SSTQ:Privacy-Preserving Vector Quantization via Subsampled Stochastic TurboQuant
By Adel Javanmard · Paper · cs.LG
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