Computation Offloading with Multiple Agents in Edge-Computing-Supported IoT

TL;DR

This paper proposes a novel distributed computation offloading algorithm for IoT devices using multiple DRL agents trained with Federated Learning, addressing NP-hard optimization challenges in edge computing.

Contribution

It introduces a new offloading algorithm combining DRL and FL for IoT edge computing, improving decision efficiency and reducing transmission costs.

Findings

The proposed algorithm effectively optimizes offloading decisions.

Federated Learning reduces communication overhead.

The approach outperforms traditional methods in various scenarios.

Abstract

With the development of the Internet of Things (IoT) and the birth of various new IoT devices, the capacity of massive IoT devices is facing challenges. Fortunately, edge computing can optimize problems such as delay and connectivity by offloading part of the computational tasks to edge nodes close to the data source. Using this feature, IoT devices can save more resources while still maintaining the quality of service. However, since computation offloading decisions concern joint and complex resource management, we use multiple Deep Reinforcement Learning (DRL) agents deployed on IoT devices to guide their own decisions. Besides, Federated Learning (FL) is utilized to train DRL agents in a distributed fashion, aiming to make the DRL-based decision making practical and further decrease the transmission cost between IoT devices and Edge Nodes. In this article, we first study the problem of computation offloading optimization and prove the problem is an NP-hard problem. Then, based on DRL and FL, we propose an offloading algorithm that is different from the traditional method. Finally, we studied the effects of various parameters on the performance of the algorithm and verified the effectiveness of both the DRL and FL in the IoT system.