# Distributed Stochastic Zeroth-Order Optimization with Compressed Communication

arXiv: https://arxiv.org/abs/2503.17429 · HTML: https://mlsanity.com/paper/2503.17429/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2025.

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

- [Gradient Manipulation in Distributed Stochastic Gradient Descent with Strategic Agents: Truthful Incentives with Convergence Guarantees](https://mlsanity.com/paper/2603.27962/) (2026)
- [SPADE-DFL: Communication-Efficient Decentralized Federated Learning via Derivative-Free Linearized ADMM](https://mlsanity.com/paper/2609.29446/) (2026)

## Abstract (arXiv)

The dual challenges of prohibitive communication overhead and the impracticality of gradient computation due to data privacy or black-box constraints in distributed systems motivate this work on communication-constrained gradient-free optimization. We propose a stochastic distributed zeroth-order algorithm (Com-DSZO) requiring only two function evaluations per iteration, integrated with general compression operators. Rigorous analysis establishes its sublinear convergence rate for both smooth and nonsmooth objectives, while explicitly elucidating the compression-convergence trade-off. Furthermore, we develop a variance-reduced variant (VR-Com-DSZO) under stochastic mini-batch feedback. The empirical algorithm performance are illustrated with numerical examples.