# Randomized Kaczmarz in Adversarial Distributed Setting

arXiv: https://arxiv.org/abs/2302.14615 · HTML: https://mlsanity.com/paper/2302.14615/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 9 of 9)

- [Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching](https://mlsanity.com/paper/2009.02276/) (2020)
- [Thieves on Sesame Street! Model Extraction of BERT-based APIs](https://mlsanity.com/paper/1910.12366/) (2019)
- [Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2007.10760/) (2020)
- [Byzantine Stochastic Gradient Descent](https://mlsanity.com/paper/1803.08917/) (2018)
- [ByRDiE: Byzantine-resilient distributed coordinate descent for decentralized learning](https://mlsanity.com/paper/1708.08155/) (2017)
- [Straggler Mitigation in Distributed Optimization Through Data Encoding](https://mlsanity.com/paper/1711.04969/) (2017)
- [Information Laundering for Model Privacy](https://mlsanity.com/paper/2009.06112/) (2020)
- [Single Projection Kaczmarz Extended Algorithms](https://mlsanity.com/paper/1504.00231/) (2015)
- [Randomized Douglas-Rachford methods for linear systems: Improved accuracy and efficiency](https://mlsanity.com/paper/2207.04291/) (2022)

## Abstract (arXiv)

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.