# Parameter-Efficient Fine-Tuning with Differential Privacy for Robust Instruction Adaptation in Large Language Models

arXiv: https://arxiv.org/abs/2512.06711 · HTML: https://mlsanity.com/paper/2512.06711/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 7 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass](https://mlsanity.com/paper/2309.06746/) (2023)
- [Differentially Private Bias-Term Fine-tuning of Foundation Models](https://mlsanity.com/paper/2210.00036/) (2022)
- [Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning](https://mlsanity.com/paper/2312.17493/) (2023)
- [Differentially Private Learning with Per-Sample Adaptive Clipping](https://mlsanity.com/paper/2212.00328/) (2022)
- [Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation](https://mlsanity.com/paper/2410.02912/) (2024)
- [Contrastive Learning-Based Dependency Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2510.13368/) (2025)
- [Modular Task Decomposition and Dynamic Collaboration in Multi-Agent Systems Driven by Large Language Models](https://mlsanity.com/paper/2511.01149/) (2025)
- [Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models](https://mlsanity.com/paper/2508.17184/) (2025)
- [Multi-Objective Adaptive Rate Limiting in Microservices Using Deep Reinforcement Learning](https://mlsanity.com/paper/2511.03279/) (2025)
- [Deep Learning Approach for Clinical Risk Identification Using Transformer Modeling of Heterogeneous EHR Data](https://mlsanity.com/paper/2511.04158/) (2025)
- [Backpropagation Clipping for Deep Learning with Differential Privacy](https://mlsanity.com/paper/2202.05089/) (2022)
- [Structural Priors and Modular Adapters in the Composable Fine-Tuning Algorithm of Large-Scale Models](https://mlsanity.com/paper/2511.03981/) (2025)
- [WeGeFT: Weight-Generative Fine-Tuning for Multi-Faceted Efficient Adaptation of Large Models](https://mlsanity.com/paper/2312.00700/) (2023)
- [Contrastive Knowledge Transfer and Robust Optimization for Secure Alignment of Large Language Models](https://mlsanity.com/paper/2510.27077/) (2025)
- [Graph Neural Networks for User Satisfaction Classification in Human-Computer Interaction](https://mlsanity.com/paper/2511.04166/) (2025)
- [Deep Learning Approach to Anomaly Detection in Enterprise ETL Processes with Autoencoders](https://mlsanity.com/paper/2511.00462/) (2025)

## Built on by (most cited first, 5 of 5)

- [Graph-Structured Deep Learning Framework for Multi-task Contention Identification with High-dimensional Metrics](https://mlsanity.com/paper/2601.20389/) (2026)
- [CaliCausalRank: Calibrated Multi-Objective Ad Ranking with Robust Counterfactual Utility Optimization](https://mlsanity.com/paper/2602.18786/) (2026)
- [Structured Prompt Optimization for Few-Shot Text Classification via Semantic Alignment in Latent Space](https://mlsanity.com/paper/2602.23753/) (2026)
- [Coordinated Semantic Alignment and Evidence Constraints for Retrieval-Augmented Generation with Large Language Models](https://mlsanity.com/paper/2603.04647/) (2026)
- [Unsupervised Graph Modeling for Anomaly Detection in Accounting Subject Relationships](https://mlsanity.com/paper/2604.26216/) (2026)

## Abstract (arXiv)

This study addresses the issues of privacy protection and efficiency in instruction fine-tuning of large-scale language models by proposing a parameter-efficient method that integrates differential privacy noise allocation with gradient clipping in a collaborative optimization framework. The method keeps the backbone model frozen and updates parameters through a low-dimensional projection subspace, while introducing clipping and adaptive noise allocation during gradient computation. This design reduces privacy budget consumption and ensures training stability and robustness. The unified framework combines gradient constraints, noise allocation, and parameter projection, effectively mitigating performance fluctuations and privacy risks in multi-task instruction scenarios. Experiments are conducted across hyperparameter, environment, and data sensitivity dimensions. Results show that the method outperforms baseline models in accuracy, privacy budget, and parameter efficiency, and maintains stable performance under diverse and uncertain data conditions. The findings enrich the theoretical integration of differential privacy and parameter-efficient fine-tuning and demonstrate its practical adaptability in instruction tasks, providing a feasible solution for secure training in complex instruction environments.