# Communication-efficient k-Means for Edge-based Machine Learning

arXiv: https://arxiv.org/abs/2102.04282 · HTML: https://mlsanity.com/paper/2102.04282/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 8 Feb 2021.

## Builds on (most cited first, 11 of 11)

- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Turning Big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering](https://mlsanity.com/paper/1807.04518/) (2018)
- [Wireless Network Intelligence at the Edge](https://mlsanity.com/paper/1812.02858/) (2018)
- [Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering](https://mlsanity.com/paper/1811.03195/) (2018)
- [Dimensionality Reduction for k-Means Clustering and Low Rank Approximation](https://mlsanity.com/paper/1410.6801/) (2014)
- [New Frameworks for Offline and Streaming Coreset Constructions](https://mlsanity.com/paper/1612.00889/) (2016)
- [Distributed k-Means and k-Median Clustering on General Topologies](https://mlsanity.com/paper/1306.0604/) (2013)
- [Improved Distributed Principal Component Analysis](https://mlsanity.com/paper/1408.5823/) (2014)
- [Poisson Random Fields for Dynamic Feature Models](https://mlsanity.com/paper/1611.07460/) (2016)
- [Robust Coreset Construction for Distributed Machine Learning](https://mlsanity.com/paper/1904.05961/) (2019)
- [k-Means for Streaming and Distributed Big Sparse Data](https://mlsanity.com/paper/1511.08990/) (2015)

## Abstract (arXiv)

We consider the problem of computing the k-means centers for a large high-dimensional dataset in the context of edge-based machine learning, where data sources offload machine learning computation to nearby edge servers. k-Means computation is fundamental to many data analytics, and the capability of computing provably accurate k-means centers by leveraging the computation power of the edge servers, at a low communication and computation cost to the data sources, will greatly improve the performance of these analytics. We propose to let the data sources send small summaries, generated by joint dimensionality reduction (DR), cardinality reduction (CR), and quantization (QT), to support approximate k-means computation at reduced complexity and communication cost. By analyzing the complexity, the communication cost, and the approximation error of k-means algorithms based on carefully designed composition of DR/CR/QT methods, we show that: (i) it is possible to compute near-optimal k-means centers at a near-linear complexity and a constant or logarithmic communication cost, (ii) the order of applying DR and CR significantly affects the complexity and the communication cost, and (iii) combining DR/CR methods with a properly configured quantizer can further reduce the communication cost without compromising the other performance metrics. Our theoretical analysis has been validated through experiments based on real datasets.