Offsite-Tuning: Transfer Learning without Full Model (2302.04870v1)

Abstract: Transfer learning is important for foundation models to adapt to downstream tasks. However, many foundation models are proprietary, so users must share their data with model owners to fine-tune the models, which is costly and raise privacy concerns. Moreover, fine-tuning large foundation models is computation-intensive and impractical for most downstream users. In this paper, we propose Offsite-Tuning, a privacy-preserving and efficient transfer learning framework that can adapt billion-parameter foundation models to downstream data without access to the full model. In offsite-tuning, the model owner sends a light-weight adapter and a lossy compressed emulator to the data owner, who then fine-tunes the adapter on the downstream data with the emulator's assistance. The fine-tuned adapter is then returned to the model owner, who plugs it into the full model to create an adapted foundation model. Offsite-tuning preserves both parties' privacy and is computationally more efficient than the existing fine-tuning methods that require access to the full model weights. We demonstrate the effectiveness of offsite-tuning on various large language and vision foundation models. Offsite-tuning can achieve comparable accuracy as full model fine-tuning while being privacy-preserving and efficient, achieving 6.5x speedup and 5.6x memory reduction. Code is available at https://github.com/mit-han-lab/offsite-tuning.

Overview of Offsite-Tuning: Transfer Learning without Full Model

The paper entitled "Offsite-Tuning: Transfer Learning without Full Model" addresses the challenges of fine-tuning large foundation models, particularly regarding privacy and computational efficiency. The authors propose a novel method that allows users to adapt foundation models to downstream tasks without requiring access to the full model parameters. This method presents a significant advancement for dealing with proprietary models and large-scale datasets that necessitate privacy preservation.

Key Contributions

1. Privacy-Preserving Framework: The authors introduce Offsite-Tuning, which enables transfer learning on large models while ensuring that neither the model owner nor the data owner needs to share their complete assets. This is achieved by splitting the model into two components: a lightweight, trainable adapter and a lossy compressed emulator. The data owner fine-tunes the adapter with the emulator and shares only the fine-tuned adapter back with the model owner.
2. Efficiency Gains: Offsite-Tuning provides computational efficiency, demonstrated by a 6.5 times speedup and a 5.6 times reduction in memory usage compared to traditional full model fine-tuning. This highlights the method's suitability for resource-constrained environments, enabling fine-tuning on devices such as a single GPU.
3. Comparable Performance: The method achieves accuracy comparable to full model fine-tuning across various tasks and large models, including GPT-2, OPT, BLOOM, CLIP, and EVA models. The performance of the Offsite-Tuned models closely aligns with that of models fine-tuned using complete parameters.

Methodology

The procedure involves the following steps:

• Adapter and Emulator Design: The model is divided into a lightweight adapter, which encapsulates task-specific knowledge, and a frozen component, which is compressed to form an emulator.
• Layer-Drop Technique: The emulator is created by layer-dropping, which effectively balances between performance preservation and model privacy.
• Distillation: Emulators are optionally distilled to improve approximation accuracy further without compromising model privacy.

Experimental Results

The experimental evaluation presents strong numerical results, showing that Offsite-Tuning effectively maintains performance across diverse tasks. This is evidenced by:

• Achieving close plug-in performance compared to full fine-tuning.
• Maintaining a significant performance gap between emulator and plug-in performance, ensuring that model privacy remains intact.

Implications and Future Directions

The practical implications of Offsite-Tuning are noteworthy; it facilitates deploying large models on edge devices and handling private data securely. This paper opens a pathway to more personal and efficient AI applications in fields where data confidentiality is paramount, such as healthcare and finance.

Future research could explore further compression techniques for the emulator to handle even larger models like GPT-3 effectively. Additionally, an investigation into theoretical guarantees regarding data and model privacy would further validate the robustness of this approach.

Conclusion

Offsite-Tuning provides a pioneering approach to transfer learning by mitigating the resource-intensive requirements and privacy concerns associated with full model access. Its ability to deliver performance efficiency while preserving privacy positions it as a valuable tool in the machine learning community, encouraging more widespread and responsible use of powerful AI models.