Federated Learning-Based Privacy-Preserving Framework for Distributed IoT Networks

Abstract

The Internet of Things (IoT) has emerged as a transformative technology that connects everyday physical objects to the internet, enabling them to send, receive, and process data. IoT applications span a wide range of domains, including smart homes, healthcare, agriculture, and industrial automation. These IoT systems generate vast amounts of data, which need to be processed efficiently to extract meaningful insights. Traditional approaches to processing such data often rely on centralized systems, where all the data is sent to a central server for analysis. However, this centralized approach poses significant challenges, especially in terms of privacy, communication overhead, and energy consumption.