Abstract

Federated Learning (FL) is an emerging machine learning paradigm designed to enable model training across decentralized data sources without requiring data to be transferred or centralized. This approach is especially valuable in environments where data privacy, regulatory compliance, and communication efficiency are paramount, such as healthcare, finance, and edge computing. Traditional machine learning methods typically require data to be aggregated in a central server, raising concerns about data privacy and security. Federated Learning addresses these concerns by keeping data on local devices and sharing only model updates, thereby preserving data sovereignty. This paper provides a comprehensive analysis of Federated Learning in distributed systems, focusing on its architecture, advantages, and the technical challenges it presents. We explore the different types of FL—including horizontal, vertical, and federated transfer learning—and explain how each is suited to specific application contexts. We also investigate critical issues such as communication overhead, model convergence, data heterogeneity, and security threats including poisoning and inference attacks. The methodology section discusses state-of-the-art FL frameworks, including Google's Federated Averaging (FedAvg), Secure Aggregation protocols, and emerging advancements like differential privacy and homomorphic encryption. Real-world implementations in mobile networks, autonomous vehicles, and medical diagnosis systems are examined to demonstrate FL’s growing applicability. The paper concludes by emphasizing the transformative potential of Federated Learning in enabling privacy-preserving AI. It also highlights the need for standardized protocols, legal frameworks, and interdisciplinary collaboration to fully harness FL’s benefits while mitigating its risks. As AI continues to permeate sensitive domains, FL offers a promising path forward for ethical and secure machine learning