Theory of Mixture-of-Experts for Mobile Edge Computing

Understand

In mobile edge computing (MEC) networks, mobile users generate diverse machine learning tasks dynamically over time.

• These tasks are typically offloaded to the nearest available edge server, by considering communication and computational efficiency.
• However, its operation does not ensure that each server specializes in a specific type of tasks and leads to severe overfitting or catastrophic forgetting of previous tasks.
• To improve the continual learning (CL) performance of online tasks, we are the first to introduce mixture-of-experts (MoE) theory in MEC networks and save MEC operation from the increasing generalization error over time.