# Privacy-preserving Decentralized Optimization via Decomposition

arXiv: https://arxiv.org/abs/1808.09566 · HTML: https://mlsanity.com/paper/1808.09566/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2018.

## Built on by (most cited first, 2 of 2)

- [A survey on secure decentralized optimization and learning](https://mlsanity.com/paper/2408.08628/) (2024)
- [Privacy-Preserving Distributed Optimization and Learning](https://mlsanity.com/paper/2403.00157/) (2024)

## Abstract (arXiv)

This paper considers the problem of privacy-preservation in decentralized optimization, in which N agents cooperatively minimize a global objective function that is the sum of N local objective functions. We assume that each local objective function is private and only known to an individual agent. To cooperatively solve the problem, most existing decentralized optimization approaches require participating agents to exchange and disclose estimates to neighboring agents. However, this results in leakage of private information about local objective functions, which is undesirable when adversaries exist and try to steal information from participating agents. To address this issue, we propose a privacy-preserving decentralized optimization approach based on proximal Jacobian ADMM via function decomposition. Numerical simulations confirm the effectiveness of the proposed approach.