# On the dynamics of multi agent nonlinear filtering and learning

arXiv: https://arxiv.org/abs/2309.03557 · HTML: https://mlsanity.com/paper/2309.03557/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 7 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Distributed Learning in Non-Convex Environments -- Part I: Agreement at a Linear Rate](https://mlsanity.com/paper/1907.01848/) (2019)
- [On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference](https://mlsanity.com/paper/2204.13741/) (2022)
- [On Stability and Convergence of Distributed Filters](https://mlsanity.com/paper/2102.11250/) (2021)
- [Distributed Bayesian Learning of Dynamic States](https://mlsanity.com/paper/2212.02565/) (2022)

## Abstract (arXiv)

Multiagent systems aim to accomplish highly complex learning tasks through decentralised consensus seeking dynamics and their use has garnered a great deal of attention in the signal processing and computational intelligence societies. This article examines the behaviour of multiagent networked systems with nonlinear filtering/learning dynamics. To this end, a general formulation for the actions of an agent in multiagent networked systems is presented and conditions for achieving a cohesive learning behaviour is given. Importantly, application of the so derived framework in distributed and federated learning scenarios are presented.