A New Perspective on Randomized Gossip Algorithms
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Abstract (arXiv)
In this short note we propose a new approach for the design and analysis of randomized gossip algorithms which can be used to solve the average consensus problem. We show how that Randomized Block Kaczmarz (RBK) method - a method for solving linear systems - works as gossip algorithm when applied to a special system encoding the underlying network. The famous pairwise gossip algorithm arises as a special case. Subsequently, we reveal a hidden duality of randomized gossip algorithms, with the dual iterative process maintaining a set of numbers attached to the edges as opposed to nodes of the network. We prove that RBK obtains a superlinear speedup in the size of the block, and demonstrate this effect through experiments.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Oct 2016.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2023Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditionscited by 14
- 2023Towards a Better Theoretical Understanding of Independent Subnetwork Trainingcited by 6
- 2022Enabling All In-Edge Deep Learning: A Literature Reviewcited by 4
- 2026On Gossip Algorithms for Machine Learning with Pairwise Objectivescited by 0
- 2026On Gossip Algorithms for Machine Learning with Pairwise Objectives25 Mar 2026
- 2023Towards a Better Theoretical Understanding of Independent Subnetwork Training28 Jun 2023
- 2023Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions27 Feb 2023
- 2022Enabling All In-Edge Deep Learning: A Literature Review7 Apr 2022