# Distributed Stochastic Multi-Task Learning with Graph Regularization

arXiv: https://arxiv.org/abs/1802.03830 · HTML: https://mlsanity.com/paper/1802.03830/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2018.

## Built on by (most cited first, 6 of 6)

- [Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques](https://mlsanity.com/paper/2102.09743/) (2021)
- [An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians](https://mlsanity.com/paper/2102.05320/) (2021)
- [GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning](https://mlsanity.com/paper/2105.01231/) (2021)
- [PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities](https://mlsanity.com/paper/2303.02532/) (2023)
- [Decentralized Personalized Federated Learning for Min-Max Problems](https://mlsanity.com/paper/2106.07289/) (2021)
- [Personalized Federated Learning with Multiple Known Clusters](https://mlsanity.com/paper/2204.13619/) (2022)

## Abstract (arXiv)

We propose methods for distributed graph-based multi-task learning that are based on weighted averaging of messages from other machines. Uniform averaging or diminishing stepsize in these methods would yield consensus (single task) learning. We show how simply skewing the averaging weights or controlling the stepsize allows learning different, but related, tasks on the different machines.