Privacy-Preserving Distributed Machine Learning And Federated Ai Systems For Healthcare And Financial Applications
Abstract
Healthcare and financial sectors increasingly rely on data-driven machine learning while facing stringent privacy, security, and regulatory constraints. Centralized learning approaches amplify data exposure risks, making them unsuitable for sensitive domains such as electronic health records and financial transactions. This study investigates privacy-preserving distributed machine learning and federated AI systems that enable collaborative model training without sharing raw data. Using experimental evaluations on healthcare-style datasets (40,000–70,000 patient records) and large-scale financial transaction data with extreme class imbalance (
Fields of Study & Subjects
Privacy-Preserving Technologies in Data
Cryptography and Data Security
Blockchain Technology Applications and Security
Publication details
- Journal / source
- International Journal of Advances in Signal and Image Sciences
- Volume
- 12
- Issue
- 2s
- Pages
- 1372–1392
- Publication date
- 10 February 2026
- Language
- EN