Federated Learning for Digital Healthcare Systems 1st Edition

Federated Learning for Digital Healthcare Systems 1st Edition by Agbotiname Lucky Imoize, Mohammad S. Obaidat, and Houbing Herbert Song is an authoritative, practical guide that brings cutting-edge machine learning to the frontlines of patient care. This comprehensive volume explains how federated learning enables secure, privacy-preserving collaboration across hospitals, clinics, wearable devices and cloud-edge environments without pooling sensitive health data.

Discover clear, accessible explanations of core concepts—distributed model training, communication-efficient algorithms, encryption and differential privacy—paired with real-world healthcare applications such as remote monitoring, diagnostic imaging, predictive analytics and personalized treatment planning. The authors balance rigorous technical detail with clinical relevance, offering case studies, architecture diagrams and best-practice frameworks designed for deployment in diverse health systems worldwide, from large academic medical centers to rural telehealth networks.

Ideal for data scientists, healthcare IT leaders, clinicians, researchers and policy makers, this 1st edition equips readers to design compliant solutions that meet regional regulations (e.g., GDPR, HIPAA) while improving outcomes and reducing operational risk. Learn how to overcome challenges in heterogenous devices, limited bandwidth, model fairness, and security threats—plus strategies to integrate federated approaches into existing EHR and IoT infrastructures.

Whether you’re building the next generation of digital health services or shaping institutional AI strategy, this book is a must-have reference that bridges theory and practice. Add Federated Learning for Digital Healthcare Systems 1st Edition to your professional library today and lead the responsible adoption of AI-driven healthcare across global markets.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.