ArticleScientific reports2025
Quantum-entangled neuro-symbolic swarm federation for privacy-preserving IoMT-driven multimodal healthcare.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The integration of Internet of Medical Things (IoMT) ecosystems with multimodal data, real-time sensors, fMRI/EEG, genomics, and clinical text, holds transformative potential for rare disease diagnostics and personalized medicine. However, ultra-scarce datasets ([Formula: see text] per institution), quantum-era threats (e.g., Shor and Grover algorithms), and stringent regulatory requirements expose critical limitations in centralized AI and classical federated learning. To address these challenges, we propose the Quantum-Entangled Neuro-Symbolic Swarm Federation (QENSSF), a pioneering framework that unifies quantum-entangled differential privacy (QEDP), neuro-symbolic swarm intelligence, and privacy-aware large language model (LLM) fine-tuning within an IoMT-driven architecture. QENSSF introduces four foundational innovations: (1) QEDP, leveraging 9-qubit W-states and variational quantum circuits to achieve [Formula: see text]-differential privacy with [Formula: see text]-0.17 and [Formula: see text], resilient to quantum inference attacks; (2) Neuro-symbolic swarm agents that fuse CNNs, GNNs, LSTMs, and Transformers with symbolic logic, optimized via Quantum-Entangled Particle Swarm Optimization for [Formula: see text] convergence; (3) Federated LLM adaptation using QEDP-masked gradients and symbolic guards to prevent hallucination-induced leaks; and (4) Ethical, explainable AI via dynamic knowledge graphs secured by quantum multi-party computation. Evaluated on IBM's 127-qubit Eagle processor ([Formula: see text], [Formula: see text]) and 128 NVIDIA A100 GPUs across synthetic and real-world datasets (ADNI, UK Biobank, MIMIC-IV), QENSSF achieves 45% higher F1-score, 30% improved ROUGE-L, and [Formula: see text] attack success rate under membership inference. It delivers 6.3 million ops/s (58% faster than FedAvg), consumes only 0.38 kWh (52% less energy), reduces communication overhead to 2.1 Mb/iter (66% lower), and attains 99% fault recovery, all while ensuring regulatory compliance and clinician-trustworthy explanations. QENSSF sets a new standard for secure, efficient, and interpretable AI in resource-constrained, privacy-sensitive healthcare environments.
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