# Learning of Tree-Structured Gaussian Graphical Models on Distributed Data under Communication Constraints

arXiv: https://arxiv.org/abs/1809.08067 · HTML: https://mlsanity.com/paper/1809.08067/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2018.

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

- [Learning Linear Polytree Structural Equation Models](https://mlsanity.com/paper/2107.10955/) (2021)
- [Decentralized Learning of Tree-Structured Gaussian Graphical Models from Noisy Data](https://mlsanity.com/paper/2109.10642/) (2021)
- [Efficient learning of differential network in multi-source non-paranormal graphical models](https://mlsanity.com/paper/2410.02496/) (2024)
- [FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projection](https://mlsanity.com/paper/2509.10041/) (2025)

## Abstract (arXiv)

In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our simulation results on both synthetic and real-world datasets show that our strategies achieve a desired accuracy in inferring the underlying structure, while spending a small budget on communication.