Federated learning in healthcare 6.0 paradigm, technologies and challenges
A. Vasuki, Vijayakumar Ponnusamy, Emilija Kisic, Nemanja Zdravković · 2025
These days, machine learning algorithms are used in almost every business, yet the effectiveness of these techniques depends on having access to high-quality training datasets. Every device produces data, and this data has the potential to inspire future advancements. Training traditional machine learning models usually requires centralized data, and collecting high-quality data is frequently difficult because of privacy issues and constraints. However, this issue can be addressed by employing Federated Learning. Federated learning can be employed to secure data and protect privacy within the healthcare sector. This chapter overviews the federated learning architecture, its impact on healthcare, various federated learning technologies, and the associated challenges.