On the dynamics of multi agent nonlinear filtering and learning

TL;DR

This paper investigates the behavior of multiagent systems with nonlinear filtering and learning dynamics, providing a general framework and conditions for cohesive learning, with applications in distributed and federated learning.

Contribution

It introduces a general formulation for agent actions in multiagent systems and establishes conditions for cohesive learning behavior, applicable to distributed and federated learning.

Findings

Framework for multiagent nonlinear filtering/learning dynamics

Conditions for achieving cohesive learning behavior

Application to distributed and federated learning scenarios

Abstract

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.