mlsanityData to 14 Oct 2026

Byzantine-Robust Gossip: Insights from a Dual Approach

arXiv 2405.03449 ↗First posted 6 May 2024cs.LG stat.MLcited by 1

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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.

Key facts. Cited by 1 paper 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

24 of 36 references.

References · 24

  1. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  2. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 288
  3. 2018RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasetscited by 149
  4. 2019Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulationcited by 141
  5. 2020Learning from History for Byzantine Robust Optimizationcited by 137
  6. 2020Byzantine-Robust Learning on Heterogeneous Datasets via Bucketingcited by 128
  7. 2017Optimal algorithms for smooth and strongly convex distributed optimization in networkscited by 83
  8. 2018Byzantine Stochastic Gradient Descentcited by 81
  9. 2023Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneitycited by 65
  10. 2022Byzantine Machine Learning Made Easy by Resilient Averaging of Momentumscited by 63
  11. 2020Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)cited by 54
  12. 2019BRIDGE: Byzantine-resilient Decentralized Gradient Descentcited by 45
12 more
  1. 2020Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimizationcited by 43
  2. 2022Byzantine-Robust Decentralized Learning via ClippedGossipcited by 31
  3. 2018A Dual Approach for Optimal Algorithms in Distributed Optimization over Networkscited by 27
  4. 2022Byzantine-Resilient Decentralized Stochastic Optimization with Robust Aggregation Rulescited by 23
  5. 2020An Optimal Algorithm for Decentralized Finite Sum Optimizationcited by 19
  6. 2020Dual-Free Stochastic Decentralized Optimization with Variance Reductioncited by 12
  7. 2021Secure Distributed Training at Scalecited by 12
  8. 2022Robust Collaborative Learning with Linear Gradient Overheadcited by 12
  9. 2018Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectivescited by 11
  10. 2013Iterative Byzantine Vector Consensus in Incomplete Graphscited by 6
  11. 2019Primal-dual methods for large-scale and distributed convex optimization and data analyticscited by 6
  12. 2023On the Geometric Convergence of Byzantine-Resilient Distributed Optimization Algorithmscited by 6

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  1. 2025Robust and Efficient Collaborative Learningcited by 0