Communication efficient privacy-preserving distributed optimization using adaptive differential quantization

Qiongxiu Li*, Richard Heusdens, Mads Græsbøll Christensen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
102 Downloads (Pure)


Privacy issues and communication cost are both major concerns in distributed optimization in networks. There is often a trade-off between them because the encryption methods used for privacy-preservation often require expensive communication overhead. To address these issues, we, in this paper, propose a quantization-based approach to achieve both communication efficient and privacy-preserving solutions in the context of distributed optimization. By deploying an adaptive differential quantization scheme, we allow each node in the network to achieve its optimum solution with a low communication cost while keeping its private data unrevealed. Additionally, the proposed approach is general and can be applied in various distributed optimization methods, such as the primal-dual method of multipliers (PDMM) and the alternating direction method of multipliers (ADMM). We consider two widely used adversary models, passive and eavesdropping, and investigate the properties of the proposed approach using different applications and demonstrate its superior performance compared to existing privacy-preserving approaches in terms of both accuracy and communication cost.

Original languageEnglish
Article number108456
JournalSignal Processing
Publication statusPublished - 2022


  • ADMM
  • Communication cost
  • Distributed optimization
  • Information-theoretic
  • PDMM
  • Privacy
  • Quantization


Dive into the research topics of 'Communication efficient privacy-preserving distributed optimization using adaptive differential quantization'. Together they form a unique fingerprint.

Cite this