论文标题
基于移动边缘计算的物联网环境中预测分析的分布式机器学习
Distributed Machine Learning for Predictive Analytics in Mobile Edge Computing Based IoT Environments
论文作者
论文摘要
基于移动边缘计算（MEC）物联网（IoT）中的预测分析在许多现实世界中都已成为高需求。基于MEC的IoT环境中的一个预测问题通常对应于一组任务的集合，每个任务都基于本地累积的数据在特定的MEC环境中解决，这可以被视为多任务学习（MTL）问题。但是，在不同的MEC环境中积累的数据（非IID）的异质性挑战了一般MTL技术在这种情况下的应用。 Federated MTL（FMTL）最近出现是为了解决这个问题。除了FMTL外，还有另一个功能强大但爆炸率不足的分布式机器学习技术，称为Network Lasso（NL），该技术与FMTL固有相关，但具有其自己独特的功能。在本文中，我们对代表现实世界应用程序方案的三个不同的IoT数据集进行了深入的评估和比较。实验结果表明，在准确性和计算效率方面，NL在基于MEC的物联网环境中的表现优于FMTL。
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.
