences Security, Reliability and Trust
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