Distributed Networked Multi-task Learning
Abstract
We consider a distributed multi-task learning scheme that accounts for multiple linear model estimation tasks with heterogeneous and/or correlated data streams. We assume that nodes can be partitioned into groups corresponding to different learning tasks and communicate according to a directed network topology. Each node estimates a linear model asynchronously and is subject to local (within-group) regularization and global (across groups) regularization terms targeting noise reduction and generalization performance improvement respectively. We provide a finite-time characterization of convergence of the estimators and task relation and illustrate the scheme's general applicability in two examples: random field temperature estimation and modeling student performance from different academic districts.
Cite
@article{arxiv.2410.03403,
title = {Distributed Networked Multi-task Learning},
author = {Lingzhou Hong and Alfredo Garcia},
journal= {arXiv preprint arXiv:2410.03403},
year = {2024}
}