Federated Learning as a Solution for Problems Related to Intergovernmental Data Sharing

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.