Quantum-inspired context-aware adaptive federated edge intelligence for privacy-preserving healthcare analytics

Abstract

Privacy-preserving healthcare analytics at the edge requires collaborative learning across distributed Internet of Medical Things (IoMT) devices while maintaining strict privacy guarantees, low inference latency, and high predictive accuracy. Existing federated learning frameworks employ static privacy budgets and fixed communication schedules, failing to adapt to heterogeneous clinical contexts, variable network conditions, and diverse device capabilities. Furthermore, conventional edge intelligence methods struggle to efficiently balance multi-objective trade-offs between privacy, utility, and latency under stringent resource constraints. This paper proposes Quantum-Inspired Context-Aware Adaptive Federated Edge Intelligence (QI-CAF-EI), a novel framework that dynamically co-optimizes privacy, utility, and latency through context-aware adaptive control and quantum-inspired optimization techniques while maintaining full compatibility with classical edge hardware. The framework integrates five tightly coupled functional modules: (1) data acquisition and edge preprocessing for privacy-preserving local analytics, (2) quantum-inspired edge intelligence leveraging tensor network decompositions for efficient model compression, (3) context-aware adaptive control that continuously monitors system state and clinical urgency to adjust privacy budgets and communication schedules, (4) secure federated learning employing differential privacy (DP) and homomorphic encryption for gradient aggregation, and (5) explainability and governance mechanisms ensuring regulatory compliance and clinical interpretability. Comprehensive experimental evaluation across five benchmark healthcare datasets MIMIC-III, PhysioNet, NIH ChestX-ray14, UCI Heart Disease, and WISDM IoMT demonstrates significant performance improvements. QI-CAF-EI achieves 3.7–4.5% accuracy improvement over differentially private federated learning, reduces inference latency by 25–36% compared to edge-only baselines, and decreases communication overhead by 40% while maintaining rigorous DP guarantees (ε ∈ [0.3, 2.0], δ = 10⁻5). Theoretical analysis provides formal convergence guarantees, DP proofs, and complexity bounds. The framework enables real-time, privacy-preserving, and resource-efficient collaborative healthcare analytics, addressing critical requirements for safe AI deployment in modern healthcare ecosystems.