A Knowledge Distillation-Based Lightweight Task Offloading Algorithm for Mobile Edge Computing

Xinyu Wang; Yimai Wang; Can Ti; Bo Cui

doi:10.6919/ICJE.202604_12(4).0042

A Knowledge Distillation-Based Lightweight Task Offloading Algorithm for Mobile Edge Computing

Xinyu Wang, Yimai Wang, Can Ti, Bo Cui

International Core Journal of Engineering, (2026), Vol.12, No.4, pp.397-411

Published: April 14, 2026

DOI: 10.6919/ICJE.202604_12(4).0042

PDF: Download Full Text PDF

Abstract

To address the issues of complex models, high inference latency, and difficulties in deploying traditional deep reinforcement learning offloading strategies on resource-constrained devices in mobile edge computing, this paper proposes a lightweight D3QN computation offloading method based on knowledge distillation. This method constructs a teacher-student network architecture, transferring the decision-making capabilities of the teacher model to a lightweight student network via offline knowledge distillation. Concurrently, an adaptive exploration strategy incorporating heuristic rules is designed to accelerate convergence, whilst LSTM temporal awareness and a dynamic energy consumption normalisation mechanism are introduced to optimise QoE. Experimental results demonstrate that, at a compression ratio of 0.4, the number of parameters in the student model is reduced by 77.61%, inference latency is reduced by 26.32%, and QoE reaches 829.07, approaching the performance of the teacher model, thereby providing an efficient and feasible lightweight solution for edge intelligence offloading.

Keywords: Mobile Edge Computing; Task Offloading; Deep Reinforcement Learning; Knowledge Distillation; Lightweight Models; D3QN.

APA Citation: Xinyu Wang, Yimai Wang, Can Ti, Bo Cui (2026). A Knowledge Distillation-Based Lightweight Task Offloading Algorithm for Mobile Edge Computing. International Core Journal of Engineering, 12(4), 397-411. https://doi.org/10.6919/ICJE.202604_12(4).0042

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