Leveraging Public Large Language Models to Enhance Differentially Private Federated Learning of On-device Language Models

Core Concepts

Leveraging public large language models (LLMs) can significantly improve the privacy-utility trade-off and sample efficiency in differentially private federated learning of on-device language models.

Abstract

The paper explores ways to leverage public data and pre-trained LLMs to enhance differentially private federated learning (FL) of on-device language models (LMs). Key insights and findings:

Using subword tokenizers from public LLMs can prevent potential privacy leakage from private tokenizer vocabulary and lead to better learning utility with DP guarantees, compared to unigram tokenizers from private corpus.

Distilling knowledge from public LLMs can significantly improve the sample efficiency of pre-training on-device LMs, achieving similar or better performance than using the full public pre-training corpus, but with only 1% of the data.

The authors propose a novel distribution matching algorithm that leverages both private on-device LMs and public LLMs to sample public records closely matching the private data distribution. This can achieve comparable performance as using the full public pre-training corpus, but with only 0.08% of the data, reducing the public training time from over a week to a few hours.

The proposed techniques demonstrate strong empirical results on the StackOverflow dataset, consistently improving the privacy-utility trade-off for both LSTM and transformer on-device LMs under different DP settings.

Stats

Using 1% of public pre-training data with LLM distillation can achieve similar or better performance than using the full public pre-training corpus.
Carefully sampling 0.08% of public data using the proposed distribution matching algorithm can achieve comparable performance as using the full public pre-training corpus.

Quotes

"Leveraging public data and pre-trained LLMs can significantly improve the privacy-utility trade-off and sample efficiency in differentially private federated learning of on-device language models."
"Our method points to a novel direction of efficiently enhancing private FL with public pretraining data and LLMs."

Key Insights Distilled From

Can Public Large Language Models Help Private Cross-device Federated Learning?

by Boxin Wang,Y... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2305.12132.pdf

Deeper Inquiries

How can the proposed distribution matching algorithm be further improved to better capture the private data distribution, especially when the private and public data distributions exhibit significant divergence?

The proposed distribution matching algorithm aims to sample public data that aligns closely with the private data distribution. To enhance its effectiveness in capturing the private data distribution, especially in cases of significant divergence between private and public data distributions, several improvements can be considered:

Fine-tuning Parameters: Fine-tuning the parameters of the distribution matching algorithm to adapt to the specific characteristics of the private data distribution can improve its performance. This could involve adjusting the weighting of the public and private log-densities in the estimation function to better reflect the true distribution.

Dynamic Sampling: Implementing a dynamic sampling strategy that adjusts the sampling process based on the evolving characteristics of the private data distribution can enhance the algorithm's adaptability. This could involve periodically re-evaluating the distribution matching process and updating the sampling criteria accordingly.

Incorporating Domain Knowledge: Incorporating domain knowledge or domain-specific features into the distribution matching algorithm can help in capturing the nuances of the private data distribution. By leveraging domain expertise, the algorithm can better identify relevant public data samples that align with the private distribution.

Ensemble Methods: Utilizing ensemble methods that combine multiple distribution matching approaches or models can provide a more robust and comprehensive estimation of the private data distribution. By aggregating the outputs of multiple models, the algorithm can capture a broader range of distribution characteristics.

Regularization Techniques: Applying regularization techniques to the distribution matching process can help prevent overfitting and improve generalization to unseen data. Regularization methods can enhance the algorithm's ability to capture the underlying patterns in the private data distribution.

By incorporating these enhancements, the distribution matching algorithm can be further refined to better capture the nuances of the private data distribution, even in cases of significant divergence from the public data distribution.

What are the potential limitations of using public LLMs in private federated learning, and how can they be addressed?

Using public Large Language Models (LLMs) in private federated learning can offer several benefits, but it also poses certain limitations that need to be addressed:

Privacy Concerns: Public LLMs may have been trained on diverse datasets that could include sensitive information, raising privacy concerns when used in private federated learning. To address this, privacy-preserving techniques such as differential privacy can be applied to ensure the protection of user data during model training.

Domain Mismatch: Public LLMs may have been trained on different domains or tasks than the private federated learning setting, leading to a domain mismatch. This can result in suboptimal performance when transferring knowledge from public LLMs to private models. Addressing this limitation involves fine-tuning the public LLMs on domain-specific data or tasks relevant to the private federated learning scenario.

Model Size and Complexity: Public LLMs are often large and computationally intensive, making them challenging to deploy on resource-constrained devices in private federated learning settings. To mitigate this limitation, techniques such as model distillation or model compression can be employed to reduce the size and complexity of the LLMs while preserving their performance.

Data Bias: Public LLMs may be biased towards the data they were trained on, which can introduce bias into the private federated learning process. Addressing data bias involves careful evaluation of the public LLMs and the incorporation of bias mitigation strategies during knowledge transfer to private models.

Generalization: Public LLMs may not generalize well to the specific characteristics of the private data distribution, leading to suboptimal performance in the private federated learning tasks. To enhance generalization, techniques such as transfer learning with fine-tuning on private data can be applied to adapt the public LLMs to the private domain.

By addressing these limitations through appropriate privacy measures, domain adaptation techniques, model optimization strategies, bias mitigation, and generalization methods, the use of public LLMs in private federated learning can be optimized for improved performance and privacy protection.

Can the techniques presented in this work be extended to other machine learning tasks beyond language modeling, such as computer vision or speech recognition?

The techniques presented in the work, such as leveraging public data, using Large Language Models (LLMs), knowledge distillation, and distribution matching, can indeed be extended to other machine learning tasks beyond language modeling, including computer vision and speech recognition. Here's how these techniques can be applied to these domains:

Computer Vision:

Public Data Utilization: Public image datasets can be used to pre-train computer vision models in a federated learning setting, similar to language models in the text domain.
LLM Adaptation: Large pre-trained vision models like CNNs or Transformers can be adapted using knowledge distillation for tasks like object detection, image classification, or segmentation.
Distribution Matching: Techniques similar to distribution matching can be employed to sample public image data that aligns with the private image distribution, improving the sample efficiency of model training.

Speech Recognition:

Public Data Integration: Public speech datasets can be utilized for pre-training speech recognition models in a federated learning framework, enhancing model performance and generalization.
LLM Transfer Learning: Large pre-trained models in speech processing can be leveraged for knowledge distillation to improve the accuracy and efficiency of private speech recognition models.
Domain Adaptation: Techniques like distribution matching can be applied to sample public speech data that closely matches the private speech data distribution, aiding in the training of accurate and robust speech recognition models.

By adapting and extending the techniques presented in the work to computer vision and speech recognition tasks, it is possible to enhance the performance, privacy, and efficiency of machine learning models across diverse domains beyond language modeling.