Not logged in.

Quick Search - Contribution

Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Federated learning as a solution for problems related to intergovernmental data sharing
Organization Unit
  • Contribution from another University/Organization than University of Zurich
Authors
  • Kilian Sprenkamp
  • Joaquin Delgado Fernandez
  • Sven Eckhardt
  • Liudmila Zavolokina
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Event Title 56th Hawaii International Conference on System Sciences
Event Type conference
Event Location Maui (HI), USA
Event Start Date January 3 - 2023
Event End Date January 6 - 2023
Abstract Text 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.
Official URL https://www.researchgate.net/publication/364308366_Federated_Learning_as_a_Solution_for_Problems_Related_to_Intergovernmental_Data_Sharing
Other Identification Number merlin-id:22905
PDF File Download from ZORA
Export BibTeX
EP3 XML (ZORA)
Keywords AI in Government, artificial intelligence, data sharing challenges, egovernment, federated learning