# Tutorial on dynamic average consensus: the problem, its applications, and the algorithms

arXiv: https://arxiv.org/abs/1803.04628 · HTML: https://mlsanity.com/paper/1803.04628/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2018.

## Built on by (most cited first, 9 of 9)

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- [Networked Signal and Information Processing](https://mlsanity.com/paper/2210.13767/) (2022)
- [The Effectiveness of Local Updates for Decentralized Learning under Data Heterogeneity](https://mlsanity.com/paper/2403.15654/) (2024)
- [A survey on secure decentralized optimization and learning](https://mlsanity.com/paper/2408.08628/) (2024)
- [MAMRL: Exploiting Multi-agent Meta Reinforcement Learning in WAN Traffic Engineering](https://mlsanity.com/paper/2111.15087/) (2021)
- [Learning to Identify Graphs from Node Trajectories in Multi-Robot Networks](https://mlsanity.com/paper/2307.04374/) (2023)
- [Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression](https://mlsanity.com/paper/2402.03174/) (2024)
- [Robust synchronization and policy adaptation for networked heterogeneous agents](https://mlsanity.com/paper/2409.03273/) (2024)
- [A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimation](https://mlsanity.com/paper/2606.20955/) (2026)

## Abstract (arXiv)

This paper considers the problem of dynamic average consensus algorithm design for a group of communicating agents. This problem consists of designing a distributed algorithm that enables a group of agents with communication and computation capabilities to use local interactions to track the average of locally time-varying reference signals at each agent. The objective of this article is to provide an overview of the dynamic average consensus problem that serves as a comprehensive introduction to the problem definition, its applications, and the distributed methods available to solve them. Our primary intention, rather than providing a full account of all the available literature, is to introduce the reader, in a tutorial fashion, to the main ideas behind dynamic average consensus algorithms, the performance trade-offs considered in their design, and the requirements needed for their analysis and convergence guarantees.