FEDERATED LEARNING FOR PRIVACY-PRESERVING INTELLIGENT SYSTEMS: A COMPREHENSIVE STUDY OF ARCHITECTURES, APPLICATIONS, AND CHALLENGES

The rapid growth of artificial intelligence has increased the demand for large-scale datasets to train accurate machine learning models. However, centralized data collection raises significant concerns regarding privacy, security, and regulatory compliance. Federated Learning (FL) has emerged as an innovative distributed machine learning paradigm that enables multiple devices or organizations to collaboratively train models without sharing raw data. Instead, only model parameters are exchanged, preserving user privacy while maintaining model performance. This paper presents a comprehensive review of federated learning, including its architecture, workflow, advantages, real-world applications, challenges, and future research directions. The paper also compares federated learning with traditional centralized machine learning approaches and discusses emerging technologies such as secure aggregation, differential privacy, blockchain-enabled federated learning, and edge intelligence. The findings indicate that federated learning has the potential to become a key technology for privacy-preserving artificial intelligence across healthcare, finance, smart cities, and the Internet of Things.