2 papers
cs.LG2025
Private Evolution Converges
Tomás González, Giulia Fanti, Aaditya Ramdas
Private Evolution (PE) is a promising training-free method for differentially private (DP) synthetic data generation. While it achieves strong performance in some domains (e.g., im…
cs.LG2025
POPri: Private Federated Learning using Preference-Optimized Synthetic Data
Charlie Hou, Mei-Yu Wang, Yige Zhu +2
In practical settings, differentially private Federated learning (DP-FL) is the dominant method for training models from private, on-device client data. Recent work has suggested t…