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

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.