Article

FedVQC for Genomic Data: A Quantum-Enhanced Privacy Approach

2026-01-30

Abstract excerpt

<title>Abstract</title> <p>This study presents a Federated Quantum Variational Quantum Classifier (FedVQC) framework designed to address critical challenges in healthcare data privacy and computational efficiency, particularly in the domain of genomic analysis. Conventional machine learning approaches in healthcare often face obstacles stemming from privacy concerns, regulatory constraints, and the fragmented nat...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 42c8517f-903b-5e6f-91df-c37c1a0c180d
DOI: 10.21203/rs.3.rs-8727224/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

FedVQC for Genomic Data: A Quantum-Enhanced Privacy ApproachDOI 10.21203/rs.3.rs-8727224/v1Read

Quantum federated learning in healthcare: The shift from development to deployment and from models to dataSemantic proximity Read

Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic EncryptionSemantic proximity Read

A Hybrid Quantum Resistant Secure Framework for Federated Healthcare Cyber-Physical SystemsSemantic proximity Read

NoisyFlow: Differentially Private Optimal Transport Using Neural Networks for Secure Biomedical Data SharingSemantic proximity Read

Quantum Machine Learning Algorithms for Genome Disease DiagnosisSemantic proximity Read

Secure secondary utilization system of genomic data using quantum secure cloudSemantic proximity Read

FedDP: Secure Federated Learning for Disease Prediction with Imbalanced Genetic DataSemantic proximity Read

FedXGB-OptDP: A Privacy-Optimised Federated XGBoost Framework with Differential Privacy for IID and Non-IID healthcare dataSemantic proximity Read

Federated Learning for the pathogenicity annotation of genetic variants in multi-site clinical settingsSemantic proximity Read

Development, implementation, and validation of an open-source Federated Learning platform to accelerate innovation and boost personalized medicine in rare and ultra-rare haematological diseases: an initiative by GenoMed4All ConsortiumSemantic proximity Read

PPML-Omics: a Privacy-Preserving federated Machine Learning method protects patients’ privacy in omic dataSemantic proximity Read

Legal aspects of privacy-enhancing technologies in genome-wide association studies and their impact on performance and feasibilitySemantic proximity Read