Privacy-Enhanced AI for IoT: Federated Learning in Security Applications

The growing number of interconnected IoT devices has led to a surge in data generation, which brings about significant concerns regarding data privacy, security, and protection. Traditional approaches to securing IoT networks often require centralized data processing, which compromises privacy and makes sensitive information susceptible to attacks. To address this challenge, we propose a Privacy-First AI Training approach for IoT security based on Federated Learning (FL). This method enables the training of machine learning models without exposing raw data by leveraging decentralized, collaborative learning. Each IoT device trains a local model on its data and only shares model updates, preserving the privacy of users while improving IoT network security through real-time intrusion detection. This paper explores the design, implementation, and evaluation of a privacy-first federated learning model for IoT security, ensuring robust protection against cyber threats.