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.

翻译：多智能体系统旨在通过去中心化共识寻求动力学实现高度复杂的学习任务，其在信号处理与计算智能领域已引起广泛关注。本文研究了具有非线性滤波/学习动力学的多智能体网络系统的行为特性。为此，我们提出了多智能体网络系统中智能体行为的通用框架，并给出了实现协同学习行为的条件。重要的是，本文展示了所推导框架在分布式学习与联邦学习场景中的应用。