# Analysis of a Reduced-Communication Diffusion LMS Algorithm

arXiv: https://arxiv.org/abs/1408.5845 · HTML: https://mlsanity.com/paper/1408.5845/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2014.

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

- [QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM](https://mlsanity.com/paper/2208.02777/) (2022)
- [Decentralized Entropic Optimal Transport for Distributed Distribution Comparison](https://mlsanity.com/paper/2301.12065/) (2023)
- [Asynchronous Diffusion Learning with Agent Subsampling and Local Updates](https://mlsanity.com/paper/2402.05529/) (2024)
- [Low-Communication Resilient Distributed Estimation Algorithm Based on Memory Mechanism](https://mlsanity.com/paper/2508.02705/) (2025)

## Abstract (arXiv)

In diffusion-based algorithms for adaptive distributed estimation, each node of an adaptive network estimates a target parameter vector by creating an intermediate estimate and then combining the intermediate estimates available within its closed neighborhood. We analyze the performance of a reduced-communication diffusion least mean-square (RC-DLMS) algorithm, which allows each node to receive the intermediate estimates of only a subset of its neighbors at each iteration. This algorithm eases the usage of network communication resources and delivers a trade-off between estimation performance and communication cost. We show analytically that the RC-DLMS algorithm is stable and convergent in both mean and mean-square senses. We also calculate its theoretical steady-state mean-square deviation. Simulation results demonstrate a good match between theory and experiment.