The utility of machine learning has rapidly expanded in the last two decades
and presents an ethical challenge. Papernot et. al. developed a technique,
known as Private Aggregation of Teacher Ensembles (PATE) to enable federated
learning in which multiple teacher models are trained on disjoint datasets.
This study is the first to apply PATE to an ensemble of quantum neural networks
(QNN) to pave a new way of ensuring privacy in quantum machine learning (QML)
models.

该研究是首次将 PATE 技术应用于量子神经网络集合，为量子机器学习模型提供隐私保护的新方法。

量子隐私聚合技术 (Quantum Privacy Aggregation of Teacher Ensembles, QPATE) 用于保护隐私的量子机器学习

Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for  Privacy-preserving Quantum Machine Learning

The Private Aggregation of Teacher Ensembles (PATE)
framework~\cite{PapernotAEGT:ICLR2017} is a versatile approach to
privacy-preserving machine learning. In PATE, teacher models are trained on
distinct portions of sensitive data, and their predictions are privately
aggregated to label new training examples for a student model.
Until now, PATE has primarily been explored with classification-like tasks,
where each example possesses a ground-truth label, and knowledge is transferred
to the student by labeling public examples. Generative AI models, however,
excel in open ended \emph{diverse} tasks with multiple valid responses and
scenarios that may not align with traditional labeled examples. Furthermore,
the knowledge of models is often encapsulated in the response distribution
itself and may be transferred from teachers to student in a more fluid way. We
propose \emph{hot PATE}, tailored for the diverse setting. In hot PATE, each
teacher model produces a response distribution and the aggregation method must
preserve both privacy and diversity of responses. We demonstrate, analytically
and empirically, that hot PATE achieves privacy-utility tradeoffs that are
comparable to, and in diverse settings, significantly surpass, the baseline
``cold'' PATE.

私有教师集成的 PATE（The Private Aggregation of Teacher Ensembles）框架是一种多用途的隐私保护机器学习方法，通过训练教师模型来聚合标记新的学生模型的训练样例。传统的 PATE 主要应用于分类类任务，但是我们提出的 hot PATE 则专为多样化场景设计，通过维持隐私和响应的多样性来实现知识迁移。我们通过分析和实证研究表明，hot PATE 在隐私 - 效用权衡方面与基线 “cold” PATE 相当甚至在多样化场景下表现显著优越。