Tutorial on dynamic average consensus: the problem, its applications, and the algorithms
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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.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Mar 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 4; 2025: 0; 2026: 1.
9 papers.
- 2022Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graphcited by 11
- 2022Networked Signal and Information Processingcited by 10
- 2024The Effectiveness of Local Updates for Decentralized Learning under Data Heterogeneitycited by 7
- 2024A survey on secure decentralized optimization and learningcited by 4
- 2021MAMRL: Exploiting Multi-agent Meta Reinforcement Learning in WAN Traffic Engineeringcited by 1
- 2023Learning to Identify Graphs from Node Trajectories in Multi-Robot Networkscited by 1
- 2024Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regressioncited by 0
- 2024Robust synchronization and policy adaptation for networked heterogeneous agentscited by 0
- 2026A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimationcited by 0
- 2026A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimation18 Jun 2026
- 2024Robust synchronization and policy adaptation for networked heterogeneous agents5 Sep 2024
- 2024A survey on secure decentralized optimization and learning16 Aug 2024
- 2024The Effectiveness of Local Updates for Decentralized Learning under Data Heterogeneity23 Mar 2024
- 2024Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression5 Feb 2024
- 2023Learning to Identify Graphs from Node Trajectories in Multi-Robot Networks10 Jul 2023
- 2022Networked Signal and Information Processing25 Oct 2022
- 2022Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph1 Oct 2022
- 2021MAMRL: Exploiting Multi-agent Meta Reinforcement Learning in WAN Traffic Engineering30 Nov 2021