Security for IoT using Federated Learning

Abstract

In recent years, the government, universities, and industry have paid attention to the distributed approach in machine learning (ML) and cybersecurity for the developing Internet of Things (IoT). Federated cybersecurity (FC) is a strategy for making the Internet of Things (IoT); more secured and effective in the future. This new approach has an ability to detect problems regarding security, implement solutions, and across the IoT systems it manages effectively to limit the propagation of vulnerabilities. Forming a federation of shared information is one way to attain a cybersecurity. Federated learning (FL) is a machine learning paradigm which is especially effective for securing the sensitive IoT environment. The origin of FL, and also FL for cyber security are presented in this article. The various security assaults, and responses are also discussed as an outcome of this article. Experiments are carried out in Google Colaboratory, using well-known python libraries. The results shows that FL provides highest level of security in comparison with centralised learning.