Federated Learning for Healthcare Applications with Case Studies | 30.51 MB
Title: Federated Learning for Healthcare Applications with Case Studies
Author: Anandan, R., Pal, Souvik, Balaganesh, D., Badie, Farshad
Category: Nonfiction, Computers, Advanced Computing, Natural Language Processing, Artificial Intelligence, General Computing
Language: English | 294 Pages | ISBN: 1032978104
Description:
The book offers an in-depth exploration of federated learning and its transformative impact on the healthcare industry. It begins by introducing the foundational concepts of federated learning, including its methods and applications within various healthcare domains. It explores how federated learning allows for model training using decentralised data, such as patient records, medical imaging, and wearable sensor data, without centralising sensitive information. This approach ensures patient privacy and addresses critical challenges in healthcare data management. A detailed overview of federated learning, its principles, and its relevance to the healthcare sector. Insights into how federated learning enhances clinical decision-making, disease prediction, diagnosis, and personalised treatment through decentralised data sources. Examination of issues such as communication overhead, model heterogeneity, and data distribution imbalance, with strategies to overcome these challenges. Practical examples of successful federated learning implementations in healthcare demonstrate its impact on patient care and operational efficiency. Discussions on maintaining data privacy, ensuring compliance with regulations, and addressing ethical concerns. This book is for researchers, healthcare professionals, data scientists, and policymakers interested in leveraging federated learning to enhance healthcare.
The book offers an in-depth exploration of federated learning and its transformative impact on the healthcare industry. It begins by introducing the foundational concepts of federated learning, including its methods and applications within various healthcare domains. It explores how federated learning allows for model training using decentralised data, such as patient records, medical imaging, and wearable sensor data, without centralising sensitive information. This approach ensures patient privacy and addresses critical challenges in healthcare data management. A detailed overview of federated learning, its principles, and its relevance to the healthcare sector. Insights into how federated learning enhances clinical decision-making, disease prediction, diagnosis, and personalised treatment through decentralised data sources. Examination of issues such as communication overhead, model heterogeneity, and data distribution imbalance, with strategies to overcome these challenges. Practical examples of successful federated learning implementations in healthcare demonstrate its impact on patient care and operational efficiency. Discussions on maintaining data privacy, ensuring compliance with regulations, and addressing ethical concerns. This book is for researchers, healthcare professionals, data scientists, and policymakers interested in leveraging federated learning to enhance healthcare.
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