Distributed Machine Learning for Predictive Analytics in Mobile Edge Computing Based IoT Environments

TL;DR

This paper compares Federated Multi-task Learning and Network Lasso for predictive analytics in Mobile Edge Computing IoT environments, finding that Network Lasso outperforms FMTL in accuracy and efficiency.

Contribution

The study provides an in-depth evaluation and comparison of FMTL and Network Lasso techniques specifically for MEC-based IoT predictive analytics.

Findings

Network Lasso outperforms FMTL in accuracy.

Network Lasso is more computationally efficient.

Results are validated on three real-world IoT datasets.

Abstract

Predictive analytics in Mobile Edge Computing (MEC) based Internet of Things (IoT) is becoming a high demand in many real-world applications. A prediction problem in an MEC-based IoT environment typically corresponds to a collection of tasks with each task solved in a specific MEC environment based on the data accumulated locally, which can be regarded as a Multi-task Learning (MTL) problem. However, the heterogeneity of the data (non-IIDness) accumulated across different MEC environments challenges the application of general MTL techniques in such a setting. Federated MTL (FMTL) has recently emerged as an attempt to address this issue. Besides FMTL, there exists another powerful but under-exploited distributed machine learning technique, called Network Lasso (NL), which is inherently related to FMTL but has its own unique features. In this paper, we made an in-depth evaluation and comparison of these two techniques on three distinct IoT datasets representing real-world application scenarios. Experimental results revealed that NL outperformed FMTL in MEC-based IoT environments in terms of both accuracy and computational efficiency.