TrustFedLLM-ZT: An LLM-Assisted Zero-Trust Federated Learning Framework for Intelligent Healthcare
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.