# Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach

arXiv: https://arxiv.org/abs/1905.11549 · HTML: https://mlsanity.com/paper/1905.11549/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

## Built on by (most cited first, 4 of 4)

- [Adaptive and Robust Multi-Task Learning](https://mlsanity.com/paper/2202.05250/) (2022)
- [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)
- [Fast and Robust Sparsity Learning over Networks: A Decentralized Surrogate Median Regression Approach](https://mlsanity.com/paper/2202.05498/) (2022)

## Abstract (arXiv)

In this work, we consider to improve the model estimation efficiency by aggregating the neighbors' information as well as identify the subgroup membership for each node in the network. A tree-based l1 penalty is proposed to save the computation and communication cost. We design a decentralized generalized alternating direction method of multiplier algorithm for solving the objective function in parallel. The theoretical properties are derived to guarantee both the model consistency and the algorithm convergence. Thorough numerical experiments are also conducted to back up our theory, which also show that our approach outperforms in the aspects of the estimation accuracy, computation speed and communication cost.