# An alternative approach for distributed parameter estimation under Gaussian settings

arXiv: https://arxiv.org/abs/2204.08317 · HTML: https://mlsanity.com/paper/2204.08317/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 14 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Consensus+Innovations Distributed Kalman Filter with Optimized Gains](https://mlsanity.com/paper/1605.06096/) (2016)
- [All-in-one: Certifiable Optimal Distributed Kalman Filter under Unknown Correlations](https://mlsanity.com/paper/2105.15061/) (2021)
- [On observability and optimal gain design for distributed linear filtering and prediction](https://mlsanity.com/paper/2203.03521/) (2022)

## Abstract (arXiv)

This paper takes a different approach for the distributed linear parameter estimation over a multi-agent network. The parameter vector is considered to be stochastic with a Gaussian distribution. The sensor measurements at each agent are linear and corrupted with additive white Gaussian noise. Under such settings, this paper presents a novel distributed estimation algorithm that fuses the the concepts of consensus and innovations by incorporating the consensus terms (of neighboring estimates) into the innovation terms. Under the assumption of distributed parameter observability, introduced in this paper, we design the optimal gain matrices such that the distributed estimates are consistent and achieves fast convergence.