AS Bhatia, DEB Neira - arXiv preprint arXiv:2405.07735, 2024 - arxiv.org
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 …
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 …
Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated learning (FL) over quantum networks, enabling collaborative quantum model training along …
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 …
A Bhatia, S Kais, M Alam - APS March Meeting Abstracts, 2023 - ui.adsabs.harvard.edu
Healthcare organizations have a high volume of sensitive data and traditional technologies have limited storage capacity and computational resources. The balanced protection of …
C Chu, L Jiang, F Chen - 2023 IEEE International Conference …, 2023 - ieeexplore.ieee.org
Recent advancements in Quantum Neural Networks (QNNs) have demonstrated theoretical and experimental performance superior to their classical counterparts in a wide range of …
Quantum federated learning (QFL) is a novel framework that integrates the advantages of classical federated learning (FL) with the computational power of quantum technologies …
R Rofougaran, S Yoo, HH Tseng… - ICASSP 2024-2024 …, 2024 - ieeexplore.ieee.org
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 …
Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially …