Federated learning in healthcare: Distributed machine learning for privacy preservation

Abstract

The mixture of artificial intelligence and cybersecurity applied for healthcare leads to the innovative approach to leveraging medical data without compromising patient rights. This chapter explores the futuristic role of federated learning (FL) in healthcare systems with emphasis on the polarity between data and privacy constraints. While healthcare companies strive to develop deep learning models that rely on terabytes of data, they face major challenges related to data ownership, privacy, and understandability. Federated learning emerges as possibly the best choice for building efficient machine learning models across distant datasets while preserving information privacy. In this chapter, the types of FL applications in healthcare are discussed with clear descriptions of FL applications based on a systematic literature review and recent research findings. The subject focuses on the technical and methodical aspects for applying FL in the healthcare domain including aggregation techniques, privacy-preserving approaches, and architectural solutions. In addition, we analyze how FL complies with the regulations and the extent to which it can enhance the reliability of AI systems in healthcare. This chapter aims at giving healthcare practitioners, academicians, and policymakers a comprehensive understanding of FL’s opportunities and drawbacks by reviewing the present state of the current studies and practical applications. Finally, we explain how federated learning will change the healthcare landscape by enabling large-scale, data-driven innovation without compromising patient privacy and data protection. © 2026 selection and editorial matter, Rejwan Bin Sulaiman, Usman Javed Butt, Yassine Maleh, Mohammad Aljaidi, Md. Simul Hasan Talukder and Musarrat Saberin Nipun.