<p>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 (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(n < 15\)</EquationSource> </InlineEquation> 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 <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\((\epsilon ,\delta )\)</EquationSource> </InlineEquation>-differential privacy with <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\epsilon =0.08\)</EquationSource> </InlineEquation>–0.17 and <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\delta =10^{-17}\)</EquationSource> </InlineEquation>, 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 <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(O(\log (1/\epsilon ))\)</EquationSource> </InlineEquation> 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 (<InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(QV=128\)</EquationSource> </InlineEquation>, <InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(F_{\text {gate}}=0.995\)</EquationSource> </InlineEquation>) 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 <InlineEquation ID="IEq8"> <EquationSource Format="TEX">\(<0.5\%\)</EquationSource> </InlineEquation> 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.</p>

Quantum-entangled neuro-symbolic swarm federation for privacy-preserving IoMT-driven multimodal healthcare

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2025年12月9日

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Soufiane Ben Othman,
Obaid Ali

摘要

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 ( \(n < 15\) 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 \((\epsilon ,\delta )\) -differential privacy with \(\epsilon =0.08\) –0.17 and \(\delta =10^{-17}\) , 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 \(O(\log (1/\epsilon ))\) 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 ( \(QV=128\) , \(F_{\text {gate}}=0.995\) ) 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 \(<0.5\%\) 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.