Federated Learning: Enhancing Privacy And Efficiency In Decentralized Machine Learning Systems

Abstract

Federated Learning (FL) represents a transformative approach in machine learning, addressing significant concerns related to data privacy and efficiency. This study explores the core principles, benefits, and challenges of FL, emphasizing its decentralized model training process that keeps data local, thereby enhancing privacy. The methodology involves a comprehensive analysis of existing literature and the application of FL across various sectors such as healthcare, finance, and the Internet of Things (IoT). Key findings reveal that FL not only improves data privacy and security but also enhances model accuracy and efficiency by reducing communication overhead and accommodating data heterogeneity. Moreover, FL's applications in healthcare demonstrate its potential for privacy-preserving patient data analysis, collaborative medical research, and personalized treatment modeling. In the financial sector, FL facilitates robust fraud detection, risk management, and collaborative forecasting. In IoT, FL enhances the functionality and security of smart home devices, industrial IoT, and autonomous transportation systems. The implications of these findings suggest that FL is poised to significantly impact multiple domains by enabling secure and efficient collaborative learning without compromising data privacy. Future research directions include the development of stronger privacy-preserving algorithms, optimization of communication protocols, expansion to new sectors, and addressing regulatory and ethical considerations.