FEDERATED LEARNING FOR SECURE AND PRIVACY-PRESERVING ARTIFICIAL INTELLIGENCE IN DISTRIBUTED COMPUTING ENVIRONMENTS

The rapid expansion of Artificial Intelligence (AI) has led to unprecedented growth in data collection and processing across distributed computing environments. However, traditional centralized machine learning approaches require the transfer of massive amounts of user data to centralized servers, creating significant concerns regarding privacy, security, and regulatory compliance. Federated Learning (FL) has emerged as a transformative paradigm that enables collaborative model training without sharing raw data. This paper explores the principles, architecture, advantages, challenges, and applications of Federated Learning in modern computing systems. The study highlights how FL enhances privacy preservation while maintaining model performance and discusses emerging techniques such as differential privacy, secure aggregation, and blockchain integration. The findings demonstrate that Federated Learning represents a promising direction for building trustworthy and scalable AI systems in healthcare, finance, smart cities, and Internet of Things (IoT) applications.