Privacy-Centric AI: Navigating the Landscape with Federated Learning

Abstract

Abstract: In the era of big data and privacy concerns, federated learning has emerged as a promising approach to training machine learning models while preserving data privacy. This paper explores the principles and applications of federated learning, highlighting its potential to revolutionize privacy-centric AI. We discuss the methodology, significance, and challenges of federated learning, providing insights into its future directions. By leveraging decentralized data and aggregating model updates, federated learning enables the development of powerful AI models without compromising individual privacy. We present real-world applications and cite relevant studies to demonstrate the transformative impact of federated learning across various domains.