Abstract
Federated Learning (FL) has emerged as a transformative paradigm for decentralized AI model training, enabling collaborative learning across distributed data sources while preserving data privacy and security. Unlike traditional centralized training, FL eliminates the need to share raw data, making it particularly appealing in sensitive domains such as healthcare, finance, and IoT networks. This paper explores the diverse applications of FL, highlighting its role in advancing privacy-preserving AI solutions and enhancing model performance in data-constrained scenarios.Despite its promise, FL faces significant challenges, including communication inefficiencies, data heterogeneity, privacy threats, and scalability issues. We review current strategies to address these challenges, such as secure aggregation protocols, model compression techniques, and incentive mechanisms. The paper also discusses future directions, including the integration of FL with blockchain for enhanced trust and auditability, as well as the development of ethical frameworks to ensure fair and responsible use. By providing a comprehensive analysis of FL’s applications and challenges, this study aims to contribute to the ongoing evolution of decentralized AI systems.