Publications

A list of my peer-reviewed journal articles, conference papers, and preprints focusing on quantum/machine learning, federated learning, post/quantum security, wireless communications, Applied/AI, distributed network architectures etc.

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20262 publications

Peer-Reviewed

Communication-Efficient Adaptive Model-Driven Quantum Federated Learning

D. Gurung, et al. — IEEE Transactions on Networking

Training federated learning (FL) at scale suffers from severe communication and heterogeneity constraints. These challenges are amplified in quantum federated learning (QFL), especially under non-IID data. We propose a model-driven QFL (mdQFL) framework that addresses communication overhead, scalability, and client drift through adaptive clustering and representative aggregation.Read full abstractTraining federated learning (FL) at scale suffers from severe communication and heterogeneity constraints. These challenges are amplified in quantum federated learning (QFL), especially under non-IID data. We propose a model-driven QFL (mdQFL) framework that addresses communication overhead, scalability, and client drift through adaptive clustering and representative aggregation. The framework enables structured personalization and efficient update compression across rounds. mdQFL is the first QFL approach to jointly analyze training efficiency, personalization, and test generalization under heterogeneous conditions. Experiments across multiple datasets and quantum platforms show 50% communication reduction while maintaining or improving accuracy over standard QFL baselines. We provide convergence guarantees and communication complexity bounds to establish scalability and robustness.Show less

Communication EfficiencyAdaptive ModelQFLAcceptedIEEE ToNCore A*

DOI

Peer-Reviewed

Towards Quantum Teleportation Based Barrage Relay Networks

D. Gurung, et al. — 40th International Conference on Information Networking (ICOIN)

In this research, we are the first to examine the potential and practicality of achieving a quantum computing advantage within communication relay networks. Barrage Relay networks (BRNs) offer a low-latency and resilient network structure capable of preventing collisions through self-directed cooperation, thus enabling a robust low-latency broadcast system. We extend BRN by designing a multi-hop quantum teleportationbased relay network.Read full abstractIn this research, we are the first to examine the potential and practicality of achieving a quantum computing advantage within communication relay networks. Barrage Relay networks (BRNs) offer a low-latency and resilient network structure capable of preventing collisions through self-directed cooperation, thus enabling a robust low-latency broadcast system. We extend BRN by designing a multi-hop quantum teleportationbased relay network. This work discovers a theoretical framework for a multi-relay strategy for quantum teleportation tailored for tactical ad-hoc networking in the post-quantum era, utilizing quantum devices along with preliminary experimental analyses to support our proposed protocol using Qiskit.Show less

Quantum NetworkingQuantum CommunicationPeer-Reviewed

DOI

202511 publications

Peer-Reviewed

Chained Continuous Quantum Federated Learning Framework

D. Gurung, et al. — Future Generation Computer Systems

The integration of quantum machine learning into federated learning paradigms is poised to transform the future of technologies that depend on diverse machine learning methodologies. This research delves into Quantum Federated Learning (QFL), presenting an initial framework modeled on the Federated Averaging (FedAvg) algorithm, implemented via Qiskit. Despite its potential, QFL encounters critical challenges... To address this, we introduce a chained continuous QFL framework (ccQFL).Read full abstractThe integration of quantum machine learning into federated learning paradigms is poised to transform the future of technologies that depend on diverse machine learning methodologies. This research delves into Quantum Federated Learning (QFL), presenting an initial framework modeled on the Federated Averaging (FedAvg) algorithm, implemented via Qiskit. Despite its potential, QFL encounters critical challenges, including (i) susceptibility to a single point of failure, (ii) communication bottlenecks, and (iii) uncertainty in model convergence. Subsequently, we dive deeper into QFL and propose an innovative alternative to traditional server-based QFL. Our approach introduces a chained continuous QFL framework (ccQFL), which eliminates the need for a central server and the FedAvg method. In our framework, clients engage in a chained continuous training process, where they exchange models and collaboratively enhance each other's performance. This approach improves both the efficiency of communication and the accuracy of the training process. Our experimental evaluation includes a proof-of-concept to demonstrate initial feasibility and a prototype study simulating TCP/IP communication between clients. This simulation enables concurrent operations, verifying the potential of ccQFL for real-world applications. We examine various datasets, including Iris, MNIST, synthetic and Genomic, covering a range of data sizes from small to large. For further validity of our proposed method, we extend our experimental analysis in other frameworks such as PennyLane and TensorCircuit where we include various ablation studies covering major considerations and factors that impact the framework to study validity, robustness, practicality, and others. Our results show that the ccQFL framework achieves model convergence, and we evaluate other critical metrics such as performance and communication delay. In addition, we provide a theoretical analysis to establish and discuss many factors such as model convergence, communication costs, etc.Show less

