Federated Learning in the Era of Digital Healthcare

Abstract

Federated Learning (FL) is a transformative approach that enables decentralized machine learning, making it a promising solution for enhancing privacy and fostering collaborative intelligence in healthcare. By allowing multiple healthcare institutions to collaborate on model training without sharing sensitive patient data, FL mitigates privacy concerns while leveraging diverse datasets. This chapter explores the fundamentals of FL, its applications in digital healthcare, and the privacy-preserving techniques that support its deployment, such as differential privacy and homomorphic encryption. The challenges and opportunities in scaling FL models, ensuring data security, and addressing regulatory issues are also discussed. With its ability to improve predictive healthcare, personalized treatment, and global health initiatives, FL has the potential to revolutionize healthcare data management and AI-driven decision-making.

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