DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models

• URL: http://arxiv.org/abs/2509.09097v1
• Date: Thu, 11 Sep 2025 02:16:34 GMT
• Title: DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models
• Authors: Honghui Xu, Shiva Shrestha, Wei Chen, Zhiyuan Li, Zhipeng Cai,
• Abstract summary: DP-FedLoRA is a privacy-enhanced federated fine-tuning framework.<n>It integrates LoRA-based adaptation with differential privacy in a communication-efficient setting.<n>We show that DP-FedLoRA delivers competitive performance while offering strong privacy guarantees.
• Score: 17.265217612125905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As on-device large language model (LLM) systems become increasingly prevalent, federated fine-tuning enables advanced language understanding and generation directly on edge devices; however, it also involves processing sensitive, user-specific data, raising significant privacy concerns within the federated learning framework. To address these challenges, we propose DP-FedLoRA, a privacy-enhanced federated fine-tuning framework that integrates LoRA-based adaptation with differential privacy in a communication-efficient setting. Each client locally clips and perturbs its LoRA matrices using Gaussian noise to satisfy ($\epsilon$, $\delta$)-differential privacy. We further provide a theoretical analysis demonstrating the unbiased nature of the updates and deriving bounds on the variance introduced by noise, offering practical guidance for privacy-budget calibration. Experimental results across mainstream benchmarks show that DP-FedLoRA delivers competitive performance while offering strong privacy guarantees, paving the way for scalable and privacy-preserving LLM deployment in on-device environments.

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