Enabling Collaborative Health Intelligence With Federated Learning

By: Ng Khai Mun (Editor), Chin-Shiuh Shieh (Editor), Danish Ather (Editor)

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Hardcover | 30 October 2025

At a Glance

Format
Hardcover

Dimensions(cm)
25.4 x 17.8 x 3.02

Hardcover

$567.75

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In the era of data-driven healthcare, the ability to gather insights from diverse data sources is critical for advancing precision medicine and improving patient outcomes. However, concerns around data privacy and security compliance often hinder the analysis of sensitive health information. Federated Learning offers a great solution by enabling collaborative health intelligence across institutions without requiring data to leave local environments. By training machine learning models on decentralized data while preserving confidentiality, federated learning empowers healthcare stakeholders to build robust and generalizable models. Enabling Collaborative Health Intelligence With Federated Learning explores the confluence of federated learning and artificial intelligence as a blueprint for the future of medical care. This book bridges the gap between cutting-edge technological developments in federated learning and the pressing needs of AI-driven medical care. Covering topics such as health intelligence, data analysis, and data security, this book is an excellent resource for academics, healthcare practitioners, policy makers, industry leaders, and graduate students.

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