Federated Learning for Privacy-Preserving Healthcare AI Models

Abstract

Federated learning (FL) has emerged as a transformative paradigm for building collaborative healthcare AI models while safeguarding patient privacy and complying with regulations such as HIPAA and GDPR. Unlike centralized training, FL enables multiple hospitals and research centers to jointly develop a global model without exchanging raw data, thereby reducing risks of privacy breaches and promoting cross-institutional collaboration. This paper reviews recent literature (2020–2025) covering advances in privacy-preserving techniques including secure aggregation, differential privacy, and homomorphic encryption, and proposes a federated pipeline that integrates these methods for both electronic health records (EHRs) and medical imaging tasks. Simulated experiments with five clients illustrate that FL can achieve performance close to centralized models while substantially reducing exposure of sensitive health data, though trade-offs emerge in the form of reduced accuracy and added communication overhead. Beyond technical outcomes, the societal benefits of FL are significant: it fosters the development of AI models that generalize across diverse populations, supports early disease detection and personalized care, and enables resource-constrained institutions to contribute to and benefit from large-scale AI without compromising patient confidentiality. Ultimately, FL provides a pathway to equitable, trustworthy, and privacy-preserving healthcare innovation that can improve population health outcomes and strengthen societal trust in AI-driven medicine.