Prada: Black-Box LLM Adaptation with Private Data on Resource-Constrained Devices

Abstract: In recent years, LLMs have demonstrated remarkable abilities in various natural language processing tasks. However, adapting these models to specialized domains using private datasets stored on resource-constrained edge devices, such as smartphones and personal computers, remains challenging due to significant privacy concerns and limited computational resources. Existing model adaptation methods either compromise data privacy by requiring data transmission or jeopardize model privacy by exposing proprietary LLM parameters. To address these challenges, we propose Prada, a novel privacy-preserving and efficient black-box LLM adaptation system using private on-device datasets. Prada employs a lightweight proxy model fine-tuned with Low-Rank Adaptation (LoRA) locally on user devices. During inference, Prada leverages the logits offset, i.e., difference in outputs between the base and adapted proxy models, to iteratively refine outputs from a remote black-box LLM. This offset-based adaptation approach preserves both data privacy and model privacy, as there is no need to share sensitive data or proprietary model parameters. Furthermore, we incorporate speculative decoding to further speed up the inference process of Prada, making the system practically deployable on bandwidth-constrained edge devices, enabling a more practical deployment of Prada. Extensive experiments on various downstream tasks demonstrate that Prada achieves performance comparable to centralized fine-tuning methods while significantly reducing computational overhead by up to 60% and communication costs by up to 80%.

• The paper introduces a novel black-box adaptation method that fine-tunes proxy models on-device using LoRA to ensure data and model privacy.
• It employs online logits offset adaptation and speculative decoding to optimize inference performance while minimizing communication and computational costs.
• Extensive benchmarks demonstrate up to a 60% reduction in computational overhead and an 80% decrease in communication costs for edge deployments.

Prada: Black-Box LLM Adaptation with Private Data on Resource-Constrained Devices

The paper "Prada: Black-Box LLM Adaptation with Private Data on Resource-Constrained Devices" proposes a novel system to adapt LLMs using private datasets on edge devices without compromising data or model privacy. The approach leverages fine-tuning of proxy models on-device using Low-Rank Adaptation (LoRA) and implementing a method to refine black-box model outputs through logits offsets, maintaining high efficiency and privacy standards.

Privacy and Efficiency Challenges in LLM Adaptation

Traditional methods of adapting LLMs, such as centralized fine-tuning or federated learning, face significant hurdles concerning data privacy and model disclosure risks. Transmitting data to central servers risks exposing sensitive user information, especially on edge devices, while sharing model parameters can lead to intellectual property concerns. This calls for systems that can efficiently perform domain-specific adaptation locally, mitigating privacy concerns without requiring vast computational resources.

Prada Methodology

Offline Proxy Model LoRA Fine-Tuning

Prada introduces a lightweight proxy model that is fine-tuned on-device using LoRA, a parameter-efficient method that injects trainable low-rank matrices into the neural network layers. This method significantly reduces memory usage and computation costs, making it feasible for resource-constrained devices like smartphones or PCs.

Online Black-Box Offset Adaptation

During inference, Prada employs an offset-based adaptation where the client computes the difference in logits between the base and adapted proxy models and refines outputs from the black-box LLM. This preservation of both data and model privacy is achieved through the black-box model's API while ensuring efficient utilization of bandwidth and computational resources.

Inference Optimization with Speculative Decoding

Prada further optimizes inference latency by using speculative decoding, enabling batch processing and parallel computations to reduce communication round-trips between client and server, all while maintaining adaptation quality.

Performance and Resource Analysis

Adaptation Effectiveness

Extensive experiments across multiple benchmarks demonstrate that Prada achieves adaptation performance comparable to direct fine-tuning methods, reducing computational overhead by up to 60% and communication costs by 80%. It excels particularly in specialized tasks such as sentiment analysis, medical data processing, and code generation.

Resource Utilization

Prada leverages resource-efficient techniques ensuring operation within the hardware constraints typical of edge devices, utilizing less than the available GPU memory and significantly reducing training and communication expenses compared to conventional methods.

Practical Implications and Future Directions

Prada's approach facilitates the practical deployment of domain-specific LLMs on edge devices, representing a significant step towards secure, efficient, and flexible models adaptable to personal user data without risking privacy. Future developments could further refine speculative decoding and explore advanced encryption techniques to safeguard data transmitted for inference.

Conclusion

Prada introduces a viable solution to the dual privacy concerns in LLM adaptation for edge devices, balancing performance trade-offs with computational and communication efficiency. Its innovative use of proxy models presents a sustainable path forward for personalized AI applications in privacy-sensitive environments.