Detecting Network Attack using Federated Learning for IoT Devices

This study examines the utilization of federated learning to improve security within Internet of Things (IoT) environments, tackling issues such as data privacy and scalability which are intrinsic to centralized approaches. The IoT connects a wide range of devices, necessitating strong security measures to protect sensitive data and maintain system integrity. Federated learning presents a decentralized remedy by allowing model training directly on edge devices, reducing data transmission to centralized servers and upholding information confidentiality. A primary emphasis is on creating a federated learning-based Intrusion Detection System (IDS) which is specifically designed for the IoT networks, with the aim of effectively detecting and mitigating network attacks while decreasing susceptibilities to data breaches. Experimental validation confirms the system's adaptability to various IoT data distributions and changing network conditions, affirming its practical effectiveness in realworld settings. Improvement of federated learning algorithms for real-time anomaly detection will, therefore, be the subject of further research efforts and the introduction of many emerging more sophisticated encryption techniques in attempts to further bolster the evolving threats against data protection mechanisms. Advanced federated learning towards robust security for IoT contributes towards network resilience that guards sensitive information within interconnected IoT systems providing insights necessary for safe implementations in health care, smart cities, and industrial automation.