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

The utility of machine learning has rapidly expanded in the last two decades and presented an ethical challenge. Papernot et. al. developed a technique, known as Private Aggregation of Teacher Ensembles (PATE) to enable federated learning in which multiple distributed teachers are trained on disjoin...

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Bibliographic Details
Main Authors: Watkins, William, Wang, Heehwan, Bae, Sangyoon, Tseng, Huan-Hsin, Cha, Jiook, Chen, Samuel Yen-Chi, Yoo, Shinjae
Format: Conference Proceeding
Language:English
Subjects:
Ensemble learning
Ethics
Federated learning
Neural networks
Privacy
Qubit
Signal processing
Online Access:Request full text
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Summary:The utility of machine learning has rapidly expanded in the last two decades and presented an ethical challenge. Papernot et. al. developed a technique, known as Private Aggregation of Teacher Ensembles (PATE) to enable federated learning in which multiple distributed teachers are trained on disjoint data sets. 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).
ISSN:2379-190X
DOI:10.1109/ICASSP48485.2024.10447786