# Byzantine-Robust Gossip: Insights from a Dual Approach

arXiv: https://arxiv.org/abs/2405.03449 · HTML: https://mlsanity.com/paper/2405.03449/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 36 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 24 of 24)

- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [A Little Is Enough: Circumventing Defenses For Distributed Learning](https://mlsanity.com/paper/1902.06156/) (2019)
- [RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets](https://mlsanity.com/paper/1811.03761/) (2018)
- [Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation](https://mlsanity.com/paper/1903.03936/) (2019)
- [Learning from History for Byzantine Robust Optimization](https://mlsanity.com/paper/2012.10333/) (2020)
- [Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing](https://mlsanity.com/paper/2006.09365/) (2020)
- [Optimal algorithms for smooth and strongly convex distributed optimization in networks](https://mlsanity.com/paper/1702.08704/) (2017)
- [Byzantine Stochastic Gradient Descent](https://mlsanity.com/paper/1803.08917/) (2018)
- [Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity](https://mlsanity.com/paper/2302.01772/) (2023)
- [Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums](https://mlsanity.com/paper/2205.12173/) (2022)
- [Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)](https://mlsanity.com/paper/2008.00742/) (2020)
- [BRIDGE: Byzantine-resilient Decentralized Gradient Descent](https://mlsanity.com/paper/1908.08098/) (2019)
- [Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization](https://mlsanity.com/paper/2006.11773/) (2020)
- [Byzantine-Robust Decentralized Learning via ClippedGossip](https://mlsanity.com/paper/2202.01545/) (2022)
- [A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks](https://mlsanity.com/paper/1809.00710/) (2018)
- [Byzantine-Resilient Decentralized Stochastic Optimization with Robust Aggregation Rules](https://mlsanity.com/paper/2206.04568/) (2022)
- [An Optimal Algorithm for Decentralized Finite Sum Optimization](https://mlsanity.com/paper/2005.10675/) (2020)
- [Dual-Free Stochastic Decentralized Optimization with Variance Reduction](https://mlsanity.com/paper/2006.14384/) (2020)
- [Secure Distributed Training at Scale](https://mlsanity.com/paper/2106.11257/) (2021)
- [Robust Collaborative Learning with Linear Gradient Overhead](https://mlsanity.com/paper/2209.10931/) (2022)
- [Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives](https://mlsanity.com/paper/1810.02660/) (2018)
- [Iterative Byzantine Vector Consensus in Incomplete Graphs](https://mlsanity.com/paper/1307.2483/) (2013)
- [Primal-dual methods for large-scale and distributed convex optimization and data analytics](https://mlsanity.com/paper/1912.08546/) (2019)
- [On the Geometric Convergence of Byzantine-Resilient Distributed Optimization Algorithms](https://mlsanity.com/paper/2305.10810/) (2023)

## Built on by (most cited first, 1 of 1)

- [Robust and Efficient Collaborative Learning](https://mlsanity.com/paper/2510.08311/) (2025)

## Abstract (arXiv)

Distributed learning has many computational benefits but is vulnerable to attacks from a subset of devices transmitting incorrect information. This paper investigates Byzantine-resilient algorithms in a decentralized setting, where devices communicate directly in a peer-to-peer manner within a communication network. We leverage the so-called dual approach for decentralized optimization and propose a Byzantine-robust algorithm. We provide convergence guarantees in the average consensus subcase, discuss the potential of the dual approach beyond this subcase, and re-interpret existing algorithms using the dual framework. Lastly, we experimentally show the soundness of our method.