Privacy-Enhanced Digital twin Framework for Smart Livestock Management: A Federated Learning Approach with Privacy-Preserving Hybrid Aggregation (PPHA)

Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3576 2019 2019 Privacy-Enhanced Digital twin Framework for Smart Livestock Management: A Federated Learning Approach with Privacy-Preserving Hybrid Aggregation (PPHA) Department of Management Information Systems, College of Business, University of Jeddah, Saudi Arabia Adel Adel Through its integration with the Federated Learning (FL) and Digital Twin (DT) technology, Internet of Things (IoT) based smart livestock farming is revolutionized toward real-time health monitoring and predictive analytics combined with secure decision-making. Privacy risks, inefficient models, large computational overheads, and heterogeneous data remain prominent in existing frameworks. This work introduces a “Privacy-Enhanced Digital Twin Livestock Optimization (PEDLO)” system, combining several adaptive and AI-driven components, including IntelliSense-Livestock Monitoring Framework (ISLMF) for multi-sensor data fusion, Privacy-Preserving Hybrid Aggregation (PPHA) Algorithm for secure federated learning, and Digital Twin-Augmented Reinforcement Learning (DTARL) for simulation-based decision-making. The PEDLO system optimizes disease prediction and anomaly detection, aims to reduce false alarms, and ensures data privacy for enhanced livestock welfare. Experimental results show 0.94 of accuracy, 0.93 of anomaly detection sensitivity, and a 40-second model convergence time, which outperform state-of-the-art techniques by a wide margin. The proposed system will enable scalable, efficient, and secure livestock management, marking a transformative shift toward sustainable precision farming. 2025 2025 01 18 10.54216/JISIoT.160101 https://www.americaspg.com/articleinfo/18/show/3576