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Federated Learning (FL) has emerged as a potentially powerfulprivacy-preserving machine learning methodology, since it avoids exchangingdata between participants, but instead exchanges model parameters. FL hastraditionally been applied to image, voice and similar data, but recently ithas started to draw attention from domains including financial services wherethe data is predominantly tabular. However, the work on tabular data has notyet considered potential attacks, in particular attacks using GenerativeAdversarial Networks (GANs), which have been successfully applied to FL fornon-tabular data. This paper is the first to explore leakage of private data inFederated Learning systems that process tabular data. We design a GenerativeAdversarial Networks (GANs)-based attack model which can be deployed on amalicious client to reconstruct data and its properties from otherparticipants. As a side-effect of considering tabular data, we are able tostatistically assess the efficacy of the attack (without relying on humanobservation such as done for FL for images). We implement our attack model in arecently developed generic FL software framework for tabular data processing.The experimental results demonstrate the effectiveness of the proposed attackmodel, thus suggesting that further research is required to counter GAN-basedprivacy attacks.

Federated Learning for Tabular Data: Exploring Potential Risk to Privacy