On the dynamics of multi agent nonlinear filtering and learning
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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.
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7 of 35 references.
References · 7
- 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
- 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
- 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
- 2019Distributed Learning in Non-Convex Environments -- Part I: Agreement at a Linear Ratecited by 22
- 2022On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inferencecited by 7
- 2021On Stability and Convergence of Distributed Filterscited by 2
- 2022Distributed Bayesian Learning of Dynamic Statescited by 2
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