Data Analytics for Fog Computing by Distributed Online Learning with Asynchronous Update

TL;DR

This paper introduces a distributed online learning system for fog computing that processes data locally and asynchronously updates a central model, achieving high accuracy and scalability for large-scale, data-intensive applications.

Contribution

It proposes a novel asynchronous update strategy for distributed online learning in fog computing, improving robustness, efficiency, and scalability over centralized models.

Findings

Comparable classification accuracy to centralized models

Enhanced system robustness through asynchronous updates

Improved efficiency and scalability in fog environments

Abstract

Fog computing extends the cloud computing paradigm by allocating substantial portions of computations and services towards the edge of a network, and is, therefore, particularly suitable for large-scale, geo-distributed, and data-intensive applications. As the popularity of fog applications increases, there is a demand for the development of smart data analytic tools, which can process massive data streams in an efficient manner. To satisfy such requirements, we propose a system in which data streams generated from distributed sources are digested almost locally, whereas a relatively small amount of distilled information is converged to a center. The center extracts knowledge from the collected information, and shares it across all subordinates to boost their performances. Upon the proposed system, we devise a distributed machine learning algorithm using the online learning approach, which is well known for its high efficiency and innate ability to cope with streaming data. An asynchronous update strategy with rigorous theoretical support is applied to enhance the system robustness. Experimental results demonstrate that the proposed method is comparable with a model trained over a centralized platform in terms of the classification accuracy, whereas the efficiency and scalability of the overall system are improved.