Efficient Quantization Mean Estimation for Distributed Learning
针对有界数据，提出一种方差缩减的相关量化方案用于分布式均值估计，理论上降低了均方误差，并通过合成和真实数据实验验证了效果。
The increasing size of data has created a pressing need for protection of communication and data privacy, spurring 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 achieves a theoretical reduction in the mean square error for fixed and randomized designs compared to the correlated quantization method under different levels and dimensions scenarios. Several synthetic data experiments were conducted to illustrate the effectiveness of the approach and to provide a reliable approximation of the reduced mean square error based on the theory. The proposed method was also applied to real-world data in different learning tasks, which yielded promising results.