The Applicability of Federated Learning to Official Statistics

TL;DR

This paper evaluates federated learning's potential for official statistics, demonstrating its ability to match centralized models across diverse data domains while preserving data privacy.

Contribution

It provides the first detailed analysis of federated learning's applicability to official statistics through simulated use cases across multiple data domains.

Findings

FL models perform close to centralized benchmarks in all use cases

Federated learning enhances data privacy for official statistics

Simulated results support practical implementation of FL in official data collection

Abstract

This work investigates the potential of Federated Learning (FL) for official statistics and shows how well the performance of FL models can keep up with centralized learning methods.F L is particularly interesting for official statistics because its utilization can safeguard the privacy of data holders, thus facilitating access to a broader range of data. By simulating three different use cases, important insights on the applicability of the technology are gained. The use cases are based on a medical insurance data set, a fine dust pollution data set and a mobile radio coverage data set - all of which are from domains close to official statistics. We provide a detailed analysis of the results, including a comparison of centralized and FL algorithm performances for each simulation. In all three use cases, we were able to train models via FL which reach a performance very close to the centralized model benchmarks. Our key observations and their implications for transferring the simulations into practice are summarized. We arrive at the conclusion that FL has the potential to emerge as a pivotal technology in future use cases of official statistics.