Enhancing IoT Security through Federated Transfer Learning Approaches

As the Internet of Things (IoT) continues to grow, the security challenges in these networks increase in complexity and scale. Traditional intrusion detection methods often struggle to handle the dynamic and diverse nature of IoT environments. Federated Learning (FL) has emerged as a promising solution to enhance privacy and scalability in IoT security, allowing multiple devices to collaboratively learn from data without sharing it. However, due to the heterogeneity of IoT devices and the limited data available on individual devices, Federated Transfer Learning (FTL) is gaining traction as a means to improve model performance across diverse devices. This paper explores the integration of Federated Learning and Transfer Learning approaches to enhance IoT security, particularly in intrusion detection systems (IDS). By leveraging pre-trained models and transferring knowledge across devices, FTL helps overcome the challenges posed by limited data and diverse network environments. We propose an FTL-based framework for intrusion detection, focusing on its ability to generalize across various IoT devices and network conditions. Experimental results demonstrate the effectiveness of this approach, providing enhanced detection accuracy, faster convergence, and reduced communication overhead compared to traditional methods.