Strengthening the Security of IoT Devices Through Federated Learning: A Comprehensive Study

There is a strong need for having an operative security framework which can help in making IoT (Internet of Things) devices more secure and reliable which can further protect from adversarial intrusions. Federated Learning, due to its decentralized architecture, has emerged as one of the ideal choices by the research practitioners in order to protect sensitive data from wide IoT-based attacks like DoS (Denial of Service) attack, Device Tampering, Sensor-Data manipulation etc. This paper discusses the significance of federated learning in addressing security concerns with IoT (Internet of Things) devices and how those issues can be minimized with the use of Federated Learning has been deliberated with the help of comparative analysis. In order to perform this comparative analysis, we investigated the published work in FL based IoT application for the last five years i.e., 2018–2022. We have defined a few inclusion/exclusion criteria and based on that we selected the desired paper and provided a comprehensive solution to IoT based applications using FL approach. Federated learning offers an optimistic approach to intensify security in IoT environments by enabling collaborative model training while preserving information privacy. In this paper a framework named Federated AI Technology Enabler (FATE) has been envisaged which is one of the recommended frameworks in safeguarding security and privacy measures of IoT devices.