Attention-Enhanced Transferable Task Offloading via Auxiliary Learning in Mobile Edge Computing

Rui Zhang, Junjie Hu, Yicheng Di, Jiayu Bao, Jiansong Fan, Yuan Liu

Abstract: The rapid development of the Internet of Things (IoT) and cyberphysical systems (CPSs) has led to the rise of mobile edge computing (MEC), enabling low-latency task offloading in dynamic environments. However, existing offloading strategies struggle to generalize across diverse and evolving network topologies, often requiring retraining or fine-tuning when deployed in new scenarios. To address these challenges, we propose AtALT, a transferable task-offloading framework that achieves zero-shot transferability. The acronym AtALT is derived from the key components of our method: attention-auxiliary learning-transferable task offloading. AtALT integrates an attention-based encoder and an auxiliary learning module. The attention-based encoder dynamically computes topology-agnostic compatibility scores, allowing for flexible task-offloading decisions across different network configurations. The auxiliary learning module predicts node states, regularizing the policy learning process and enhancing generalization. Experimental results demonstrate that AtALT outperforms existing methods in transferability and efficiency, making it suitable for deployment in previously unseen environments without the need for further training.

External IDs:dblp:journals/iotj/ZhangHDBFL26