# Zeroth-Order Stochastic Coordinate Methods for Decentralized Non-convex Optimization

arXiv: https://arxiv.org/abs/2204.04743 · HTML: https://mlsanity.com/paper/2204.04743/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2022.

## Built on by (most cited first, 1 of 1)

- [Heterogeneous Federated Learning with Splited Language Model](https://mlsanity.com/paper/2403.16050/) (2024)

## Abstract (arXiv)

In this letter, we first propose a underlineZeroth-underlineOrder cunderlineOordinate underlineMethod~(ZOOM) to solve the stochastic optimization problem over a decentralized network with only zeroth-order~(ZO) oracle feedback available. Moreover, we equip a simple mechanism "powerball" to ZOOM and propose ZOOM-PB to accelerate the convergence of ZOOM. Compared with the existing methods, we verify the proposed algorithms through two benchmark examples in the literature, namely the black-box binary classification and the generating adversarial examples from black-box DNNs in order to compare with the existing state-of-the-art centralized and distributed ZO algorithms. The numerical results demonstrate a faster convergence rate of the proposed algorithms.