Federated Learning for IoT: A Decentralized Approach to Enhance Privacy and Efficiency in Cyber-Physical Systems

Abstract

The rapid proliferation of Internet of Things (IoT) devices has revolutionized cyber-physical systems, enabling a myriad of applications ranging from smart cities to health monitoring. However, the centralized data processing approaches that often underpin these systems raise significant concerns regarding user privacy, data security, and inefficient resource utilization [1][2]. This manuscript presents a comprehensive exploration of Federated Learning (FL) as a decentralized approach to address these challenges. We elucidate the conceptual framework of FL, highlighting its ability to facilitate collaborative model training across multiple IoT devices without the need for raw data to leave their local environments. This preserves the confidentiality of sensitive information while still enabling the generation of robust machine learning models. We detail the implementation of FL in various domains of IoT, showcasing its potential to enhance efficiency by leveraging the computational power of edge devices [3].