Reference : Federated Learning as a Solution for Problems Related to Intergovernmental Data Sharing
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Multidisciplinary, general & others
Business & economic sciences : Multidisciplinary, general & others
Computational Sciences; Security, Reliability and Trust
http://hdl.handle.net/10993/54319
Federated Learning as a Solution for Problems Related to Intergovernmental Data Sharing
English
Sprenkamp, Kilian mailto [University of Zurich > Department of Informatics]
Delgado Fernandez, Joaquin mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX >]
Eckhardt, Sven mailto [University of Zurich > Department of Informatics]
Zavolokina, Liudmila mailto [University of Zurich > Department of Informatics]
3-Jan-2023
Proceedings of the 56th Hawaii International Conference on System Sciences
10
Yes
International
978-0-9981331-6-4
56th Hawaii International Conference on System Sciences
from 03-01-23 to 06-01-23
University of Hawaii
Maui, Hawaii
USA
[en] federated learning ; artificial intelligence ; eGovernment ; data sharing challenges
[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.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations ; Digital Society Initiative > University of Zurich
European Commission - EC
Researchers ; Professionals ; General public
http://hdl.handle.net/10993/54319
https://hdl.handle.net/10125/102838
H2020 ; 814654 - MDOT - Medical Device Obligations Taskforce
FnR ; FNR13342933 > Gilbert Fridgen > DFS > Paypal-fnr Pearl Chair In Digital Financial Services > 01/01/2020 > 31/12/2024 > 2019

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