(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2025)
Federated Machine Learning (FL) is a decentralized machine learning paradigm that enables multiple parties to collaboratively train shared models while keeping their data localized, thus preserving privacy and complying with data protection regulations. This research investigates the design, implementation, and evaluation of federated learning systems on Amazon Web Services (AWS) for privacy‑preserving applications in two sensitive domains: financial forecasting and healthcare analytics. By leveraging AWS services such as Amazon SageMaker, Amazon Elastic Kubernetes Service (EKS), and cloud infrastructure security features, the proposed framework supports collaborative model training across institutions (e.g., banks and hospitals) without centralizing raw data. Privacy‑enhancing techniques—including differential privacy, secure aggregation, and encrypted communication—are integrated to mitigate inference attacks and regulatory risks. The financial forecasting component focuses on risk estimation and credit scoring models across participating banks, while the healthcare analytics component targets predictive diagnostics and patient outcome predictions across hospitals. Experimental results show federated models achieving performance comparable to centralized baselines while preserving data privacy and reducing compliance burdens. Operational metrics such as model accuracy, communication overhead, and scalability under AWS infrastructure are analyzed. The study demonstrates that AWS‑based federated learning architectures provide a viable and scalable foundation for cross‑institutional privacy‑preserving AI applications in finance and healthcare. (Amazon Web Services, Inc.)