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Federated learning as a solution for problems related to intergovernmental data sharing


Sprenkamp, Kilian; Delgado Fernandez, Joaquin; Eckhardt, Sven; Zavolokina, Liudmila (2023). Federated learning as a solution for problems related to intergovernmental data sharing. In: 56th Hawaii International Conference on System Sciences, Maui (HI), USA, 3 January 2023 - 6 January 2023.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
08 Research Priority Programs > Digital Society Initiative
Dewey Decimal Classification:000 Computer science, knowledge & systems
Uncontrolled Keywords:AI in Government, artificial intelligence, data sharing challenges, egovernment, federated learning
Scope:Discipline-based scholarship (basic research)
Language:English
Event End Date:6 January 2023
Deposited On:13 Mar 2023 08:49
Last Modified:11 Mar 2024 12:34
OA Status:Green
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
  • Content: Published Version
  • Language: English