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Preserving individual privacy while enabling collaborative data sharing iscrucial for organizations. Synthetic data generation is one solution, producingartificial data that mirrors the statistical properties of private data. Whilenumerous techniques have been devised under differential privacy, theypredominantly assume data is centralized. However, data is often distributedacross multiple clients in a federated manner. In this work, we initiate thestudy of federated synthetic tabular data generation. Building upon a SOTAcentral method known as AIM, we present DistAIM and FLAIM. We show it isstraightforward to distribute AIM, extending a recent approach based on securemulti-party computation which necessitates additional overhead, making it lesssuited to federated scenarios. We then demonstrate that naively federating AIMcan lead to substantial degradation in utility under the presence ofheterogeneity. To mitigate both issues, we propose an augmented FLAIM approachthat maintains a private proxy of heterogeneity. We simulate our methods acrossa range of benchmark datasets under different degrees of heterogeneity and showthis can improve utility while reducing overhead.

FLAIM: AIM-based Synthetic Data Generation in the Federated Setting