K. Hossain

FedDP-PALD: A Privacy-Preserving Federated Latent Diffusion Framework with Prototype Aggregation for Medical Data Synthesis

Results show that FedDP-PALD generates private synthetic representations that preserve useful decision performance while strongly resisting membership inference, and introduces Differentially Private Prototype Mixture Aggregation (DP-PMA), which clips class-level latent prototypes and adds calibrated Gaussian noise before combining them on the server to maintain differential privacy.