摘要：The increasing size of data has created a pressing need for communication and data privacy protection, which has spurred significant interest in quantization. This paper proposes a novel scheme for variance reduced correlated quantization that is designed for data with bounded support and distributed mean estimation. Our method is shown to achieve a theoretical reduction in mean square error for both fixed and randomized designs compared to the correlated quantization method under different levels and dimensions scenarios. We conducted several synthetic data experiments to illustrate the effectiveness of our approach and to provide a good approximation of the reduced mean square error based on our theory. We further applied our proposed method to real-world data with different learning tasks, and it produced promising results.