Federated Quantum Machine Learning for Secure Multi-Party Genomic Data Analysis: A Hybrid Architecture with Differential Privacy

David Jumpa Malem Sembiring; Sinek Mehuli Br Perangin Angin

doi:10.56741/jnest.v5i01.1087

Federated Quantum Machine Learning for Secure Multi-Party Genomic Data Analysis: A Hybrid Architecture with Differential Privacy

Authors

David Jumpa Malem Sembiring Institut Teknologi dan Bisnis Indonesia https://orcid.org/0009-0005-5244-904X
Sinek Mehuli Br Perangin Angin Institut Teknologi dan Bisnis Indonesia https://orcid.org/0009-0008-3987-2486

DOI:

https://doi.org/10.56741/jnest.v5i01.1087

Keywords:

Architecture, Federated Learning, Genomic Data, Machine Learning, Quantum

Abstract

The exponential growth of genomic data has created opportunities for collaborative biomedical research while posing significant privacy and security challenges. Traditional centralized machine learning approaches risk data breaches and regulatory non-compliance. This study proposes a novel hybrid architecture combining Federated Quantum Machine Learning (FQML) with Differential Privacy (DP) for secure multi-party genomic data analysis. Our simulated framework enables institutions to collaboratively train quantum machine learning models on distributed genomic datasets without exposing sensitive data. Using variational quantum circuits within a federated learning framework, we implement privacy-preserving models across three simulated genomic data silos. Gaussian noise injection via DP mechanisms during gradient updates ensures formal privacy guarantees (ε = 0.85). Experimental results on synthetic datasets demonstrate that FQML achieves 93.4% predictive accuracy in genome-wide association studies simulation, outperforming classical federated models in convergence speed by 27% and reducing communication overhead by 19%. These findings suggest FQML with DP provides a promising foundation for future secure genomic collaboration, though real-world validation remains necessary.

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Author Biographies

David Jumpa Malem Sembiring, Institut Teknologi dan Bisnis Indonesia

was born in Medan on September 10, 1969. He earned his Diploma in Technology from Universitas Sisingamangaraja XII, a Bachelor’s degree in Informatics Engineering from STMIK Sisingamangaraja XII, a Master’s degree in Computer Science from Universitas Gadjah Mada, and a Doctorate in Computer Science from the University of Sumatera Utara. He is currently the Rector of the Institute of Technology and Business Indonesia. His research focuses on data mining, business intelligence, sentiment analysis, machine learning, and artificial intelligence applications in information systems. (email: davidjmsembiring@itbi.ac.id).

Sinek Mehuli Br Perangin Angin, Institut Teknologi dan Bisnis Indonesia

was born in Kuta Male on May 19, 1970. She earned her Bachelor’s degree in Accounting from Universitas Pembangunan Panca Budi, Medan, in 2006, and a Master’s degree in Informatics Engineering from STMIK Eresha, Jakarta, in 2014. She is a permanent lecturer and has served as Dean of the Faculty of Business and Humanities at the Institute of Technology and Business Indonesia since 2019. Her research focuses on information systems, data management, and IT applications in business. (email: sinekmehulibrperanginangin@itbi.ac.id).

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