Task Scheduling with Collaborative Computing of MEC System Based on Federated Learning

In response to the ever-increasing demands of users for delay-sensitive applications, issues on shortening the task completion time in the mobile edge computing (MEC) system has aroused widespread concern. From the perspective of task execution order, this work provide a task scheduling scheme for multiple edge nodes (EN) while federated learning (FL) is utilized for the collaboration of the ENs in the MEC system. First, to acquire an efficient execution order for the pending computational tasks that dynamically generated on one edge node, a task scheduling algorithm based on deep Q network (DQN) is proposed, which reduces the average task completion time. Then, based on the federated learning, of which characteristic matches the edge system well, we integrate an aggregation mechanism to take advantage of every participating edge node to obtain a set of global parameter that optimizes task completion delay for all nodes in the MEC system. Simulations verify the effectiveness and the superiority of the proposed algorithm in processing delay-sensitive tasks and analyze the key factors that contributes to the system performance.