Federated Learning Based Privacy Ensured Sensor Communication in IoT Networks: A Taxonomy, Threats and Attacks

Our daily lives are significantly impacted by intelligent Internet of Things (IoT) application, services, IoT gadgets, and more intelligent industries. Artificial Intelligence (AI) is anticipated to have a substantial impact on training machine learning algorithms on IoT devices without sharing data. As data privacy has become a serious societal concern, Federated Learning (FL) has emerged as a hot research area for enabling the collaborative training of machine learning models across many smart IoT devices while adhering to privacy constraints. Although, FL is utilized for preserving privacy in IoT networks, but it is also facing some challenges such as privacy, effectiveness, and efficiency. In this article, a taxonomy of FL-based IoT systems is proposed and an analysis of related works on FL-based IoT systems is presented. Further, a comprehensive study of various types of threats, attacks, and frameworks is done. In addition to this, a taxonomy of privacy-preserving FL techniques for IoT networks is also devised. Finally, the study is concluded by highlighting the various open research challenges in FL-based IoT networks.

Automated summary

Our daily lives are significantly influenced by intelligent IoT applications, services, devices, and industries. AI is expected to have a major impact on training machine learning algorithms on IoT devices without sharing data. Federated learning has emerged as a popular research area to address privacy concerns when training machine learning models across IoT devices. However, there are challenges such as privacy, effectiveness, and efficiency. This article proposes a taxonomy of FL-based IoT systems, analyzes related works, studies threats and attacks, devises privacy-preserving FL techniques, and highlights open research challenges.