Collaborative Anomaly Detection in IoT Using Federated Deep Learning

With the exponential growth of the Internet of Things (IoT), ensuring the security of IoT devices and networks has become a significant challenge. Anomaly detection techniques play a pivotal role in identifying unusual behaviors that may indicate cyber threats. Traditional anomaly detection systems often struggle with scalability, privacy concerns, and the need for continuous model improvement. In this paper, we propose a collaborative anomaly detection framework for IoT systems based on Federated Deep Learning (FDL). This framework allows IoT devices to collaboratively train deep learning models for anomaly detection without sharing raw data, thereby addressing privacy issues. The federated approach also improves scalability by reducing the communication overhead typically associated with centralized learning systems. Experimental results demonstrate the effectiveness of the proposed framework in detecting anomalies while preserving privacy and ensuring scalability across diverse IoT devices