Quantum MLFederated LearningContinuous LearningCore A

DOI

Peer-Reviewed

Quantum Federated Learning for Metaverse: Analysis, Design and Implementation

D. Gurung, et al. — IEEE Transactions on Network and Service Management

We present a novel decentralized and trustworthy Quantum Federated Learning (QFL) framework tailored for the emerging Metaverse. This virtual environment, enabling social interaction, gaming, and commerce, demands secure and transparent systems. By integrating blockchain, our QFL framework ensures integrity, resilience, and transparency.Read full abstractWe present a novel decentralized and trustworthy Quantum Federated Learning (QFL) framework tailored for the emerging Metaverse. This virtual environment, enabling social interaction, gaming, and commerce, demands secure and transparent systems. By integrating blockchain, our QFL framework ensures integrity, resilience, and transparency. Comparative analysis with classical Federated Learning (CFL) highlights its practicality and advantages in distributed settings. New insights developed emphasize the importance of decentralized systems for the Metaverse's evolution, with a blockchain-based QFL application demonstrated in a hybrid model. Our evaluation, implementation details and code are publicly available 1.Show less

QMLMetaverseNetwork ManagementQ1 Journal

DOI

Peer-Reviewed

Performance Analysis and Design of a Weighted Personalized Quantum Federated Learning

D. Gurung, et al. — IEEE Transactions on Artificial Intelligence

Advances in federated and quantum computing have improved data privacy and efficiency in distributed systems. Quantum Federated Learning (QFL), like its classical counterpart, Classic Federated Learning (CFL), struggles with challenges in heterogeneous environments. To address these, we propose wp-QFL, a weighted personalized approach with quantum federated averaging (qFedAvg), tackling non-IID data and local model drift.Read full abstractAdvances in federated and quantum computing have improved data privacy and efficiency in distributed systems. Quantum Federated Learning (QFL), like its classical counterpart, Classic Federated Learning (CFL), struggles with challenges in heterogeneous environments. To address these, we propose wp-QFL, a weighted personalized approach with quantum federated averaging (qFedAvg), tackling non-IID data and local model drift. While CFL personalization has been well explored, its application to QFL remains underdeveloped due to inherent differences. The proposed wp-QFL fills this gap by adapting to data heterogeneity with weighted personalization and drift correction. The code implementation is available at https://github.com/s222416822/wpQFL.Show less

QMLPersonalizationWeighted FLQ1 Journal

DOI

Preprint

Quantum Vanguard: Server Optimized Privacy Fortified Federated Intelligence for Future Vehicles

