# Competing Adaptive Networks

arXiv: https://arxiv.org/abs/2103.15664 · HTML: https://mlsanity.com/paper/2103.15664/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 17 of 17)

- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization](https://mlsanity.com/paper/1404.6264/) (2014)
- [NEXT: In-Network Nonconvex Optimization](https://mlsanity.com/paper/1602.00591/) (2016)
- [Multitask learning over graphs: An Approach for Distributed, Streaming Machine Learning](https://mlsanity.com/paper/2001.02112/) (2020)
- [Distributed Learning in Non-Convex Environments -- Part I: Agreement at a Linear Rate](https://mlsanity.com/paper/1907.01848/) (2019)
- [On the Learning Behavior of Adaptive Networks - Part I: Transient Analysis](https://mlsanity.com/paper/1312.7581/) (2013)
- [Exact Diffusion for Distributed Optimization and Learning --- Part II: Convergence Analysis](https://mlsanity.com/paper/1702.05142/) (2017)
- [Distributed stochastic optimization with gradient tracking over strongly-connected networks](https://mlsanity.com/paper/1903.07266/) (2019)
- [Social Learning over Weakly-Connected Graphs](https://mlsanity.com/paper/1609.03703/) (2016)
- [Distributed Learning for Stochastic Generalized Nash Equilibrium Problems](https://mlsanity.com/paper/1608.00039/) (2016)
- [Primal-dual methods for large-scale and distributed convex optimization and data analytics](https://mlsanity.com/paper/1912.08546/) (2019)
- [Information Exchange and Learning Dynamics over Weakly-Connected Adaptive Networks](https://mlsanity.com/paper/1412.1523/) (2014)
- [Distributed Algorithms for Aggregative Games on Graphs](https://mlsanity.com/paper/1605.00267/) (2016)
- [Distributed Nash Equilibrium Seeking under Partial-Decision Information via the Alternating Direction Method of Multipliers](https://mlsanity.com/paper/1707.01965/) (2017)
- [On the linear convergence of distributed Nash equilibrium seeking for multi-cluster games under partial-decision information](https://mlsanity.com/paper/2005.06923/) (2020)
- [Geometric Convergence of Distributed Gradient Play in Games with Unconstrained Action Sets](https://mlsanity.com/paper/1907.07144/) (2019)
- [Gradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games](https://mlsanity.com/paper/2102.09406/) (2021)

## Built on by (most cited first, 1 of 1)

- [Diffusion Stochastic Learning Over Adaptive Competing Networks](https://mlsanity.com/paper/2504.19635/) (2025)

## Abstract (arXiv)

Adaptive networks have the capability to pursue solutions of global stochastic optimization problems by relying only on local interactions within neighborhoods. The diffusion of information through repeated interactions allows for globally optimal behavior, without the need for central coordination. Most existing strategies are developed for cooperative learning settings, where the objective of the network is common to all agents. We consider in this work a team setting, where a subset of the agents form a team with a common goal while competing with the remainder of the network. We develop an algorithm for decentralized competition among teams of adaptive agents, analyze its dynamics and present an application in the decentralized training of generative adversarial neural networks.