Federated Learning for Healthcare by R. Anandan (.ePUB)+
File Size: 30.5 MB
Federated Learning for Healthcare: Applications with Case Studies by R. Anandan, Souvik Pal, D. Balaganesh, Farshad Badie
Requirements: .ePUB, .PDF reader, 30.5 MB | True PDF, True EPUB
Overview: The book offers an in-depth exploration of Federated Learning (FL) 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. Federated Learning for Healthcare: Applications with Case Studies is the result of collaboration between experts from around the world in AI, healthcare, and cybersecurity. Our shared mission was to explore how federated learning can help make use of valuable healthcare data spread across different locations — all while keeping patient information safe and meeting legal requirements.
FL represents a revolutionary approach to Machine Learning that emphasizes data privacy and security. Traditional Machine Learning models typically rely on centralized data collection, where vast amounts of data are sent to a central server for training. However, this approach raises significant privacy concerns, especially when dealing with sensitive information such as personal health records, financial data, or proprietary business information. FL addresses these concerns by decentralizing the training process, allowing models to be trained across multiple devices or servers without sharing raw data.
Genre: Non-Fiction > Tech & Devices
Free Download links: