QFedAstro: Quantum-Enhanced Federated Learning with Adaptive Mission Intelligence for Astronaut Health Monitoring
Shhaurya Jaiswal
Deep-space medical surveillance necessitates decentralized AI frameworks that are resilient to communication latency and resource scarcity. This study introduces QFedAstro, which integrates variational quantum circuits with federated learning for astronaut health anomaly detection across heterogeneous mission profiles. The architecture employs five synergistic modules: quantum-classical hybrid optimization (QCHO) navigating non-convex loss landscapes via superposition-inspired parameter updates; adaptive mission intelligence networks (AMIN) reconfiguring layer weights based on gravity-radiation-isolation vectors; temporal-spatial health embedding (TSHE) capturing physiological interdependencies through graph attention; privacy-preserving biometric fusion (PPBF) applying mission-calibrated differential noise (ϵ = 0.5, δ = 10 -5 ); and cross-mission knowledge crystallization (CMKC) enabling inter-environment transfer learning. Validation across 150-astronaut simulations spanning ISS, Lunar, Martian, and Deep Space contexts yields 96.73% accuracy—2.61% above centralized baselines—while achieving 147x privacy amplification and 68% bandwidth reduction. Paired t-tests confirmed significance (p < 0.001, Cohen’s d = 2.41). The framework challenges conventional privacy-utility tradeoffs, demonstrating that quantum augmentation enables the concurrent optimization of fidelity and sovereignty in mission-critical federated systems.
Procedia Computer Science•Jan 1, 2026•0 citations