# Distributed Black-Box Optimization via Error Correcting Codes

## TL;DR

This paper presents a distributed black-box optimization method using error correcting codes, improving resilience to stragglers and accelerating computation, especially for adversarial attacks on neural networks.

## Contribution

It introduces a novel coded search direction framework for distributed derivative-free optimization, extending evolution strategies with error correction capabilities.

## Key findings

- Significant reduction in computation times for black-box adversarial attacks
- Enhanced robustness to stragglers in distributed optimization
- Effective application to deep neural network attacks

## Abstract

We introduce a novel distributed derivative-free optimization framework that is resilient to stragglers. The proposed method employs coded search directions at which the objective function is evaluated, and a decoding step to find the next iterate. Our framework can be seen as an extension of evolution strategies and structured exploration methods where structured search directions were utilized. As an application, we consider black-box adversarial attacks on deep convolutional neural networks. Our numerical experiments demonstrate a significant improvement in the computation times.