FedDP-PALD: Privacy-Preserving Federated Latent Diffusion for Medical Data Synthesis
Researchers introduce FedDP-PALD, a federated latent diffusion framework designed to generate synthetic medical images and ECG signals with formal differential privacy guarantees. The approach uses prototype aggregation with calibrated noise to protect against membership inference attacks while maintaining diagnostic utility, achieving F1 scores and AUROC values close to those obtained with real data. The method is evaluated on multiple medical datasets and demonstrates strong privacy protection with minimal loss in predictive performance.
Why it matters: This work offers a significant advance in privacy-preserving synthetic data generation for medical applications, enabling collaborative model training across institutions without compromising patient privacy.
Full story at: arXiv Computer Vision ↗