FLAIM: AIM-based Synthetic Data Generation in the Federated Setting. (arXiv:2310.03447v1 [cs.CR])

Preserving individual privacy while enabling collaborative data sharing is
crucial for organizations. Synthetic data generation is one solution, producing
artificial data that mirrors the statistical properties of private data. While
numerous techniques have been devised under differential privacy, they
predominantly assume data is centralized. However, data is often distributed
across multiple clients in a federated manner. In this work, we initiate the
study of federated synthetic tabular data generation. Building upon a SOTA
central method known as AIM, we present …

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