64 citations · 252 across the 64 of their papers we have counts for
11 papers · 1 filter
Permutation-Invariant Tabular Data Synthesis
Yujin Zhu, Zilong Zhao, Robert Birke +1
Tabular data synthesis is an emerging approach to circumvent strict regulations on data privacy while discovering knowledge through big data. Although state-of-the-art AI-based tab…
Federated Learning for Tabular Data: Exploring Potential Risk to Privacy
Han Wu, Zilong Zhao, Lydia Y. Chen +1
Federated Learning (FL) has emerged as a potentially powerful privacy-preserving machine learning methodology, since it avoids exchanging data between participants, but instead exc…
FCT-GAN: Enhancing Table Synthesis via Fourier Transform
Zilong Zhao, Robert Birke, Lydia Y. Chen
Synthetic tabular data emerges as an alternative for sharing knowledge while adhering to restrictive data access regulations, e.g., European General Data Protection Regulation (GDP…
Aergia: Leveraging Heterogeneity in Federated Learning Systems
Bart Cox, Lydia Y. Chen, Jérémie Decouchant
Federated Learning (FL) is a popular approach for distributed deep learning that prevents the pooling of large amounts of data in a central server. FL relies on clients to update a…
Targeted Influence with Community and Gender-Aware Seeding
Maciej Styczen, Bing-Jyue Chen, Ya-Wen Teng +3
When spreading information over social networks, seeding algorithms selecting users to start the dissemination play a crucial role. The majority of existing seeding algorithms focu…
Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets
Federico Lucchetti, Jérémie Decouchant, Maria Fernandes +2
Federated learning allows clients to collaboratively train models on datasets that are acquired in different locations and that cannot be exchanged because of their size or regulat…