Efficient Federated Prompt Tuning for Black-box Large Pre-trained Models

Original Paper: https://arxiv.org/abs/2310.03123

By: Zihao Lin, Yan Sun, Yifan Shi, Xueqian Wang, Lifu Huang, Li Shen, Dacheng Tao

Abstract:

With the blowout development of pre-trained models (PTMs), the efficient tuning of these models for diverse downstream applications has emerged as a pivotal research concern. Although recent investigations into prompt tuning have provided promising avenues, three salient challenges persist: (1) memory constraint: the continuous growth in the size of open-source PTMs renders fine-tuning, even a fraction of their parameters, challenging for many practitioners. (2) model privacy: existing PTMs often function as public API services, with their parameters inaccessible for effective or tailored fine-tuning. (3) data privacy: the fine-tuning of PTMs necessitates high-quality datasets, which are typically localized and not shared to public. To optimally harness each local dataset while navigating memory constraints and preserving privacy, we propose Federated Black-Box Prompt Tuning (Fed-BBPT). This innovative approach eschews reliance on parameter architectures and private dataset access, instead capitalizing on a central server that aids local users in collaboratively training a prompt generator through regular aggregation. Local users leverage API-driven learning via a zero-order optimizer, obviating the need for PTM deployment. Relative to extensive fine-tuning, Fed-BBPT proficiently sidesteps memory challenges tied to PTM storage and fine-tuning on local machines, tapping into comprehensive, high-quality, yet private training datasets. A thorough evaluation across 40 datasets spanning CV and NLP tasks underscores the robustness of our proposed model.

Summary Notes

Efficient Federated Prompt Tuning for Large Models

In the dynamic field of Artificial Intelligence (AI), large pre-trained models (PTMs) like GPT-3 and LLaMA are revolutionizing various sectors.

Despite their capabilities, deploying these models for specific tasks presents significant challenges, such as memory limitations and privacy concerns. The Federated Black-Box Prompt Tuning (Fed-BBPT) framework offers a solution, particularly for AI Engineers in enterprise environments facing these hurdles.

Challenges with Large PTMs

Large PTMs bring unmatched benefits but also face considerable obstacles:

• Memory Constraints: Their enormous size demands extensive memory and computational power, making fine-tuning difficult.
• Model Privacy: Often provided as proprietary black-box APIs, these models cannot be directly fine-tuned.
• Data Privacy: The lack of publicly available, high-quality datasets, especially in sensitive areas, limits personalized training opportunities.

Fed-BBPT Framework Overview

Fed-BBPT combines federated learning with black-box prompt tuning to address these issues, allowing for collaborative prompt generator training across multiple users without direct model access or data sharing.

Key Features

• Eliminates Local Deployment: Avoids the need for local PTM deployment, reducing memory and computational requirements.
• Boosts Privacy: Safeguards model and data privacy during training.
• Lowers Resource Needs: Reduces client-side resource demands, ideal for enterprises.

Methodology

System Architecture

At its core, the Fed-BBPT has a central server managing local prompt models' training, which interact with a PTM in the cloud via API calls.

This setup supports collaborative learning without sharing sensitive data.

Training Process

1. Prompt Generation and API Calls: Users create prompts with their data, sent to the PTM through an API.
2. Local Prompt Model Updates: PTM responses help update local models using zero-order optimization.
3. Model Aggregation: The central server periodically combines these models to improve training.

Experiments and Results

Tested over 40 different datasets in computer vision and natural language processing, Fed-BBPT showed its effectiveness, matching or outperforming existing methods in efficiency, performance, and privacy.

Conclusion and Future Directions

Fed-BBPT represents a significant step forward in applying large PTMs practically, addressing key challenges without sacrificing performance.

It shows promise for scenarios with strict privacy needs.

Future Work: Opportunities include exploring more complex datasets, comparing additional methods, and incorporating advanced federated learning techniques. Fed-BBPT sets the stage for future AI advancements, enabling efficient and secure use of large PTMs.

Illustrations:

• Figure 1: Shows the Fed-BBPT framework's interaction between users, the central server, and the PTM.
• Table 1: Compares various methods, emphasizing Fed-BBPT's strengths in efficiency and privacy.

In summary, Fed-BBPT offers a groundbreaking approach to overcoming the challenges of large PTMs, paving the way for innovative, private, and collaborative AI development.