The widespread use of Large Language Models (LLMs) in AI-driven Internet of Medical Things (IoMT) ecosystems has introduced new challenges in areas such as data privacy, communication burden, computational efficiency and collaborative learning in a secure manner. To overcome these challenges, we introduce a novel Trustworthy Large Language Model-Assisted Zero-Trust Federated Learning Framework (TrustFedLLM-ZT) for secure and privacy-preserving healthcare intelligence. The proposed framework embeds Zero-Trust principles within federated learning (FL) and LLM-assisted intelligent model optimization, enhancing the continuous authentication, decentralized privacy protection, secure model aggregation, and trustworthy clinical decision support without compromising sensitive patient data. The framework is tested with the MIMIC-IV healthcare dataset and simulated in the PyTorch Federated Learning (PySyft) environment. Five critical performance metrics, namely, Privacy Preservation Rate, Training Time, Response Time, Communication Cost and Computation Cost, are used for comparison between experimental results and the conventional CNN-LSTM model, Blockchain-Assisted Federated Learning (BAFL), and Zero-Trust Federated Learning (ZT-FL) models. Proposed TrustFedLLM-ZT achieves PPR of 99.1% that is 14.5%, 8.2%, and 4.1% higher than CNN-LSTM, BAFL and ZT-FL, respectively. Moreover, it decreases the Training Time by 27.4%, Response Time by 31.6%, Communication Cost by 35.8% and Computation Cost by 29.7% in comparison with the current approaches. The outcomes illustrate that the TrustFedLLM-ZT approach effectively balances security, computational and communication efficiency, while offering clear benefits in the secure collaborative learning context and highlighting its potential as a robust and scalable solution for future AI-powered medical applications and privacy-aware intelligent healthcare systems.
Harika B, Vishwesh Nagamalla, Kodipaka Rajeshwar Rao et al.· 2026 International Conferenc...· 0 citations
It is demonstrated that the proposed method is more effective, in terms of the detection, stability, and convergence behavior, than the existing centralized and federated IDS models, and can effectively deal with non-IID data distribution and the extensibility of the approach to different types of distributed network environment.
Jothi Prabha Appadurai, Revoori Swetha, V. Srinivas et al.· Scientific Reports· 1 citation
This paper proposes an Intelligent Link Failure Prediction and QoS-Aware Routing framework for Mobile Ad Hoc Networks (MANETs) using the Harris Hawk Optimization (HHO) algorithm to achieve reliable and efficient data transmission under highly dynamic network conditions. The proposed model integrates proactive link failure prediction with HHO-based multi-objective route optimization to select stable, energy-efficient, and QoS-compliant paths. The framework is implemented and evaluated using the SimPy simulation environment under a realistic node mobility and traffic workload generated using Random Waypoint mobility with CBR and VBR traffic patterns, which is widely adopted for MANET performance evaluation. The proposed method is compared with Multi-Agent Deep Learning (MADL), Multi-Agent Deep Reinforcement Learning (MADRL), and the Communication-Aware Hierarchical Routing Framework (CAHRF). Experimental results demonstrate that the HHO-based approach significantly improves network performance by increasing the Packet Delivery Ratio (PDR) by 9.8-15.6%, reducing Link Failure Recovery Time by 21.4-34.7%, extending Network Lifetime by 18.2-27.9%, and decreasing Control Packet Cost by 16.5-25.3% compared to the benchmark methods. These improvements confirm that the proposed HHO-driven intelligent routing framework provides a robust, scalable, and QoS-aware solution for reliable communication in highly dynamic MANET environments.
K. Helini, Malleswari Lakkapogu, Suryateja Kothuru et al.· 2026 7th International Confe...· 0 citations
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