# Technical Program

## Paper Detail

 Paper ID L.3.1 Paper Title Communication Efficient Distributed Approximate Newton Method Authors Avishek Ghosh, University of California, Berkeley, United States; Raj Kumar Maity, Arya Mazumdar, University of Massachusetts, Amherst, United States; Kannan Ramachandran, University of California, Berkeley, United States Session L.3: Distributed Learning Performance Analysis Presentation Lecture Track Statistics and Learning Theory Manuscript Click here to download the manuscript Virtual Presentation Click here to watch in the Virtual Symposium Abstract In this paper, we develop a communication efficient second order distributed Newton-type algorithm. For communication efficiency, we consider a generic class of $\delta$-approximate compressors (Karimireddy et al., 2019), which includes \emph{sign}-based compression and top-$k$ sparsification. We provide three potential settings where compression can be employed; and provide rate of convergence for smooth objectives. We show that, in the regime where $\delta$ is constant, our theoretical convergence rate matches that of a state-of-the-art distributed second order algorithm called \emph{DINGO} (Crane and Roosta, 2019). This implies that we get the compression for free in this regime. The full paper can be found at: https://tinyurl.com/ujnpt4c.