Dong L, Jiang F, Peng Y, Wang K, Yang K, Pan C, Schober R (2025)
Publication Type: Journal article
Publication year: 2025
Book Volume: 63
Pages Range: 88-94
Journal Issue: 4
Next-generation edge intelligence is anticipated to benefit various applications via offloading techniques. However, traditional offloading architectures face several issues, including heterogeneous constraints, partial perception, uncertain generalization, and lack of tractability. In this article, we propose a large AI model-based offloading (LAMBO) framework with over one billion parameters for solving these problems. We first use input embedding (IE) to achieve normalized feature representation with heterogeneous constraints and task prompts. Then, we introduce a novel asymmetric encoder-decoder (AED) as the decision-making model, which is an improved transformer architecture consisting of a deep encoder and a shallow decoder for global perception and decision. Next, actor-critic learning (ACL) is used to pre-train the AED for different optimization tasks under corresponding prompts, enhancing the AED's generalization in multi-task scenarios. Finally, we propose an active learning from expert feedback (ALEF) method to fine-tune the decoder of the AED for tracking changes in dynamic environments. Our simulation results validate the advantages of the proposed LAMBO framework.
APA:
Dong, L., Jiang, F., Peng, Y., Wang, K., Yang, K., Pan, C., & Schober, R. (2025). LAMBO: Large AI Model Empowered Edge Intelligence. IEEE Communications Magazine, 63(4), 88-94. https://doi.org/10.1109/MCOM.001.2400076
MLA:
Dong, Li, et al. "LAMBO: Large AI Model Empowered Edge Intelligence." IEEE Communications Magazine 63.4 (2025): 88-94.
BibTeX: Download