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

02/08/2021
by   Hanlin Lu, et al.
13

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) and cardinality reduction (CR), 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 state-of-the-art DR/CR methods, we show that: (i) it is possible to achieve a near-optimal approximation 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 findings are validated through experiments based on real datasets.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/19/2018

Mathematical Analysis on Out-of-Sample Extensions

Let X=X∪Z be a data set in R^D, where X is the training set and Z is the...
research
04/29/2022

Local Explanation of Dimensionality Reduction

Dimensionality reduction (DR) is a popular method for preparing and anal...
research
01/25/2021

Cloud, Fog or Edge: Where to Compute?

The computing continuum extends the high-performance cloud data centers ...
research
06/28/2022

Feature Learning for Dimensionality Reduction toward Maximal Extraction of Hidden Patterns

Dimensionality reduction (DR) plays a vital role in the visual analysis ...
research
03/30/2016

Towards Geo-Distributed Machine Learning

Latency to end-users and regulatory requirements push large companies to...
research
03/07/2020

Improving IoT Analytics through Selective Edge Execution

A large number of emerging IoT applications rely on machine learning rou...

Please sign up or login with your details

Forgot password? Click here to reset