Federated Discrete and Transferable Prompt Tuning for Black-Box Large Language Models: Enhancing Privacy and Efficiency in Federated Learning

核心概念

FedDTPT is a novel federated learning framework that enables privacy-preserving and communication-efficient fine-tuning of large language models by optimizing discrete and transferable prompts in a black-box setting.

摘要

Bibliographic Information:

Wu, J., Chen, S., Yang, Y., Li, Y., Hou, S., Jing, R., Wang, Z., Chen, W., & Tian, Z. (2024). FedDTPT: Federated Discrete and Transferable Prompt Tuning for Black-Box Large Language Models. arXiv preprint arXiv:2411.00985.

Research Objective:

This paper introduces FedDTPT, a novel federated learning framework designed to address the challenges of privacy and efficiency in fine-tuning large language models (LLMs) for specific downstream tasks. The research aims to enable the learning of transferable and interpretable prompts while safeguarding both the privacy of the server's model parameters and the client's data.

Methodology:

FedDTPT employs a token-level discrete prompt tuning strategy on the client side, utilizing a feedback loop based on prediction accuracy to drive gradient-free prompt optimization through the MLM API. On the server side, an attention mechanism based on semantic similarity filters prompt tokens from all clients, enhanced by embedding distance elbow detection and DBSCAN clustering for improved selection.

Key Findings:

Experimental results demonstrate that FedDTPT outperforms state-of-the-art methods in terms of accuracy, communication overhead, and robustness to non-iid data in a black-box setting. The optimized prompts also exhibit transferability, allowing their application to other LLMs.

Main Conclusions:

FedDTPT offers a practical and effective solution for privacy-preserving and efficient fine-tuning of LLMs in federated learning scenarios. The use of discrete and transferable prompts addresses limitations associated with continuous prompts, enabling wider applicability and knowledge sharing among clients.

Significance:

This research contributes significantly to the field of federated learning by introducing a novel approach for prompt tuning that prioritizes both privacy and efficiency. The proposed framework has the potential to facilitate collaborative LLM training across multiple devices while mitigating privacy concerns and reducing computational demands.

Limitations and Future Research:

While FedDTPT demonstrates promising results, further exploration is needed to investigate its performance on a wider range of downstream tasks and with larger LLMs. Additionally, future research could explore the integration of more sophisticated clustering algorithms and the development of adaptive strategies for prompt length optimization.

统计

FedDTPT achieves 95.73% accuracy on the QQP dataset using DeepSeek, outperforming manual prompting by over 13%.
When using Llama-3.1, FedDTPT achieves 95.52% accuracy on MRPC and 95.9% on WNLI, significantly surpassing other black-box methods.
FedDTPT requires only 150 trainable parameters when using Llama-3.1, significantly fewer than continuous prompt learning methods.
Removing the feedback loop in client-level optimization results in a consistent drop in performance across all tasks.
Compared to the baseline method Server-1, FedDTPT demonstrates improvements of 65.51% for SST-2 and 36.97% for MRPC.

引用

"To address the aforementioned challenges, we propose FedDTPT [which] eliminates the need for clients to access model parameters and requires only forward propagation for local training, reducing computational and storage demands for both devices and LLM service providers."
"Additionally, our discrete prompts are interpretable to developers and can be transferred to other LLMs without any modifications."

从中提取的关键见解

FedDTPT: Federated Discrete and Transferable Prompt Tuning for Black-Box Large Language Models

by Jiaqi Wu, Si... 在 arxiv.org 11-05-2024

https://arxiv.org/pdf/2411.00985.pdf

更深入的查询

How might the principles of FedDTPT be applied to other domains beyond natural language processing, such as computer vision or robotics, where federated learning is increasingly relevant?

The core principles of FedDTPT, namely federated discrete and transferable prompt tuning, hold significant potential for adaptation to domains beyond NLP, particularly in computer vision and robotics where data privacy and model transferability are crucial. Here's how:
Computer Vision:

Discrete Prompt Representation: Instead of text prompts, we can utilize discrete visual tokens or image patches as prompts. These could represent salient features or regions of interest within an image.
Federated Prompt Optimization: Each client, equipped with its local image data, can optimize these discrete visual prompts using a feedback loop similar to FedDTPT. The feedback signal could be based on the accuracy of image classification, object detection, or other relevant tasks.
Semantic Aggregation: The central server can aggregate the learned visual prompts from different clients using techniques analogous to FedDTPT's semantic similarity-based attention mechanism. This could involve clustering similar visual tokens or identifying representative image patches.
Transferable Visual Prompts: The aggregated, discrete visual prompts can then be transferred to other computer vision models or tasks, enabling efficient adaptation without sharing raw image data.
Robotics:

Action Prompts: Discrete prompts could represent sequences of robot actions or control commands.
Federated Learning in Robot Swarms: Individual robots in a swarm, each with its unique experiences and sensor data, can act as clients, collaboratively optimizing these action prompts for a shared task (e.g., navigation, object manipulation).
Prompt Aggregation for Collective Intelligence: The central server can combine the learned action prompts, potentially giving higher weight to prompts from robots that achieved better performance in their local environments.
Transferable Action Knowledge: The aggregated prompts can be disseminated back to the robots, enabling the transfer of learned knowledge and improved performance across the swarm.
Challenges and Considerations:

Domain-Specific Prompt Design: Defining meaningful and effective discrete prompts for computer vision and robotics tasks will require careful consideration of the specific domain knowledge and task requirements.
Efficient Embedding and Similarity Metrics: Adapting embedding techniques and similarity metrics used in FedDTPT to handle visual or action-based prompts will be crucial.
Handling Data Heterogeneity: Addressing the challenges of non-IID data distributions, common in both computer vision and robotics, will be essential for effective prompt aggregation and knowledge transfer.

