SSTQ:Privacy-Preserving Vector Quantization via Subsampled Stochastic TurboQuant

Achieving local differential privacy in distributed optimization while maintaining low communication cost remains challenging.

Achieving local differential privacy in distributed optimization while maintaining low communication cost remains challenging. Existing vector quantization methods, such as vqSGD, use high-dimensional geometric constructions but incur unfavorable dimension-dependent variance. In…