collaborators

7 papers

cs.CR2025

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

Dev Gurung, Shiva Raj Pokhrel

Quantum Federated Learning (QFL) promises to revolutionize distributed machine learning by combining the computational power of quantum devices with collaborative model training. Y…

cs.CR2025

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

Dev Gurung, Shiva Raj Pokhrel

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 auto…

cs.CR2025

QuantumShield: Multilayer Fortification for Quantum Federated Learning

Dev Gurung, Shiva Raj Pokhrel

In this paper, we propose a groundbreaking quantum-secure federated learning (QFL) framework designed to safeguard distributed learning systems against the emerging threat of quant…

cs.DC2025

orb-QFL: Orbital Quantum Federated Learning

Dev Gurung, Shiva Raj Pokhrel

Recent breakthroughs in quantum computing present transformative opportunities for advancing Federated Learning (FL), particularly in non-terrestrial environments characterized by…

cs.DC2025

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

Dev Gurung, Shiva Raj Pokhrel

Low Earth orbit (LEO) constellations violate core assumptions of standard (quantum) federated learning (FL): client-server connectivity is intermittent, participation is time varyi…

cs.LG2025

Communication Efficient Adaptive Model-Driven Quantum Federated Learning

Dev Gurung, Shiva Raj Pokhrel

Training with huge datasets and a large number of participating devices leads to bottlenecks in federated learning (FL). Furthermore, the challenges of heterogeneity between multip…