Federated Learning for Tabular Data: Exploring Potential Risk to Privacy

Han Wu, Zilong Zhao, Lydia Y. Chen, Aad van Moorsel

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Federated Learning (FL) has emerged as a potentially powerful privacy-preserving machine learning method-ology, since it avoids exchanging data between participants, but instead exchanges model parameters. FL has traditionally been applied to image, voice and similar data, but recently it has started to draw attention from domains including financial services where the data is predominantly tabular. However, the work on tabular data has not yet considered potential attacks, in particular attacks using Generative Adversarial Networks (GANs), which have been successfully applied to FL for non-tabular data. This paper is the first to explore leakage of private data in Federated Learning systems that process tabular data. We design a Generative Adversarial Networks (GANs)-based attack model which can be deployed on a malicious client to reconstruct data and its properties from other participants. As a side-effect of considering tabular data, we are able to statistically assess the efficacy of the attack (without relying on human observation such as done for FL for images). We implement our attack model in a recently developed generic FL software framework for tabular data processing. The experimental results demonstrate the effectiveness of the proposed attack model, thus suggesting that further research is required to counter GAN-based privacy attacks.
Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE)
EditorsCristina Ceballos
Place of PublicationPiscataway
Number of pages12
ISBN (Electronic)978-1-6654-5132-1
ISBN (Print)978-1-6654-5133-8
Publication statusPublished - 2022
Event2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE) - Charlotte, United States
Duration: 31 Oct 20223 Nov 2022
Conference number: 33rd


Conference2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE)
Country/TerritoryUnited States

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Federated learning
  • GAN
  • Privacy
  • Tabular Data


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