artificial intelligence; data sharing challenges; federated learning; eGovernment
Résumé :
[en] Intergovernmental collaboration is needed to address global problems. Modern solutions to these problems often include data-driven methods like artificial intelligence (AI), which require large amounts of data to perform well. As AI emerges as a central catalyst in deriving effective solutions for global problems, the infrastructure that supports its data needs becomes crucial. However, data sharing between governments is often constrained due to socio-technical barriers such as concerns over data privacy, data sovereignty issues, and the risks of information misuse. Federated learning (FL) presents a promising solution as a decentralized AI methodology, enabling the use of data from multiple silos without necessitating central aggregation. Instead of sharing raw data, governments can build their own models and just share the model parameters with a central server aggregating all parameters, resulting in a superior overall model. By conducting a structured literature review, we show how major intergovernmental data-sharing challenges listed by the Organisation for Economic Co-operation and Development can be overcome by utilizing FL. Furthermore, we provide a tangible resource implementing FL linked to the Ukrainian refugee crisis that can be utilized by researchers and policymakers alike who want to implement FL in cases where data cannot be shared. Enhanced AI while maintaining privacy through FL thus allows governments to collaboratively address global problems, positively impacting governments and citizens.
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations
Disciplines :
Ingénierie, informatique & technologie: Multidisciplinaire, généralités & autres Sciences informatiques
Auteur, co-auteur :
Sprenkamp, Kilian
DELGADO FERNANDEZ, Joaquin ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
Eckhardt, Sven
Zavolokina, Liudmila
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Overcoming intergovernmental data sharing challenges with federated learning
Date de publication/diffusion :
2024
Titre du périodique :
Data and Policy
eISSN :
2632-3249
Maison d'édition :
Cambridge University Press (CUP)
Volume/Tome :
6
Fascicule/Saison :
27
Peer reviewed :
Peer reviewed vérifié par ORBi
Focus Area :
Computational Sciences Security, Reliability and Trust
H2020 - 814654 - MDOT - Medical Device Obligations Taskforce
Projet FnR :
FNR13342933 - Paypal-fnr Pearl Chair In Digital Financial Services, 2019 (01/01/2020-31/12/2024) - Gilbert Fridgen
Organisme subsidiant :
FNR - Fonds National de la Recherche EC - European Commission Union Européenne
Subventionnement (détails) :
We thank the University of Zurich and the Digital Society Initiative for (partially) funding this study under the
Digitalization Initiative of the Zurich Higher Education Institutions postdoc fellowship of L.Z. Further, this work has been supported
by the European Union (EU) within its Horizon 2020 program, project MDOT (Medical Device Obligations Taskforce), grant
agreement 814,654, and from PayPal and the Luxembourg National Research Fund FNR (P17/IS/13342933/PayPal-FNR/Chair in
DFS/Gilbert Fridgen).
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