Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Conference on Innovative Mechanisms for Industry Applications

Integrating Federated Learning in Quantum Computing for Secure and Scalable Model Training

D. K. Shareef, T. Shakeel, E. Hariprasad, A. Naresh, G. Naga Rama Devi, Naga Venkatesh Gangabathula

Published Sep 3, 2025• 3 citations
Publisher
DOI
10.1109/icimia67127.2025.11200828

Related papers

Quantum computing meets federated learning

Kaifeng Bu

Science China Physics Mechanics and Astronomy•Nov 8, 2021•4 citations

When Federated Learning Meets Quantum Computing: Survey and Research Opportunities

Aakar Mathur, Ashish Gupta, Sajal Kumar Das

Quantum Federated Learning (QFL) is an emerging field that harnesses advances in Quantum Computing (QC) to improve the scalability and efficiency of decentralized Federated Learning (FL) models. This paper provides a systematic and comprehensive survey of the emerging problems and solutions when FL meets QC, from research protocol to a novel taxonomy, particularly focusing on both quantum and federated limitations, such as their architectures, Noisy Intermediate Scale Quantum (NISQ) devices, and privacy preservation, so on. With the introduction of two novel metrics, qubit utilization efficiency and quantum model training strategy, we present a thorough analysis of the current status of the QFL research. This work explores key developments and integration strategies, along with the impact of QC on FL, keeping a sharp focus on hybrid quantum-classical approaches. The paper offers an in-depth understanding of how the strengths of QC, such as gradient hiding, state entanglement, quantum key distribution, quantum security, and quantum-enhanced differential privacy, have been integrated into FL to ensure the privacy of participants in an enhanced, fast, and secure framework. Finally, this study proposes potential future directions to address the identified research gaps and challenges, aiming to inspire faster and more secure QFL models for practical use.

arXiv:2504.08814•IEEE Communications Surveys & Tutorials•Nov 18, 2025•14 citations

Introduction to quantum federated machine learning

Samuel Yen-Chi Chen, Shinjae Yoo

Elsevier eBooks•Jan 1, 2024•5 citations

Computer Vision in the Era of Big Data: A Deep Learning-Based Analytical Perspective

R. Ruth Shobitha, E Anbalagan, Khaled Alqawasmi +2 more

The exponential growth of bioinformatics data, including genomic sequences and protein structures, necessitates advanced analytical frameworks that ensure privacy, computational efficiency, and scalability. To address these challenges, we propose an integrated framework that combines Federated Learning (FL) with Quantum Computing (QC) for secure and efficient bioinformatics data analysis across distributed healthcare institutions. The proposed Quantum Federated Learning (QFL) architecture employs variational quantum circuits to expedite local model training, achieving a 35% reduction in computation time compared to classical FL methods. To strengthen security, Quantum Secure Communication Protocols (QSCP) are incorporated, resulting in over 40% enhanced resilience against adversarial attacks relative to conventional encryption schemes. Experimental validation on distributed genomic datasets, such as the Cancer Genome Atlas, demonstrates that QFL attains a classification accuracy of 94.8% in gene expression-based cancer subtype prediction, surpassing traditional FL by 6.3%. Furthermore, the model exhibits a 25% reduction in communication overhead through quantum state compression, ensuring efficient data exchange. This hybrid approach effectively addresses the critical requirements of data privacy, computational acceleration, and model robustness in bioinformatics, establishing a promising pathway for secure and scalable precision medicine solutions empowered by quantum-enhanced federated learning.

Asia Pacific Conference on Innovation in Technology (APCIT)•Sep 19, 2025•0 citations

