FedQNN: Federated Learning using Quantum Neural Networks
In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a
framework for training Quantum Machine Learning (QML) models via distributed networks …
framework for training Quantum Machine Learning (QML) models via distributed networks …
Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcare
Healthcare industries frequently handle sensitive and proprietary data, and due to strict
privacy regulations, they are often reluctant to share data directly. In today's context …
privacy regulations, they are often reluctant to share data directly. In today's context …
Quantum Fuzzy Federated Learning for Privacy Protection in Intelligent Information Processing
Z Qu, L Zhang, P Tiwari - IEEE Transactions on Fuzzy Systems, 2024 - ieeexplore.ieee.org
With the advent of the intelligent information processing era, more and more private
sensitive data are being collected and analyzed for intelligent decision making tasks. Such …
sensitive data are being collected and analyzed for intelligent decision making tasks. Such …
Quantum Federated Learning Experiments in the Cloud with Data Encoding
Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated
learning (FL) over quantum networks, enabling collaborative quantum model training along …
learning (FL) over quantum networks, enabling collaborative quantum model training along …
Towards quantum federated learning
Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the
principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of …
principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of …
Handling privacy-sensitive clinical data with federated quantum machine learning
Healthcare organizations have a high volume of sensitive data and traditional technologies
have limited storage capacity and computational resources. The balanced protection of …
have limited storage capacity and computational resources. The balanced protection of …
Cryptoqfl: quantum federated learning on encrypted data
Recent advancements in Quantum Neural Networks (QNNs) have demonstrated theoretical
and experimental performance superior to their classical counterparts in a wide range of …
and experimental performance superior to their classical counterparts in a wide range of …
Foundations of quantum federated learning over classical and quantum networks
Quantum federated learning (QFL) is a novel framework that integrates the advantages of
classical federated learning (FL) with the computational power of quantum technologies …
classical federated learning (FL) with the computational power of quantum technologies …
Federated quantum machine learning with differential privacy
The preservation of privacy is a critical concern in the implementation of artificial intelligence
on sensitive training data. There are several techniques to preserve data privacy but …
on sensitive training data. There are several techniques to preserve data privacy but …