论文标题
沟通效率的分布式分布式牛顿般的优化，具有梯度和M测验器
Communication-efficient Distributed Newton-like Optimization with Gradients and M-estimators
论文作者
论文摘要
在现代数据科学中，很常见的是，大规模数据在许多位置进行了存储和处理。出于包括机密性问题在内的原因，只有每个平行中心的有限数据信息才有资格转移。为了更有效地解决这些问题，正在积极开发一组沟通效率的方法。我们提出了两种通信效率的牛顿型算法，结合了M估计器和从每个数据中心收集的梯度。它们是通过在全球范围内使用这些沟通高效统计数据构建两个Fisher信息估计器来创建的。享受较高的融合速度，该框架在现有的类似牛顿的方法上有所改善。此外，我们提出了两个偏置调整后的一步分布式估计器。当中心样本量的平方比中心总数更大时，它们的效率与全球$ m $ $估计器的效率均匀。我们方法的优势是通过广泛的理论和经验证据来说明的。
In modern data science, it is common that large-scale data are stored and processed parallelly across a great number of locations. For reasons including confidentiality concerns, only limited data information from each parallel center is eligible to be transferred. To solve these problems more efficiently, a group of communication-efficient methods are being actively developed. We propose two communication-efficient Newton-type algorithms, combining the M-estimator and the gradient collected from each data center. They are created by constructing two Fisher information estimators globally with those communication-efficient statistics. Enjoying a higher rate of convergence, this framework improves upon existing Newton-like methods. Moreover, we present two bias-adjusted one-step distributed estimators. When the square of the center-wise sample size is of a greater magnitude than the total number of centers, they are as efficient as the global $M$-estimator asymptotically. The advantages of our methods are illustrated by extensive theoretical and empirical evidences.
