FLEMING-MS: A Secure and Environmentally Sustainable Intrusion Detection System for AIoT-Driven Medical Supply Chains

Medical supply chains increasingly integrate AI-enabled IoT (AIoT) to improve operational efficiency, reduce waste, and cut costs. However, this digitalization brings new security risks that endanger sensitive patient information, as well as critical medical operations. Existing intrusion detection systems for these configurations either infringe upon patient privacy through data aggregation or rely on constrained resources that diminish the ecological advantages touted by green supply chain frameworks. We propose FLEMING-MS, a federated learning framework for green medical supply chains with AIoT-driven intrusion detection and medical security transfer efficiency optimization. Our system allows boundary intrusions to be detected collaboratively across health facilities without the need to exchange sensitive information and reduces environmental impact through intelligent resource management. FLEMING-MS creates (1) a node selection algorithm that captures medical data from healthcare facilities as nodes with high-quality information; (2) adaptive bandwidth allocation for network resource optimization; (3) transfer performance appraisal for quantifying each facilitys contribution to the global model; and (4) deep deterministic policy gradient algorithm to solve the transfer efficiency maximization problem. Experimental results on NSL-KDD and CIC IoT 2023 datasets demonstrate that FLEMING-MS reduces model training time by 74.3% compared to state-of-the-art approaches while maintaining superior detection performance. FLEMING-MS also achieved a 52.8% reduction in energy consumption and a 53.7% decrease in carbon emissions, offering an environmentally friendly, privacy-preserving, and secure solution to contemporary medical supply chains.