Security of Quantum Federated Machine Learning With Blockchain for Electronics Health Records
Abstract
The continuous growth of healthcare data requires robust architectures and solutions. Thanks to Quantum Federated Machine Learning (QFML), which combines quantum computing power, federated learning, and blockchain technology, that enhances decentralization and privacy-preserving benefits. However, the combination of these technologies brings a new level of complexity. This paper presents a systematic literature review on the security of QFML in blockchain for healthcare. Our review shows the limitations and the lack of adequate and robust solutions to address the current vulnerabilities. The review then paves the road for advancing the security of the QFML system on blockchain for healthcare applications.
How strong is the evidence?
Evidence Hub reads this as a review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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