Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks

arXiv:2404.03227v1 Announce Type: cross
Abstract: We address the challenge of sampling and remote estimation for autoregressive Markovian processes in a multi-hop wireless network with statistically-identical agents. Agents cache the most recent samples from others and communicate over wireless collision channels governed by an underlying graph topology. Our goal is to minimize time-average estimation error and/or age of information with decentralized scalable sampling and transmission policies, considering both oblivious (where decision-making is independent of the physical processes) and non-oblivious policies (where …

abstract agents arxiv cache challenge channels collision cs.lg decentralized eess.sp error graph graph neural networks network networks neural networks processes samples sampling strategies topology type wireless