federated learning; artificial intelligence; eGovernment; data sharing challenges
Abstract :
[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.
Research center :
- Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations Digital Society Initiative > University of Zurich
Disciplines :
Business & economic sciences: Multidisciplinary, general & others Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
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
External co-authors :
yes
Language :
English
Title :
Federated Learning as a Solution for Problems Related to Intergovernmental Data Sharing
Publication date :
03 January 2023
Event name :
56th Hawaii International Conference on System Sciences
Event organizer :
University of Hawaii
Event place :
Maui, Hawaii, United States
Event date :
from 03-01-23 to 06-01-23
Audience :
International
Main work title :
Proceedings of the 56th Hawaii International Conference on System Sciences
ISBN/EAN :
978-0-9981331-6-4
Pages :
10
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences Security, Reliability and Trust
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