The Applicability of Federated Learning to Official Statistics
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 28 Jul 2023.
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References · 9
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
- 2020Adaptive Federated Optimizationcited by 1,046
- 2019Deep Leakage from Gradientscited by 1,021
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- 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 334
- 2017Machine Learning Models that Remember Too Muchcited by 169
- 2020Survey of Personalization Techniques for Federated Learningcited by 145
- 2019Practical Federated Gradient Boosting Decision Treescited by 54
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