D. Gurung, et al. — Arxiv (Submitted)

This work presents vQFL (vehicular Quantum Federated Learning), a new framework that leverages quantum machine learning techniques to tackle key privacy and security issues in autonomous vehicular networks. Furthermore, we propose a server-side adapted fine-tuning method, ft-VQFL,to achieve enhanced and more resilient performance.Read full abstractThis work presents vQFL (vehicular Quantum Federated Learning), a new framework that leverages quantum machine learning techniques to tackle key privacy and security issues in autonomous vehicular networks. Furthermore, we propose a server-side adapted fine-tuning method, ft-VQFL,to achieve enhanced and more resilient performance. By integrating quantum federated learning with differential privacy and quantum key distribution (QKD), our quantum vanguard approach creates a multi-layered defense against both classical and quantum threats while preserving model utility. Extensive experimentation with industry-standard datasets (KITTI, Waymo, and nuScenes) demonstrates that vQFL maintains accuracy comparable to standard QFL while significantly improving privacy guaranties and communication security. Our implementation using various quantum models (VQC, QCNN, and SamplerQNN) reveals minimal performance overhead despite the added security measures. This work establishes a crucial foundation for quantum-resistant autonomous vehicle systems that can operate securely in the post-quantum era while efficiently processing the massive data volumes (20-40TB/day per vehicle) generated by modern autonomous fleets. The modular design of the framework allows for seamless integration with existing vehicular networks, positioning vQFL as an essential component for future intelligent transportation infrastructure.Show less

Vehicular NetworksPrivacyServer OptimizationUnder Review

arXiv

Preprint

Scaling Trust in Quantum Federated Learning: A Multi-Protocol Privacy Design

D. Gurung, et al. — Arxiv (Submitted)

Quantum Federated Learning (QFL) promises to revolutionize distributed machine learning by combining the computational power of quantum devices with collaborative model training. Yet, privacy of both data and models remains a critical challenge. In this work, we propose a privacy-preserving QFL framework where a network of quantum devices trains local models and transmits them to a central server under a multi-layered privacy protocol.Read full abstractQuantum Federated Learning (QFL) promises to revolutionize distributed machine learning by combining the computational power of quantum devices with collaborative model training. Yet, privacy of both data and models remains a critical challenge. In this work, we propose a privacy-preserving QFL framework where a network of quantum devices trains local models and transmits them to a central server under a multi-layered privacy protocol. Our design leverages Singular Value Decomposition (SVD), Quantum Key Distribution (QKD), and Analytic Quantum Gradient Descent (AQGD) to secure data preparation, model sharing, and training stages. Through theoretical analysis and experiments on contemporary quantum platforms and datasets, we demonstrate that the framework robustly safeguards data and model confidentiality while maintaining training efficiency.Show less

PrivacyMulti-Protocol SecurityCryptographyUnder Review

arXiv

Preprint

QuantumShield: Multilayer Fortification for Quantum Federated Learning

D. Gurung, et al. — Arxiv (Submitted)

In this paper, we propose a groundbreaking quantum-secure federated learning (QFL) framework designed to safeguard distributed learning systems against the emerging threat of quantum-enabled adversaries. As classical cryptographic methods become increasingly vulnerable to quantum attacks, our framework establishes a resilient security architecture that remains robust even in the presence of quantum-capable attackers.Read full abstractIn this paper, we propose a groundbreaking quantum-secure federated learning (QFL) framework designed to safeguard distributed learning systems against the emerging threat of quantum-enabled adversaries. As classical cryptographic methods become increasingly vulnerable to quantum attacks, our framework establishes a resilient security architecture that remains robust even in the presence of quantum-capable attackers. We integrate and rigorously evaluate advanced quantum and post-quantum protocols including Quantum Key Distribution (QKD), Quantum Teleportation, Key Encapsulation Mechanisms (KEM) and Post-Quantum Cryptography (PQC) to fortify the QFL process against both classical and quantum threats. These mechanisms are systematically analyzed and implemented to demonstrate their seamless interoperability within a secure and scalable QFL ecosystem. Through comprehensive theoretical modeling and experimental validation, this work provides a detailed security and performance assessment of the proposed framework. Our findings lay a strong foundation for next-generation federated learning systems that are inherently secure in the quantum era.Show less

