{"ID":2868312,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16505","arxiv_id":"2509.16505","title":"orb-QFL: Orbital Quantum Federated Learning","abstract":"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. 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.","short_abstract":"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 Federa...","url_abs":"https://arxiv.org/abs/2509.16505","url_pdf":"https://arxiv.org/pdf/2509.16505v1","authors":"[\"Dev Gurung\",\"Shiva Raj Pokhrel\"]","published":"2025-09-20T02:50:14Z","proceeding":"cs.DC","tasks":"[\"cs.DC\",\"cs.LG\"]","methods":"[]","has_code":false}