While FedDTPT focuses on mitigating privacy risks, could the reliance on a central server for prompt aggregation introduce potential vulnerabilities to adversarial attacks or single points of failure?

You are right to point out that while FedDTPT offers privacy advantages by avoiding the direct sharing of raw data, the central server, acting as the aggregator of locally tuned prompts, does become a critical point to consider in terms of security vulnerabilities and potential points of failure.
Here's a breakdown of the potential risks and mitigation strategies:
Adversarial Attacks:

Prompt Poisoning: Malicious clients could inject carefully crafted prompts designed to bias the global model towards incorrect or undesirable outputs. This is analogous to data poisoning attacks in traditional federated learning.
Inference Attacks on Aggregated Prompts: Analyzing the aggregated prompts on the server might reveal sensitive information about the underlying data distributions across clients, even though the raw data itself is not directly shared.
Mitigation Strategies:

Robust Aggregation Techniques: Implementing robust aggregation methods that are less susceptible to outlier prompts or malicious contributions is crucial. This could involve techniques like median aggregation, trimmed mean, or more sophisticated methods like Byzantine-tolerant averaging.
Differential Privacy: Introducing noise into the prompt aggregation process using differential privacy techniques can help mask the contributions of individual clients, making it harder for attackers to infer sensitive information.
Prompt Verification: Developing mechanisms to verify the validity and trustworthiness of prompts received from clients before inclusion in the aggregation process could help filter out potentially malicious prompts.
Single Points of Failure:

Server Downtime: If the central server experiences downtime, it disrupts the prompt aggregation process and potentially halts the entire federated learning system.
Server Compromise: A security breach at the server could expose all the aggregated prompts, potentially compromising the privacy benefits of the federated learning approach.
Mitigation Strategies:

Decentralized Architectures: Exploring decentralized prompt aggregation mechanisms, such as using blockchain technologies or peer-to-peer communication protocols, could eliminate the reliance on a single central server.
Redundancy and Fault Tolerance: Implementing redundancy measures, such as backup servers or distributed storage of aggregated prompts, can enhance the system's resilience to server failures.
Secure Communication and Storage: Employing strong encryption protocols for communication between clients and the server, as well as for storing the aggregated prompts, is essential to mitigate the impact of potential security breaches.

Considering the increasing emphasis on explainable AI, how can the interpretability of discrete prompts in FedDTPT be further leveraged to enhance trust and transparency in federated learning applications?

The interpretability of discrete prompts in FedDTPT offers a significant advantage for enhancing trust and transparency in federated learning, a domain where understanding the decision-making process is often opaque. Here's how we can leverage this:
1. Prompt Visualization and Analysis:

Human-Readable Representations: Unlike continuous embeddings, discrete prompts can be directly presented to users in a human-readable format. Visualizing the evolution of these prompts over federated learning rounds can provide insights into how the global model is adapting.
Identifying Important Features: Analyzing the frequency and position of specific tokens within the aggregated prompts can highlight which features or concepts are most relevant for a given task across the client data.
2. Prompt-Level Explanations:

Rationale Generation: Techniques can be developed to generate natural language explanations alongside the model's predictions, directly based on the learned discrete prompts. For example, if a model predicts a sentiment as "positive" based on a prompt containing the token "amazing," the explanation could highlight this token's contribution.
Counterfactual Analysis: By systematically altering or removing specific tokens from the prompt and observing the impact on the model's predictions, we can gain a deeper understanding of how individual prompt components influence the decision-making process.
3. Auditing and Bias Detection:

Prompt Bias Assessment: The interpretability of discrete prompts allows for easier auditing for potential biases. Analyzing the language and sentiment embedded within the prompts can reveal if the model is unfairly favoring certain demographics or viewpoints.
Fairness-Aware Prompt Aggregation: Developing aggregation techniques that explicitly consider fairness metrics, potentially down-weighting prompts that contribute to biased outcomes, can promote more equitable and trustworthy federated learning models.
4. User-Centric Trust Building:

Prompt Customization and Control: Allowing users some degree of control over the prompt generation or selection process can enhance trust and transparency. For instance, users could have the option to provide initial prompts or specify constraints on the language used.
Interactive Prompt Refinement: Enabling users to provide feedback on the generated prompts and iteratively refine them in collaboration with the model can foster a sense of ownership and trust in the federated learning process.
By focusing on these strategies, we can leverage the inherent interpretability of discrete prompts in FedDTPT to move beyond simply protecting data privacy and towards building more explainable, trustworthy, and user-centric federated learning applications.