FEDERATED LEARNING FOR DATA PRIVACY AND SECURITY IN INDUSTRIAL APPLICATIONS

Abstract

The new opportunities in data privacy and security come with the rapid growth of the Internet of Things (IoT) and Cloud computing in industrial applications. IoT networks, through which physical objects exchange information in real-time, fundamentally alter the industrials using them for productivity and efficiency. With these technologies being integrated into each other, data security and privacy issues, as well as the resilience of the networks to external challenges, are the critical issues involved. Since data is rapidly growing and being shared globally through IoT and cloud-based systems, the concerns about security data breaches and unauthorized access about compliance with regulatory standards keep growing. Data is being increasingly shared across the globe through IoT and cloud-based systems. This has led to rising concerns about data breaches and unauthorized access in regard to compliance with regulatory standards. This paper deals with an in-depth study of federated learning as a framework to protect industrial IoT and cloud systems. This paper shall assess whether federated learning will be able to address security risks, tackle compliance challenges, maintain data integrity, and ensure that the optimal performance of the system can be ascertained under varying forms of workload. In addition to that, the paper puts forward significant challenges, such as requirements of standardization in security protocols and regulatory frameworks, and promotes future research directions toward the powerful adoption of federated learning in industrial applications.

Keywords: IoT, Federated Learning, Data Privacy, Cloud Computing, Industrial Applications, Security, Information Technology