Carla Fabiana Chiasserini

Forget choosing between differential privacy and homomorphic encryption in federated learning – this round-based interleaving strategy dynamically combines them with synthetic data for tunable privacy-quality-efficiency trade-offs.

By cleverly interleaving homomorphic encryption with synthetic data training in federated learning, this work achieves a sweet spot: better accuracy *and* lower encryption costs.