  With the rapid advancement of large models and mobile edge computing,
transfer learning, particularly through fine-tuning, has become crucial for
adapting models to downstream tasks. Traditionally, this requires users to
share their data with model owners for fine-tuning, which is not only costly
but also raises significant privacy concerns. Furthermore, fine-tuning
large-scale models is computationally intensive and often impractical for many
users. To tackle these challenges, we introduce a system that combines
offsite-tuning with physical-layer security, which provides local data owners
with a lightweight adapter and a compressed emulator. Data owners then
fine-tune the adapter locally and securely send it back to the model owners
through a confidential channel for integration, ensuring privacy and resource
conservation. Our paper focuses on optimizing computational resource allocation
among data owners and the large model owner deployed on edge, and on the
compression ratio of adapters. We incorporate a secrecy uplink channel to
maximize the utility that we defined while minimizing system costs like energy
consumption and delay. The optimization uses the Dinkelbach algorithm,
fractional programming, successive convex approximation and alternating
optimization. Experiments demonstrate our algorithm's superiority over existing
methods.
