Decentralized AI for Secure IoT: Federated Learning Meets Intrusion Detection

The proliferation of Internet of Things (IoT) devices has significantly expanded the attack surface for cyber threats, necessitating robust security measures. Traditional Intrusion Detection Systems (IDS) often rely on centralized architectures, which can compromise data privacy and scalability. This paper explores the integration of Federated Learning (FL) into IDS for IoT networks, enabling decentralized model training while preserving data privacy. By leveraging local computation and aggregating model updates, FL facilitates collaborative learning across distributed IoT devices. The proposed approach aims to enhance detection accuracy, reduce latency, and maintain user privacy, addressing the challenges posed by the dynamic and heterogeneous nature of IoT environments.