  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.
