50 citations · 162 across the 60 of their papers we have counts for
6 papers · 2 filters
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…
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…
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…
AGIC: Approximate Gradient Inversion Attack on Federated Learning
Jin Xu, Chi Hong, Jiyue Huang +2
Federated learning is a private-by-design distributed learning paradigm where clients train local models on their own data before a central server aggregates their local updates to…
CTAB-GAN+: Enhancing Tabular Data Synthesis
Zilong Zhao, Aditya Kunar, Robert Birke +1
While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) limit its full effectiven…