Enhancing Threat Detection in IoT Networks through Federated Learning: A Collaborative Approach to Cybersecurity

Abstract

The Internet of Things (IoT) has revolutionized the way devices communicate and operate, providing numerous benefits across various sectors. However, the proliferation of IoT devices also presents significant cybersecurity challenges, including vulnerability to attacks and data privacy concerns. This paper explores the application of federated learning as a collaborative approach to enhance threat detection in IoT networks. By enabling decentralized model training, federated learning preserves data privacy while improving the accuracy of threat detection algorithms. The proposed framework is evaluated against traditional centralized approaches, highlighting the effectiveness and efficiency of federated learning in mitigating cybersecurity threats in IoT environments.