Research Scientist, Apple
3 papers at NeurIPS 2025
We introduce the first practical benchmark for FL with DP in ASR, combining theoretical insights on gradient heterogeneity with empirical results that demonstrate scalability and strong performance under user-level privacy guarantees.
We show that private vector aggregation can be done with sublinear communication in the two-server setting, with efficient protocols and zero-knowledge proofs.