Privacy-Preserving Federated Learning for Secure Healthcare Data Analysis in Distributed Computing Environments

Abstract

Healthcare analytics increasingly depends on geographically distributed clinical records, connected medical devices, imaging repositories, and institutional data platforms. Conventional centralized learning can increase exposure of sensitive information and amplify governance challenges when data must cross organizational boundaries. Federated learning offers a collaborative alternative in which participating sites train local models and exchange updates rather than raw records. However, model updates can still disclose private information, and operational heterogeneity can compromise accuracy, fairness, and reliability. This narrative review develops a privacy-preserving federated learning framework for secure healthcare data analysis, combining local preprocessing, identity and consent controls, secure aggregation, differential privacy, authenticated communication, robust aggregation, and continuous audit. It evaluates design trade-offs across clinical utility, confidentiality, interoperability, deployment cost, and reproducibility. The proposed framework is conceptual rather than an experimentally validated implementation; its evaluation criteria and illustrative figure are intended to guide future empirical studies. A governance-centered design is recommended in which technical protections complement clinical oversight and institutional accountability.