Distributed Networked Multi-task Learning

Lingzhou Hong; Alfredo Garcia

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arXiv · 2410.03403

Distributed Networked Multi-task Learning

Lingzhou Hong·Alfredo Garcia

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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.

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Lingzhou Hong, Alfredo Garcia. 2024-10-04. Distributed Networked Multi-task Learning. https://arxiv.org/abs/2410.03403

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