Leveraging Personalized Customer Experiences in Mobile Edge Computing Through Split Learning Using Smart Data-Driven Modeling - V. Vinoth Kumar; K. M. Karthick Raghunath; Iyappan Perumal; K. Manikandan (IEEE Access)
The rapid growth and opportunities of Mobile Edge Computing (MEC) technology have transformed smart consumer electronics like smartphones and wearable devices that produce massive and complicated data sets. Analyzing consumer electronics data to understand customers is difficult because of its vast scale and detailed content. To solve these issues, we present Enhanced Smart Data-Driven Modeling (ESDDM), which combines Smart Data-Driven Modeling (SDDM) with modern Deep Learning (DL). ESDDM combines multiple data streams to better understand consumer electronics systems beyond the normal Machine Learning (ML) capabilities. ESDDM’s integration with Split Learning (SL) protects data by lessening transmission risks and avoiding central cloud storage while keeping sensitive information secure on user devices and delivering better system performance. The prediction results from ESDDM show its strength with a low Mean Squared Error (MSE) of 0.267, which reveals its capability to reshape the MEC domain while creating business possibilities and better customer outcomes.