.png){kind=icon}
.png){kind=icon}
Return
Optimizing Resource Allocation and Secure Wireless Communication in Large Model Based Mobile Edge Computing Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/tmc.2026.3674200.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
With the rapid advancement of large models and mobile edge computing, transfer learning through fine-tuning has become essential for adapting models to downstream tasks. Traditionally, users must share their data with model owners, which is costly and raises privacy risks.In addition, fine-tuning large-scale models is computationally intensive and often impractical for many users. To address these challenges, we propose a model that combines offsite-tuning with physical-layer security. Local data owners are given a lightweight adapter and a compressed emulator extracted from the original model. They fine-tune the adapter locally and securely send it back to the model owner through a confidential channel for integration, ensuring privacy and resource conservation. Our work focuses on optimizing computational resource allocation between data owners and the large model owner at the edge, while also optimizing the adapter compression ratio to improve efficiency. We integrate a secrecy uplink channel to maximize the defined utility while minimizing system costs such as energy consumption and delay. The optimization process employs the Dinkelbach algorithm, fractional programming, successive convex approximation, branch-and-bound algorithm, and alternating optimization. Experimental results validate the superiority of our algorithm over several baseline methods.
Keywords:
Large model
mobile edge computing
physical layer security
resource allocation
Journal
IF:
9.2
Papers:
5.6K
Citations:
1.8W