# On the Learning Behavior of Adaptive Networks - Part I: Transient Analysis

arXiv: https://arxiv.org/abs/1312.7581 · HTML: https://mlsanity.com/paper/1312.7581/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2013.

## Built on by (most cited first, 20 of 20)

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- [Improving the Transient Times for Distributed Stochastic Gradient Methods](https://mlsanity.com/paper/2105.04851/) (2021)
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- [Distributed Stochastic Optimization under a General Variance Condition](https://mlsanity.com/paper/2301.12677/) (2023)
- [Enforcing Privacy in Distributed Learning with Performance Guarantees](https://mlsanity.com/paper/2301.06412/) (2023)
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- [On the Trade-off between Flatness and Optimization in Distributed Learning](https://mlsanity.com/paper/2406.20006/) (2024)
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- [Compressed Regression over Adaptive Networks](https://mlsanity.com/paper/2304.03638/) (2023)
- [Competing Adaptive Networks](https://mlsanity.com/paper/2103.15664/) (2021)
- [Policy Evaluation in Decentralized POMDPs with Belief Sharing](https://mlsanity.com/paper/2302.04151/) (2023)
- [Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization](https://mlsanity.com/paper/2304.14024/) (2023)
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- [Exact Subspace Diffusion for Decentralized Multitask Learning](https://mlsanity.com/paper/2304.07358/) (2023)
- [Deep-Relative-Trust-Based Diffusion for Decentralized Deep Learning](https://mlsanity.com/paper/2501.03162/) (2025)
- [Diffusion Learning with Partial Agent Participation and Local Updates](https://mlsanity.com/paper/2505.11307/) (2025)
- [Row-Stochastic Matrices Can Provably Outperform Doubly Stochastic Matrices in Decentralized Learning](https://mlsanity.com/paper/2511.19513/) (2025)
- [Topology-Independent Robustness of the Weighted Mean under Label Poisoning Attacks in Heterogeneous Decentralized Learning](https://mlsanity.com/paper/2601.02682/) (2026)

## Abstract (arXiv)

This work carries out a detailed transient analysis of the learning behavior of multi-agent networks, and reveals interesting results about the learning abilities of distributed strategies. Among other results, the analysis reveals how combination policies influence the learning process of networked agents, and how these policies can steer the convergence point towards any of many possible Pareto optimal solutions. The results also establish that the learning process of an adaptive network undergoes three (rather than two) well-defined stages of evolution with distinctive convergence rates during the first two stages, while attaining a finite mean-square-error (MSE) level in the last stage. The analysis reveals what aspects of the network topology influence performance directly and suggests design procedures that can optimize performance by adjusting the relevant topology parameters. Interestingly, it is further shown that, in the adaptation regime, each agent in a sparsely connected network is able to achieve the same performance level as that of a centralized stochastic-gradient strategy even for left-stochastic combination strategies. These results lead to a deeper understanding and useful insights on the convergence behavior of coupled distributed learners. The results also lead to effective design mechanisms to help diffuse information more thoroughly over networks.