# A Survey on Fault-tolerance in Distributed Optimization and Machine Learning

arXiv: https://arxiv.org/abs/2106.08545 · HTML: https://mlsanity.com/paper/2106.08545/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2021.

## Built on by (most cited first, 5 of 5)

- [Robust Collaborative Learning with Linear Gradient Overhead](https://mlsanity.com/paper/2209.10931/) (2022)
- [A Survey on Distributed Online Optimization and Game](https://mlsanity.com/paper/2205.00473/) (2022)
- [Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data](https://mlsanity.com/paper/2403.03871/) (2024)
- [Utilizing Redundancy in Cost Functions for Resilience in Distributed Optimization and Learning](https://mlsanity.com/paper/2110.10858/) (2021)
- [Impact of Redundancy on Resilience in Distributed Optimization and Learning](https://mlsanity.com/paper/2211.08622/) (2022)

## Abstract (arXiv)

The robustness of distributed optimization is an emerging field of study, motivated by various applications of distributed optimization including distributed machine learning, distributed sensing, and swarm robotics. With the rapid expansion of the scale of distributed systems, resilient distributed algorithms for optimization are needed, in order to mitigate system failures, communication issues, or even malicious attacks. This survey investigates the current state of fault-tolerance research in distributed optimization, and aims to provide an overview of the existing studies on both fault-tolerant distributed optimization theories and applicable algorithms.