Secure Video Offloading in MEC-Enabled IIoT Networks: A Multicell Federated Deep Reinforcement Learning Approach

Wireless video offloading in mobile-edge-computing (MEC)-enabled Industrial Internet of Things imposes a risk of exposing users' private data to eavesdroppers. It is difficult for existing secure video offloading schemes to simultaneously guarantee security, reduce latency and energy consumption in privacy-sensitive multicell scenarios where users are unwilling to offload data to other cells. In this article, a secure video offloading scheme based on multicell federated (MCF) deep reinforcement learning (DRL) is proposed to facilitate a secure, real-time, and efficient MEC network by efficient orchestration of limited resources. We formulate a collaborative optimization problem of video frame resolution and resources to minimize latency and energy consumption while maximizing the security rate subject to analytic accuracy and limited resources. To solve the formulated NP-hard problem, a MCF DRL algorithm based on the frameworks of multicell horizontal federated learning (FL) and hierarchical reward function-based twin delayed deep deterministic policy gradient (TD3) is proposed. First of all, hierarchical reward function-based TD3 is employed to solve the collaborative optimization NP-hard problem formulated for each single cell, where the optimal solution can be efficiently approached by the agent under the guidance of the innovatively designed hierarchical reward function. Then, multicell horizontal FL is applied on TD3 to obtain a model with higher model quality by averagely aggregating multiple individual TD3 models. Simulation results reveal that the proposed algorithm outperforms comparison algorithms in terms of utility, cost, latency, energy consumption, and security rate.

I. Introduction

The vigorous development of the 5G mobile communication technology, video processing technology, and artificial intelligence is incubating a broad spectrum of emerging video applications in Industrial Internet of Things (IIoT) [1], ranging from fault monitoring in smart manufacturing, video-based production line inspection in the smart factory to illegal behavior monitoring in smart transportation and health tracking in smart healthcare, which have higher requirements of latency, energy consumption, and quality of experience (QoE). It is challenging to offload the computation-intensive tasks generated by the emerging applications to remote central cloud servers to be processed due to the heavy network burden brought by the massive user access and the high latency resulting from the faraway distance between users and central cloud servers.