Resource Allocation Using Deep Deterministic Policy Gradient-Based Federated Learning for Multi-Access Edge Computing

Abstract

The study focuses on utilizing the computational resources present in vehicles to enhance the performance of multi-access edge computing (MEC) systems. While vehicles are typically equipped with computational services for vehicle-centric Internet of Vehicles (IoV) applications, their resources can also be leveraged to reduce the workload on edge servers and improve task processing speed in MEC scenarios. Previous research efforts have overlooked the potential resource utilization of passing vehicles, which can be a valuable addition to MEC systems alongside parked cars. This study introduces an assisted MEC scenario where a base station (BS) with an edge server serves various devices, parked cars, and vehicular traffic. A cooperative approach using the Deep Deterministic Policy Gradient (DDPG) based Federated Learning method is proposed to optimize resource allocation and job offloading. This method enables the transfer of device operations from devices to the BS or from the BS to vehicles based on specific requirements. The proposed system also considers the duration for which a vehicle can provide job offloading services within the range of the BS before leaving. The objective of the DDPG-FL method is to minimize the overall priority-weighted task computation time. Through simulation results and a comparison with three other schemes, the study demonstrates the superiority of their proposed method in seven different scenarios. The findings highlight the potential of incorporating vehicular resources in MEC systems, showcasing improved task processing efficiency and overall system performance.