# Randomized Kaczmarz in Adversarial Distributed Setting

## TL;DR

This paper introduces an adversary-tolerant distributed optimization method based on randomized Kaczmarz, demonstrating its effectiveness in convex problems with adversarial workers through simulations.

## Contribution

It proposes a novel iterative approach that ensures convergence and robustness in distributed convex optimization under adversarial conditions.

## Key findings

- Method converges despite adversarial workers
- High accuracy in identifying adversarial workers
- Effective in various adversary rate scenarios

## Abstract

Developing large-scale distributed methods that are robust to the presence of adversarial or corrupted workers is an important part of making such methods practical for real-world problems. In this paper, we propose an iterative approach that is adversary-tolerant for convex optimization problems. By leveraging simple statistics, our method ensures convergence and is capable of adapting to adversarial distributions. Additionally, the efficiency of the proposed methods for solving convex problems is shown in simulations with the presence of adversaries. Through simulations, we demonstrate the efficiency of our approach in the presence of adversaries and its ability to identify adversarial workers with high accuracy and tolerate varying levels of adversary rates.