A two-phase decentralized strategy using Gaussian Markov random field priors can estimate graph Laplacians from noisy stochastic gradient iterates and enable cooperative multitask learning

We propose a decentralized two-phase strategy that first estimates a generalized graph Laplacian from noisy non-cooperative stochastic gradient iterates, and subsequently exploits the learned graph to enable cooperative multitask diffusion learning. This framework is motivated by a Gaussian Markov random field prior, which gives rise to a decentralized maximum likelihood estimator for the graph Laplacian.

http://arxiv.org/abs/2608.26989v1