DP-FedTabDiff wraps an existing federated tabular diffusion model with per-client DP-SGD and reports how the privacy budget, number of clients, and local update count affect synthetic data quality and empirical privacy risk.
Communication-Efficient Learning of Deep Networks from Decentral- ized Data,
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Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis
DP-FedTabDiff wraps an existing federated tabular diffusion model with per-client DP-SGD and reports how the privacy budget, number of clients, and local update count affect synthetic data quality and empirical privacy risk.