Privacy-Preserving Incremental ADMM for Decentralized Consensus Optimization

Yu Ye, Hao Chen, Ming Xiao, Mikael Skoglund, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


The alternating direction method of multipliers (ADMM) has been recently recognized as a promising optimizer for large-scale machine learning models. However, there are very few results studying ADMM from the aspect of communication costs, especially jointly with privacy preservation, which are critical for distributed learning. We investigate the communication efficiency and privacy-preservation of ADMM by solving the consensus optimization problem over decentralized networks. Since walk algorithms can reduce communication load, we first propose incremental ADMM (I-ADMM) based on the walk algorithm, the updating order of which follows a Hamiltonian cycle instead. However, I-ADMM cannot guarantee the privacy for agents against external eavesdroppers even if the randomized initialization is applied. To protect privacy for agents, we then propose two privacy-preserving incremental ADMM algorithms, i.e., PI-ADMM1 and PI-ADMM2, where perturbation over step sizes and primal variables is adopted, respectively. Through theoretical analyses, we prove the convergence and privacy preservation for PI-ADMM1, which are further supported by numerical experiments. Besides, simulations demonstrate that the proposed PI-ADMM1 and PI-ADMM2 algorithms are communication efficient compared with state-of-the-art methods.

Original languageEnglish (US)
Article number9214458
Pages (from-to)5842-5854
Number of pages13
JournalIEEE Transactions on Signal Processing
StatePublished - 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


  • Decentralized optimization
  • alternating direction method of multipliers (ADMM)
  • privacy preservation


Dive into the research topics of 'Privacy-Preserving Incremental ADMM for Decentralized Consensus Optimization'. Together they form a unique fingerprint.

Cite this