ISBN: 9781032978109 - federated learning for healthcare: applications with case studies (12 risultati)

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: New. R. Anandan completed his UG, PG, Doctorate in Computer Science and Engineering and Post-Doctoral Degree - D.Sc. in Computer Science and Engineering from Mexico in North America.He is IBMS/390 Mainframe professional and he is recognized as Chartere.

Hardcover. Condizione: Brand New. 296 pages. 9.18x6.12x9.45 inches. In Stock.

Buch. Condizione: Neu. Neuware - 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. …

Hardcover. Condizione: new. Hardcover. 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 sectorInsights into how federated learning enhances clinical decision-making, disease prediction, diagnosis, and personalised treatment through decentralised data sourcesExamination of issues such as communication overhead, model heterogeneity, and data distribution imbalance, with strategies to overcome these challengesPractical examples of successful federated learning implementations in healthcare demonstrate its impact on patient care and operational efficiencyDiscussions on maintaining data privacy, ensuring compliance with regulations, and addressing ethical concernsThis 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condizione: new. Hardcover. 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 sectorInsights into how federated learning enhances clinical decision-making, disease prediction, diagnosis, and personalised treatment through decentralised data sourcesExamination of issues such as communication overhead, model heterogeneity, and data distribution imbalance, with strategies to overcome these challengesPractical examples of successful federated learning implementations in healthcare demonstrate its impact on patient care and operational efficiencyDiscussions on maintaining data privacy, ensuring compliance with regulations, and addressing ethical concernsThis 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Condizione: new. Hardcover. 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 sectorInsights into how federated learning enhances clinical decision-making, disease prediction, diagnosis, and personalised treatment through decentralised data sourcesExamination of issues such as communication overhead, model heterogeneity, and data distribution imbalance, with strategies to overcome these challengesPractical examples of successful federated learning implementations in healthcare demonstrate its impact on patient care and operational efficiencyDiscussions on maintaining data privacy, ensuring compliance with regulations, and addressing ethical concernsThis 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…