Federated Deep Learning for Healthcare: A Practical Guide with Challenges and Opportunities

Amandeep Kaur (Curatore)

Chetna Kaushal (Curatore)

Taylor & Francis Ltd, 2024

This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and ethical dilemmas. It investigates several privacy-preserving methods such as homomorphic encryption, secure multi-party computation, and differential privacy. It will enable readers to build and implement federated learning systems that safeguard private medical information. Features: Offers a thorough introduction of federated deep learning methods designed exclusively for medical applications. Investigates privacy-preserving methods with emphasis on data security and privacy. Discusses healthcare scaling and resource efficiency considerations. Examines methods for sharing information among various healthcare organizations while retaining model performance. This book is aimed at graduate students and researchers in federated learning, data science, AI/machine learning, and healthcare.

• Curatore: Amandeep Kaur, Chetna Kaushal
• Editore: Taylor & Francis Ltd
• Collana: Advances in Smart Healthcare Technologies
• Anno: 2024
• Rilegatura: Hardback
• Pagine: 252 p.
• Dimensioni: 234 x 156 mm
• Peso: 650 gr.
• EAN: 9781032689555

Testo in English