Deep learning with differential privacy (DP) has garnered significant
attention over the past years, leading to the development of numerous methods
aimed at enhancing model accuracy and training efficiency. This paper delves
into the problem of training Transformer models with differential privacy. Our
treatment is modular: the logic is to `reduce' the problem of training DP
Transformer to the more basic problem of training DP vanilla neural nets. The
latter is better understood and amenable to many model-agnostic methods. Such
`reduction' is done by first identifying the hardness unique to DP Transformer
training: the attention distraction phenomenon and a lack of compatibility with
existing techniques for efficient gradient clipping. To deal with these two
issues, we propose the Re-Attention Mechanism and Phantom Clipping,
respectively. We believe that our work not only casts new light on training DP
Transformers but also promotes a modular treatment to advance research in the
field of differentially private deep learning.

本文研究在差分隐私情况下使用深度学习进行 Transformer 模型训练，提出了重新注意机制和幻影剪切作为解决注意力分散现象和梯度剪切问题的方法。这不仅对于训练差分隐私 Transformer 模型提供了新的思路，也推动了差分隐私深度学习领域内的模块化研究。

探究差分隐私 Transformer

Delving into Differentially Private Transformer

The Transformer has emerged as a versatile and effective architecture with
broad applications. However, it still remains an open problem how to
efficiently train a Transformer model of high utility with differential privacy
guarantees. In this paper, we identify two key challenges in learning
differentially private Transformers, i.e., heavy computation overhead due to
per-sample gradient clipping and unintentional attention distraction within the
attention mechanism. In response, we propose DPFormer, equipped with Phantom
Clipping and Re-Attention Mechanism, to address these challenges. Our
theoretical analysis shows that DPFormer can reduce computational costs during
gradient clipping and effectively mitigate attention distraction (which could
obstruct the training process and lead to a significant performance drop,
especially in the presence of long-tailed data). Such analysis is further
corroborated by empirical results on two real-world datasets, demonstrating the
efficiency and effectiveness of the proposed DPFormer.

本文提出了 DPFormer 模型，引入了幽灵剪辑和再关注机制以提高训练 Transformer 模型时的效率和精度，通过理论分析以及两个真实数据集的实验证明了 DPFormer 的有效性和可行性。