Authors: Khushwant Singh
Abstract: The rising demand for secure, efficient, and privacy-preserving methods of managing and sharing patient health information has driven advancements in technologies like federated learning. Unlike traditional machine learning, which centralizes data, federated learning allows models to be trained across decentralized devices or institutions without exposing raw data. This makes it uniquely suited to healthcare environments where data sensitivity and privacy regulations such as HIPAA and GDPR are paramount. Federated learning facilitates collaborative model development among hospitals, research institutions, and other stakeholders while safeguarding patient confidentiality. It empowers personalized medicine and predictive analytics by leveraging the collective intelligence of distributed datasets. Moreover, it reduces the attack surface for cyber threats by limiting data movement. This article reviews the core principles of federated learning, its integration with privacy-enhancing technologies such as differential privacy and secure multiparty computation, and explores case studies demonstrating its efficacy in real-world healthcare applications. The challenges of system heterogeneity, communication overhead, and model convergence are also discussed. Federated learning stands at the intersection of artificial intelligence and data governance, presenting a promising paradigm for the future of medical research and clinical decision support. With proper implementation, it holds the potential to unlock valuable insights from patient data while respecting ethical and legal boundaries.
DOI: https://doi.org/10.5281/zenodo.16750821
