# Distributed Networked Multi-task Learning

arXiv: https://arxiv.org/abs/2410.03403 · HTML: https://mlsanity.com/paper/2410.03403/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Multi-Task Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2009.09796/) (2020)
- [A Survey on Distributed Machine Learning](https://mlsanity.com/paper/1912.09789/) (2019)
- [Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/1802.09941/) (2018)
- [An Overview on the Estimation of Large Covariance and Precision Matrices](https://mlsanity.com/paper/1504.02995/) (2015)
- [Distributed Multitask Learning](https://mlsanity.com/paper/1510.00633/) (2015)
- [Distributed Multi-Task Relationship Learning](https://mlsanity.com/paper/1612.04022/) (2016)
- [Multitask diffusion adaptation over asynchronous networks](https://mlsanity.com/paper/1412.1798/) (2014)
- [Ridge Estimation of Inverse Covariance Matrices from High-Dimensional Data](https://mlsanity.com/paper/1403.0904/) (2014)
- [A note on sum and difference of correlated chi-squared variables](https://mlsanity.com/paper/1906.09982/) (2019)
- [Distributed Networked Real-time Learning](https://mlsanity.com/paper/2009.04103/) (2020)

## Abstract (arXiv)

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.