Communication-Efficient Distributed Statistical Inference

Michael I. Jordan, Jason D. Lee, Yun Yang

Research output: Contribution to journalArticlepeer-review

220 Scopus citations


We present a communication-efficient surrogate likelihood (CSL) framework for solving distributed statistical inference problems. CSL provides a communication-efficient surrogate to the global likelihood that can be used for low-dimensional estimation, high-dimensional regularized estimation, and Bayesian inference. For low-dimensional estimation, CSL provably improves upon naive averaging schemes and facilitates the construction of confidence intervals. For high-dimensional regularized estimation, CSL leads to a minimax-optimal estimator with controlled communication cost. For Bayesian inference, CSL can be used to form a communication-efficient quasi-posterior distribution that converges to the true posterior. This quasi-posterior procedure significantly improves the computational efficiency of Markov chain Monte Carlo (MCMC) algorithms even in a nondistributed setting. We present both theoretical analysis and experiments to explore the properties of the CSL approximation. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)668-681
Number of pages14
JournalJournal of the American Statistical Association
Issue number526
StatePublished - Apr 3 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • Communication efficiency
  • Distributed inference
  • Likelihood approximation


Dive into the research topics of 'Communication-Efficient Distributed Statistical Inference'. Together they form a unique fingerprint.

Cite this