# PrivateLoRA For Efficient Privacy Preserving LLM

**Authors:** Yiming Wang, Yu Lin, Xiaodong Zeng, Guannan Zhang  
**arXiv:** [2311.14030](https://arxiv.org/abs/2311.14030)  
**Date:** 2023-11-23  
**Summary page:** https://www.summarizepaper.com/en/paper/2311.14030v1/

## Key points

- PrivateLoRA is a novel solution that balances privacy and efficiency in Large Language Model (LLM) services
- It strategically distributes privacy-sensitive computations on edge devices and shared computations in the cloud
- The key innovation of PrivateLoRA is reducing communication overhead by leveraging residual activations, achieving a 95% reduction
- Under standard 5G networks, PrivateLoRA outperforms device-only solutions for 7B models by over 300% throughput and surpasses A100 GPU for 33B models with over 80% efficiency
- PrivateLoRA democratizes access to generative AI for edge devices, enabling tailored LLM experiences for a wider audience

## Summary

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PrivateLoRA: An Innovative Solution for Efficient and Privacy-Preserving LLM Services

PrivateLoRA is a groundbreaking solution that effectively addresses the trade-off between privacy and efficiency in Large Language Model (LLM) service paradigms. Traditional cloud-based approaches often sacrifice data locality for generation quality and processing speed, while edge device paradigms struggle to deliver optimal performance. In response to these challenges, PrivateLoRA introduces a novel LLM service paradigm that strategically distributes privacy-sensitive computations on edge devices and shared computations in the cloud. By transmitting only activations between the central cloud and edge devices, PrivateLoRA preserves data locality without compromising performance. The key innovation of PrivateLoRA lies in its ability to significantly reduce communication overhead by leveraging the low rank of residual activations, achieving an impressive 95% reduction in communication. This efficient approach ensures that data remains localized while maximizing resource utilization. Under standard 5G networks, PrivateLoRA outperforms device-only solutions for 7B models by achieving over 300% throughput and surpasses an A100 GPU for 33B models with over 80% efficiency. Additionally, PrivateLoRA offers tuning performance comparable to LoRA for advanced personalization. By democratizing access to cutting-edge generative AI for edge devices, PrivateLoRA opens up new possibilities for tailored LLM experiences for a wider audience. This innovative framework represents the first efficient and privacy-preserving solution of its kind in the literature, paving the way for enhanced privacy protection and optimized performance in LLM applications. The collaborative efforts of authors Yiming Wang, Yu Lin, Xiaodong Zeng, and Guannan Zhang have led to a significant advancement in the field of LLM services with the development of PrivateLoRA.