Distributed Networked Multi-task Learning
<|reference_start|>Distributed Networked Multi-task Learning: 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.<|reference_end|>
@article{hong2024distributed,
    title={Distributed Networked Multi-task Learning},
    author={Lingzhou Hong, Alfredo Garcia},
    journal={arXiv preprint arXiv:2410.03403},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03403},
    primaryClass={cs.MA cs.LG}
}