SSTQ:Privacy-Preserving Vector Quantization via Subsampled Stochastic TurboQuant
SSTQ targets the privacy, variance, and bandwidth tradeoff in distributed vector quantization.
The paper proposes Subsampled Stochastic TurboQuant, combining tight frames, coordinate subsampling, and privacy-aware one-dimensional quantization. It describes two variants: Flat Randomized Response and Metric-Aware Laplace, with the latter aimed at higher bit-width codebooks. The authors claim optimal mean squared error scaling using `ceil(log2 N) + b` bits per client, with `N = Theta(d)`. They also report CIFAR-10 and Fashion-MNIST federated learning tests showing favorable utility and communication efficiency against baselines. ArXiv · AI/CL/LG's note
The paper proposes Subsampled Stochastic TurboQuant, combining tight frames, coordinate subsampling, and privacy-aware one-dimensional quantization. It describes two variants: Flat Randomized Response and Metric-Aware Laplace, with the latter aimed at higher bit-width codebooks. The authors claim optimal mean squared error scaling using `ceil(log2 N) + b` bits per client, with `N = Theta(d)`. They also report CIFAR-10 and Fashion-MNIST federated learning tests showing favorable utility and communication efficiency against baselines. ArXiv · AI/CL/LG's note
score 4