Differentially Private Low-Rank Adaptation of Large Language Model Using
  Federated Learning
Xiao-Yang Liu Rongyi Zhu Daochen Zha Jiechao Gao Shan Zhong Meikang Qiu
The surge in interest and application of large language models (LLMs) has
sparked a drive to fine-tune these models to suit specific applications, such
as finance and medical science. However, concerns regarding data privacy have
emerged, especially when multiple stakeholders aim to collaboratively enhance
LLMs using sensitive data. In this scenario, federated learning becomes a
natural choice, allowing decentralized fine-tuning without exposing raw data to
central servers. Motivated by this, we investigate how data privacy can be
ensured in LLM fine-tuning through practical federated learning approaches,
enabling secure contributions from multiple parties to enhance LLMs. Yet,
challenges arise: 1) despite avoiding raw data exposure, there is a risk of
inferring sensitive information from model outputs, and 2) federated learning
for LLMs incurs notable communication overhead. To address these challenges,
this article introduces DP-LoRA, a novel federated learning algorithm tailored
for LLMs. DP-LoRA preserves data privacy by employing a Gaussian mechanism that
adds noise in weight updates, maintaining individual data privacy while
facilitating collaborative model training. Moreover, DP-LoRA optimizes
communication efficiency via low-rank adaptation, minimizing the transmission
of updated weights during distributed training. The experimental results across
medical, financial, and general datasets using various LLMs demonstrate that
DP-LoRA effectively ensures strict privacy constraints while minimizing
communication overhead.