Federated Learning: Advancing Privacy-Preserving AI in Decentralized Environments

Abstract

Federated Learning (FL) represents a paradigm shift in machine learning, allowing models to be trained across decentralized data sources while preserving user privacy. This paper provides an in-depth analysis of FL's core principles, including the challenges of communication efficiency, model heterogeneity, and privacy preservation. We explore current solutions, evaluate performance metrics, and discuss real-world applications in mobile edge computing and IoT. Finally, we identify open research areas and future directions for advancing FL.