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. 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.
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. 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.
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.
With the rapid advancement of Low Earth Orbit (LEO) satellite constellations, ensuring the security of satellite-to-ground communication has become a critical concern. Traditional cryptographic techniques, while effective against classical attacks, face increasing threats from quantum computing. This paper proposes a quantum key secure transmission method tailored for LEO satellite-ground communication scenarios. The method integrates quantum key distribution (QKD) with dynamic channel estimation and orbital mobility prediction to support secure and low-latency key handshakes between satellites and ground stations. A hybrid encryption strategy is introduced, where QKD-generated keys are used to refresh symmetric session keys in real time, significantly enhancing resistance to eavesdropping and man-in-the-middle attacks. Simulation results demonstrate that the proposed method ensures stable key distribution under Doppler-shifted and intermittent link conditions, achieving improved secrecy capacity and resilience against quantum-level adversaries. This work provides a viable and scalable quantum-safe communication solution for future secure satellite networks.
Recent advances in low Earth orbit (LEO) satellites have made it possible to achieve zero blind spots on Earth. Considering the give locations of these devices, this makes satellite-ground links between quantum devices a practical possibility. This paper proposes the first quantum federated learning (QFL) application in satellite-ground communication. To improve communication and computing performance, this paper adopts slimmable quantum federated learning (SQFL) and slimmable quantum neural networks (sQNN), which allow for two different configurations in quantum neural networks: the angle and pole configurations. This paper also employs superposition coding and successive decoding to increase communication opportunities. Through extensive experiments, the proposed satellite-ground SQFL framework performs well and is both computationally and communicationally efficient compared to classical federated learning and QFL.
Dynamic Radio Resource Allocation (RRA) in Low Earth Orbit-Space Integrated Ground Networks (LEOSIGNs) presents unprecedented challenges due to extreme dynamism, sparse resource availability, and heterogeneous quality-of-service requirements across satellite constellations like Starlink, OneWeb, and Kuiper. This paper introduces a paradigmshifting Quantum-Optimized Deep Learning with Federated Edge Intelligence (QO-FEDI) framework that synergistically integrates: (1) Variational Quantum Circuits (VQC) for decomposing the NP-hard RRA problem into parametric quantum optimization, (2) federated attention-based predictive models across satellite clusters for privacy-preserving demand forecasting, and (3) adaptive edge-compute resource coordination for sub- 100 ms allocation latency. Novel contributions include: mathematical formulation of quantum cost Hamiltonians capturing interference constraints as entanglement patterns, federated learning topology with Byzantine-robust aggregation, and spatio-temporal attention mechanisms for orbital trajectory-aware predictions. Extensive evaluations across 15 realistic LEO scenarios with varying satellite configurations (32-256 satellites), traffic patterns (1−25Gbps), and environmental conditions demonstrate: (1)71%spectrum efficiency improvement (reaching6.2bps/Hz), (2) 58% latency reduction (achieving 8.4 ms average), (3) 82 % handover optimization with & 96 % success rates, and (4) superior scalability to1000+satellite constellations. Comparative analysis with 8 state-of-the-art methods validates near-optimal solution quality (96.1 % vs. theoretical bounds). Real-world testbed deployment on constellation simulator validates practical applicability and deployment readiness for commercial satellite operators.
Recent advancements in Quantum Neural Networks (QNNs) have demonstrated theoretical and experimental performance superior to their classical counterparts in a wide range of applications. However, existing centralized QNNs cannot solve many real-world problems because collecting large amounts of training data to a common public site is time-consuming and, more importantly, violates data privacy. Federated Learning (FL) is an emerging distributed machine learning framework that allows collaborative model training on decentralized data residing on multiple devices without breaching data privacy. Some initial attempts at Quantum Federated Learning (QFL) either only focus on improving the QFL performance or rely on a trusted quantum server that fails to preserve data privacy. In this work, we propose CryptoQFL, a QFL framework that allows distributed QNN training on encrypted data. CryptoQFL is (1) secure, because it allows each edge to train a QNN with local private data, and encrypt its updates using quantum homomorphic encryption before sending them to the central quantum server; (2) communication-efficient, as CryptoQFL quantize local gradient updates to ternary values, and only communicate non-zero values to the server for aggregation; and (3) computation-efficient, as CryptoQFL presents an efficient quantum aggregation circuit with significantly reduced latency compared to state-of-the-art approaches.
Single-photon quantum communications (QC) offers the attractive feature of 'future proof', forward security rooted in the laws of quantum physics. Ground based quantum key distribution (QKD) experiments in optical fiber have attained transmission ranges in excess of 200km, but for larger distances we proposed a methodology for satellite-based QC. Over the past decade we have devised solutions to the technical challenges to satellite-to-ground QC, and we now have a clear concept for how space-based QC could be performed and potentially utilized within a trusted QKD network architecture. Functioning as a trusted QKD node, a QC satellite ('QC-sat') could deliver secret keys to the key stores of ground-based trusted QKD network nodes, to each of which multiple users are connected by optical fiber or free-space QC. A QC-sat could thereby extend quantum-secured connectivity to geographically disjoint domains, separated by continental or inter-continental distances. In this paper we describe our system concept that makes QC feasible with low-earth orbit (LEO) QC-sats (200-km-2,000-km altitude orbits), and the results of link modeling of expected performance. Using the architecture that we have developed, LEO satellite-to-ground QKD will be feasible with secret bit yields of several hundred 256-bit AES keys per contact. With multiple ground sites separated by {approx} 100km, mitigation of cloudiness over any single ground site would be possible, potentially allowing multiple contact opportunities each day. The essential next step is an experimental QC-sat. A number of LEO-platforms would be suitable, ranging from a dedicated, three-axis stabilized small satellite, to a secondary experiment on an imaging satellite. to the ISS. With one or more QC-sats, low-latency quantum-secured communications could then be provided to ground-based users on a global scale. Air-to-ground QC would also be possible.