A secure quantum federated learning for lightweight quantum clients

Wei‐Min Shi, Zi-Xuan Xu, Yu‐Guang Yang +1 more

Chinese Journal of Physics•Jul 6, 2026•0 citations

When Quantum Federated Learning Meets Blockchain in 6G Networks

Dinh C. Nguyen, Md Bokhtiar Al Zami, Ratun Rahman +3 more

Quantum federated learning (QFL) is emerging as a key enabler for intelligent, secure, and privacy-preserving model training in next-generation 6G networks. By leveraging the computational advantages of quantum devices, QFL offers significant improvements in learning efficiency and resilience against quantum-era threats. However, future 6G environments are expected to be highly dynamic, decentralized, and data-intensive, which necessitates moving beyond traditional centralized federated learning frameworks. To meet this demand, blockchain technology provides a decentralized, tamper-resistant infrastructure capable of enabling trustless collaboration among distributed quantum edge devices. This paper presentsQFLchain, a novel framework that integrates QFL with blockchain to support scalable and secure 6G intelligence. In this work, we investigate four key pillars ofQFLchainin the 6G context: 1) communication and consensus overhead; 2) scalability and storage overhead; 3) energy inefficiency; and 4) security vulnerability. A case study is also presented, demonstrating potential advantages ofQFLchain, based on simulation, over state-of-the-art approaches in terms of training performance.

arXiv:2512.09958•IEEE Communications Standards Magazine•Jan 1, 2025•1 citation
Quantum Federated Learning (QFL) is an emerging field that harnesses advances in Quantum Computing (QC) to improve the scalability and efficiency of decentralized Federated Learning (FL) models. This paper provides a systematic and comprehensive survey of the emerging problems and solutions when FL meets QC, from research protocol to a novel taxonomy, particularly focusing on both quantum and federated limitations, such as their architectures, Noisy Intermediate Scale Quantum (NISQ) devices, and privacy preservation, so on. With the introduction of two novel metrics, qubit utilization efficiency and quantum model training strategy, we present a thorough analysis of the current status of the QFL research. This work explores key developments and integration strategies, along with the impact of QC on FL, keeping a sharp focus on hybrid quantum-classical approaches. The paper offers an in-depth understanding of how the strengths of QC, such as gradient hiding, state entanglement, quantum key distribution, quantum security, and quantum-enhanced differential privacy, have been integrated into FL to ensure the privacy of participants in an enhanced, fast, and secure framework. Finally, this study proposes potential future directions to address the identified research gaps and challenges, aiming to inspire faster and more secure QFL models for practical use.
The exponential growth of bioinformatics data, including genomic sequences and protein structures, necessitates advanced analytical frameworks that ensure privacy, computational efficiency, and scalability. To address these challenges, we propose an integrated framework that combines Federated Learning (FL) with Quantum Computing (QC) for secure and efficient bioinformatics data analysis across distributed healthcare institutions. The proposed Quantum Federated Learning (QFL) architecture employs variational quantum circuits to expedite local model training, achieving a 35% reduction in computation time compared to classical FL methods. To strengthen security, Quantum Secure Communication Protocols (QSCP) are incorporated, resulting in over 40% enhanced resilience against adversarial attacks relative to conventional encryption schemes. Experimental validation on distributed genomic datasets, such as the Cancer Genome Atlas, demonstrates that QFL attains a classification accuracy of 94.8% in gene expression-based cancer subtype prediction, surpassing traditional FL by 6.3%. Furthermore, the model exhibits a 25% reduction in communication overhead through quantum state compression, ensuring efficient data exchange. This hybrid approach effectively addresses the critical requirements of data privacy, computational acceleration, and model robustness in bioinformatics, establishing a promising pathway for secure and scalable precision medicine solutions empowered by quantum-enhanced federated learning.
Quantum federated learning (QFL) is emerging as a key enabler for intelligent, secure, and privacy-preserving model training in next-generation 6G networks. By leveraging the computational advantages of quantum devices, QFL offers significant improvements in learning efficiency and resilience against quantum-era threats. However, future 6G environments are expected to be highly dynamic, decentralized, and data-intensive, which necessitates moving beyond traditional centralized federated learning frameworks. To meet this demand, blockchain technology provides a decentralized, tamper-resistant infrastructure capable of enabling trustless collaboration among distributed quantum edge devices. This paper presentsQFLchain, a novel framework that integrates QFL with blockchain to support scalable and secure 6G intelligence. In this work, we investigate four key pillars ofQFLchainin the 6G context: 1) communication and consensus overhead; 2) scalability and storage overhead; 3) energy inefficiency; and 4) security vulnerability. A case study is also presented, demonstrating potential advantages ofQFLchain, based on simulation, over state-of-the-art approaches in terms of training performance.