How WASP Generates High‑Quality DP Synthetic Data with Multi‑Model Collaboration
WASP is a privacy‑preserving framework that fuses multiple pretrained language models through a weighted Top‑Q voting scheme to synthesize differential‑private data, dramatically improving downstream task performance even when only a few private samples are available, and it scales to federated settings.
