Federated Learning (FL) is a new paradigm in distributed machine learning that allows numerous participants to train a common model without exchanging raw data. This strategy improves data privacy and security, addressing major concerns in sensitive domains including healthcare, banking, and mobile applications. Unlike traditional centralized learning, FL moves model training closer to the data source, reducing data leakage risks and boosting regulatory compliance (e.g., GDPR, HIPAA). This paper provides a complete overview of federated learning, including its core architecture, distinct types (horizontal, vertical, and transfer learning), enabling technologies (such as secure multiparty computation, differential privacy, and homomorphic encryption), and real-world applications. The paper also examines the fundamental issues encountered by FL, such as data heterogeneity, communication overhead, and vulnerability to adversarial assaults. The report concludes with interesting research prospects for increasing model performance, security, and scalability in federated environments.
Agrawal et al. (Fri,) studied this question.