# Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance

arXiv: https://arxiv.org/abs/1805.10032 · HTML: https://mlsanity.com/paper/1805.10032/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2018.

## Built on by (most cited first, 8 of 8)

- [Robust Federated Learning with Attack-Adaptive Aggregation](https://mlsanity.com/paper/2102.05257/) (2021)
- [Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning](https://mlsanity.com/paper/2404.17617/) (2024)
- [BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackers](https://mlsanity.com/paper/2110.09660/) (2021)
- [Towards cost-effective and resource-aware aggregation at Edge for Federated Learning](https://mlsanity.com/paper/2204.07767/) (2022)
- [FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders](https://mlsanity.com/paper/2211.13389/) (2022)
- [Probabilistic Inference for Learning from Untrusted Sources](https://mlsanity.com/paper/2101.06171/) (2021)
- [Utilizing Redundancy in Cost Functions for Resilience in Distributed Optimization and Learning](https://mlsanity.com/paper/2110.10858/) (2021)
- [A Robust Classification Framework for Byzantine-Resilient Stochastic Gradient Descent](https://mlsanity.com/paper/2301.07498/) (2023)

## Abstract (arXiv)

We present Zeno, a technique to make distributed machine learning, particularly Stochastic Gradient Descent (SGD), tolerant to an arbitrary number of faulty workers. Zeno generalizes previous results that assumed a majority of non-faulty nodes; we need assume only one non-faulty worker. Our key idea is to suspect workers that are potentially defective. Since this is likely to lead to false positives, we use a ranking-based preference mechanism. We prove the convergence of SGD for non-convex problems under these scenarios. Experimental results show that Zeno outperforms existing approaches.