PrivateLoRA For Efficient Privacy Preserving LLM

Understand

End users face a choice between privacy and efficiency in current Large Language Model (LLM) service paradigms.

• In cloud-based paradigms, users are forced to compromise data locality for generation quality and processing speed.
• Conversely, edge device paradigms maintain data locality but fail to deliver satisfactory performance.
• In this work, we propose a novel LLM service paradigm that distributes privacy-sensitive computation on edge devices and shared computation in the cloud.