SecurityFortificationQuantum ThreatsUnder Review

arXiv

Preprint

sat-QFL: Secure Quantum Federated Learning for Low Orbit Satellites

D. Gurung, et al. — Arxiv (Submitted)

Low Earth orbit (LEO) constellations violate core assumptions of standard (quantum) federated learning (FL): client-server connectivity is intermittent, participation is time varying, and latency budgets are strict. We present sat-QFL, a hierarchical, access aware quantum federated learning (QFL) framework that partitions satellites into primary (ground connected) and secondary as inter-satellite links (ISL-only) roles, and schedules sequential, simultaneous, or asynchronous edge training aligned with visibility windows.Read full abstractLow Earth orbit (LEO) constellations violate core assumptions of standard (quantum) federated learning (FL): client-server connectivity is intermittent, participation is time varying, and latency budgets are strict. We present sat-QFL, a hierarchical, access aware quantum federated learning (QFL) framework that partitions satellites into primary (ground connected) and secondary as inter-satellite links (ISL-only) roles, and schedules sequential, simultaneous, or asynchronous edge training aligned with visibility windows. For quantum-resilient confidentiality and integrity, sat-QFL integrates quantum key distribution (QKD) based key establishment with authenticated encryption for model exchange; we also assess teleportation as a feasibility primitive for quantum state transfer. Using derived constellation traces and QFL workloads (Qiskit), we show that sat-QFL sustains robust aggregation under varying participation and reduces communication bottlenecks with modest security overhead. Our implementation and results are available at https://github.com/s222416822/satQFL.Show less

Satellite NetworksSecurityQFLUnder Review

arXiv

Under Review

Hierarchical Clustered Personalized Quantum Federated Learning Framework

D. Gurung, et al. — Under Review (Internal)

Quantum Federated Learning (QFL) faces significant challenges due to statistical heterogeneity, particularly with non-IID data. To address this, we propose a personalized QFL algorithm based on hierarchical clustering, pQFL-HC, which employs clustering of clients based on the similarity of their locally trained model parameters.

ClusteringPersonalizationQFLUnder Review

Not Publicly Available

Preprint

LLM-QFL: Distilling Large Language Model for Quantum Federated Learning

D. Gurung, et al. — Arxiv

Inspired by the power of large language models (LLMs), our research adapts them to quantum federated learning (QFL) to boost efficiency and performance. We propose a federated fine-tuning method that distills an LLM within QFL, allowing each client to locally adapt the model to its own data while preserving privacy and reducing unnecessary global updates.Read full abstractInspired by the power of large language models (LLMs), our research adapts them to quantum federated learning (QFL) to boost efficiency and performance. We propose a federated fine-tuning method that distills an LLM within QFL, allowing each client to locally adapt the model to its own data while preserving privacy and reducing unnecessary global updates. The fine-tuned LLM also acts as a reinforcement agent, optimizing QFL by adjusting optimizer steps, cutting down communication rounds, and intelligently selecting clients. Experiments show significant efficiency gains. We pioneer a synergy between LLM and QFL, offering: i) practical efficiency: Reduced communication costs and faster convergence. ii) theoretical rigor: Provable guarantees for adaptive federated optimization. iii) scalability: PEFT methods (LoRA, QLoRA) enable deployment on resource-constrained quantum devices. Code implementation is available here 1.Show less

LLMModel DistillationQFLUnder Review

arXiv

Preprint

Quantum Federated Learning: A Review and Comprehensive Analysis

D. Gurung, et al. — Arxiv

A detailed survey and critical analysis of the current state, open challenges, and future directions of Quantum Federated Learning. (Under Review)

ReviewSurveyQFLUnder Review

arXiv(Not Updated Version)

