Shaba Shaon

Distributed Quantum Learning over Near-term Devices: Convergence Analysis and Security Design

Atit Pokharel, Shaba Shaon, Thomas Morris, Dinh C. Nguyen

Distributed quantum learning (DQL) has emerged as a promising paradigm to scale quantum-enhanced machine learning by interconnecting multiple quantum devices. However, for efficient real-world deployment, it is essential to characterize how DQL converges under practical scenarios while simultaneously safeguarding multi-device quantum infrastructures …

Distributed Quantum Learning over Near-term Devices: Convergence Analysis and Security Design

Atit Pokharel, Shaba Shaon, Thomas Morris, Dinh C. Nguyen

Distributed quantum learning (DQL) has emerged as a promising paradigm to scale quantum-enhanced machine learning by interconnecting multiple quantum devices. However, for efficient real-world deployment, it is essential to characterize how DQL converges under practical scenarios while simultaneously safeguarding multi-device quantum infrastructures …

Communication-Efficient Quantum Federated Learning over Large-Scale Wireless Networks

Shaba Shaon, Christopher G. Brinton, Dinh C. Nguyen

Quantum federated learning (QFL) combines the robust data processing of quantum computing with the privacy-preserving features of federated learning (FL). However, in large-scale wireless networks, optimizing sum-rate is crucial for unlocking the true potential of QFL, facilitating effective model sharing and aggregation …

Communication-Efficient Quantum Federated Learning over Large-Scale Wireless Networks

Shaba Shaon, Christopher G. Brinton, Dinh C. Nguyen

Quantum federated learning (QFL) combines the robust data processing of quantum computing with the privacy-preserving features of federated learning (FL). However, in large-scale wireless networks, optimizing sum-rate is crucial for unlocking the true potential of QFL, facilitating effective model sharing and aggregation …

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Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach

Ratun Rahman, Shaba Shaon, Dinh Cong Nguyen

Quantum federated learning (QFL) emerges as a powerful technique that combines quantum computing with federated learning to efficiently process complex data across distributed quantum devices while ensuring data privacy in quantum networks. Despite recent research efforts, existing QFL frameworks struggle to achieve …

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Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach

Ratun Rahman, Shaba Shaon, Dinh Cong Nguyen

Quantum federated learning (QFL) emerges as a powerful technique that combines quantum computing with federated learning to efficiently process complex data across distributed quantum devices while ensuring data privacy in quantum networks. Despite recent research efforts, existing QFL frameworks struggle to achieve …

Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach

Ratun Rahman, Shaba Shaon, Dinh Cong Nguyen

Quantum federated learning (QFL) emerges as a powerful technique that combines quantum computing with federated learning to efficiently process complex data across distributed quantum devices while ensuring data privacy in quantum networks. Despite recent research efforts, existing QFL frameworks struggle to achieve …

Resource-Efficient Distributed Quantum Learning over Wireless Networks with Qubit Reuse

Shaba Shaon, Atit Pokharel, Alexander Coutras, Avimanyu Sahoo et autres

Distributed quantum computing (DQC) mitigates the hardware limitations of near-term devices by partitioning large circuits into smaller segments executed on qubit-limited processors. However, even these reduced circuits often exceed the capacity of near-term devices that provide only extremely few reliable qubits. To …

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Communication-Efficient Quantum Federated Learning Over Large-Scale Wireless Networks

Shaba Shaon, Christopher G. Brinton, Dinh C. Nguyen

Quantum federated learning (QFL) combines the robust data processing of quantum computing with the privacy-preserving features of federated learning (FL). However, in large-scale wireless networks, optimizing sum-rate is crucial for unlocking the true potential of QFL, facilitating effective model sharing and aggregation …

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Energy-Efficient Quantum Federated Learning Over Low-Altitude UAV Networks With Theoretical Guarantees

Shaba Shaon, Atit Pokharel, Dinh C. Nguyen

This paper studies a novel energy-efficient quantum federated learning (QFL) framework designed for low-altitude quantum-enabled unmanned aerial vehicle (UAV) networks. We develop a novel energy minimization problem by jointly considering UAV’s quantum computing power, computational frequency, transmit power, number of quantum noise …

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Collaborative Multimodal Learning Over Integrated Aerial–Terrestrial Networks Under Adversarial Attacks

Shaba Shaon, Dinh C. Nguyen, Dusit Niyato, H. Vincent Poor

With the rapid growth of intelligent aerial-terrestrial applications, enabling collaborative multimodal learning (CML) across heterogeneous data sources, such as aerial images from unmanned aerial vehicles (UAVs) and time-series signals from ground edge devices (EDs), has become essential for achieving reliable intelligence beyond …

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Distributed Quantum Learning Over Near-Term Devices: Convergence Analysis and Security Design

Atit Pokharel, Shaba Shaon, Thomas Morris, Dinh C. Nguyen

Distributed quantum learning (DQL) has emerged as a promising paradigm to scale quantum-enhanced machine learning by interconnecting multiple quantum devices. However, for efficient real-world deployment, it is essential to characterize how DQL converges under practical scenarios while simultaneously safeguarding multi-device quantum infrastructures …

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