Distributed Kernel Regression: An Algorithm for Training Collaboratively

TL;DR

This paper introduces a distributed kernel regression algorithm designed for collaborative training under communication constraints, with analysis of its convergence and statistical properties in a simplified setting.

Contribution

It proposes a novel algorithm for distributed kernel regression and analyzes its convergence and statistical behavior, addressing communication constraints in distributed learning.

Findings

Algorithm converges under certain conditions

Statistical properties are characterized in simplified models

Applicable to wireless sensor networks and distributed data mining

Abstract

This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributed databases. After formalizing a general model for distributed learning, an algorithm for collaboratively training regularized kernel least-squares regression estimators is derived. Noting that the algorithm can be viewed as an application of successive orthogonal projection algorithms, its convergence properties are investigated and the statistical behavior of the estimator is discussed in a simplified theoretical setting.