Preprint

orb-QFL: Orbital Quantum Federated Learning

D. Gurung, et al. — Arxiv

Recent breakthroughs in quantum computing present transformative opportunities for advancing Federated Learning (FL), particularly in non-terrestrial environments characterized by stringent communication and coordination constraints. In this study, we propose orbital QFL, termed orb-QFL, a novel quantum-assisted Federated Learning framework tailored for Low Earth Orbit (LEO) satellite constellations.Read full abstractRecent breakthroughs in quantum computing present transformative opportunities for advancing Federated Learning (FL), particularly in non-terrestrial environments characterized by stringent communication and coordination constraints. In this study, we propose orbital QFL, termed orb-QFL, a novel quantum-assisted Federated Learning framework tailored for Low Earth Orbit (LEO) satellite constellations. Distinct from conventional FL paradigms, termed orb-QFL operates without centralized servers or global aggregation mechanisms (e.g., FedAvg), instead leveraging quantum entanglement and local quantum processing to facilitate decentralized, inter-satellite collaboration. This design inherently addresses the challenges of orbital dynamics, such as intermittent connectivity, high propagation delays, and coverage variability. The framework enables continuous model refinement through direct quantum-based synchronization between neighboring satellites, thereby enhancing resilience and preserving data locality. To validate our approach, we integrate the Qiskit quantum machine learning toolkit with Poliastro-based orbital simulations and conduct experiments using Statlog dataset.Show less

OrbitalQFLSatelliteUnder Revision

arXiv

20242 publications

Peer-Reviewed

Performance Analysis and Evaluation of Postquantum Secure Blockchained Federated Learning

D. Gurung, et al. — Computer Networks

As the field of quantum computing progresses, traditional cryptographic algorithms such as RSA and ECDSA are becoming increasingly vulnerable to quantum-based attacks, underscoring the need for robust post-quantum security in critical systems like Federated Learning (FL) and Blockchain. In light of this, we propose a novel hybrid approach for blockchain-based FL (BFL) that integrates a stateless signature scheme, such as Dilithium or Falcon, with a stateful hash-based scheme like XMSS.Read full abstractAs the field of quantum computing progresses, traditional cryptographic algorithms such as RSA and ECDSA are becoming increasingly vulnerable to quantum-based attacks, underscoring the need for robust post-quantum security in critical systems like Federated Learning (FL) and Blockchain. In light of this, we propose a novel hybrid approach for blockchain-based FL (BFL) that integrates a stateless signature scheme, such as Dilithium or Falcon, with a stateful hash-based scheme like XMSS. This combination leverages the complementary strengths of both schemes to provide enhanced security. To further optimize performance, we introduce a linear formula-based device role selection method that takes into account key factors such as computational power and stake accumulation. This selection process is reinforced by a verifiable random function (VRF), which strengthens the blockchain consensus mechanism. Our extensive experimental results demonstrate that this hybrid approach significantly enhances both the security and efficiency of BFL systems, establishing a robust framework for the integration of post-quantum cryptography as we transition into the quantum computing era.Show less

PQCBlockchainFederated LearningCore A

DOI

Peer-Reviewed

A Personalized Quantum Federated Learning

D. Gurung, et al. — Proceedings of the 8th Asia-Pacific Workshop on Networking (APNet '24)

We develop a novel method by combining weighted personalization with quantum federated averaging to address impending challenges such as non-IID data distribution and client drift. The proposed weighted personalized Quantum Federated Learning (wpQFL) dynamically adapts to data heterogeneity, improving performance, validated through theoretical insights and empirical observations.Read full abstractWe develop a novel method by combining weighted personalization with quantum federated averaging to address impending challenges such as non-IID data distribution and client drift. The proposed weighted personalized Quantum Federated Learning (wpQFL) dynamically adapts to data heterogeneity, improving performance, validated through theoretical insights and empirical observations.Show less

QMLPersonalizationNetworkingConference

DOI

20231 publications

Peer-Reviewed

Secure Communication Model for Quantum Federated Learning: A Proof Of Concept

D. Gurung, et al. — 11th International Conference on Learning Representations (ICLR) Tiny Papers

We design a model of Post Quantum Cryptography (PQC) Quantum Federated Learning (QFL). We develop a proof of concept with a dynamic server selection and study convergence and security conditions. We develop a preliminary study with a proof of concept model of post-quantum secure QFL.Read full abstractWe design a model of Post Quantum Cryptography (PQC) Quantum Federated Learning (QFL). We develop a proof of concept with a dynamic server selection and study convergence and security conditions. We develop a preliminary study with a proof of concept model of post-quantum secure QFL.Show less

SecurityQFLPoC

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