Search papers, labs, and topics across Lattice.
2
0
3
6
Noise-aware shrinkage can enhance the utility of differentially private fine-tuning, outperforming traditional methods without compromising privacy or efficiency.
Geometry-consistent aggregation can boost federated learning accuracy by over 2% while maintaining differential privacy.
