SSTQ:Privacy-Preserving Vector Quantization via Subsampled Stochastic TurboQuant
Achieving local differential privacy in distributed optimization while maintaining low communication cost remains challenging. Existing vector quantization methods, such as vqSGD, rely on high-dimensional geometric constructions but incur unfavorable dimension-dependent variance. In this work, we propose Subsampled Stochastic TurboQuant (SSTQ), a framework that combines a bounded Kashin representation, data-independent coordinate subsampling, and privacy-aware one-dimensional quantization. SSTQ includes two variants: (1) a Flat Randomized Response variant that is unbiased and, for a fixed codebook bit-width, frame redundancy, and dimension-independent Kashin level, achieves reconstruction...
| Authors | Adel Javanmard, David P. Woodruff, Vahab Mirrokni |
|---|---|
| Primary category | cs.LG |
| Published | 5 August 2026 |
| Last updated at source | 28 September 2026 |
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