Название: Federated Learning Techniques and Its Application in the Healthcare IndustryАвтор: H L Gururaj, Tanuja Kayarga, Francesco Flammini, Dalibor Dobrilovic
Издательство: World Scientific Publishing
Год: 2024
Страниц: 235
Язык: английский
Формат: pdf (true)
Размер: 13.1 MB
Federated Learning is currently an emerging technology in the field of Machine Learning. Federated Learning is a structure which trains a centralized model for a given assignment, where the data is de-centralized across different edge devices or servers. This enables preservation of the confidentiality of data on various edge devices, as only the updated outcomes of the models are shared with the centralized model. This means the data can remain on each edge device, while we can still train a model using that data.
Federated Learning has greatly increased the potential to transmute data in the healthcare industry, enabling healthcare professionals to improve treatment of patients. This book comprises chapters on applying Federated models in the field of healthcare industry. Federated Learning mainly concentrates on securing the privacy of data by training local data in a shared global model without putting the training data in a centralized location. The importance of Federated Learning lies in its innumerable uses in health care that ranges from maintaining the privacy of raw data of the patients, discover clinically alike patients, forecasting hospitalization due to cardiac events impermanence and probable solutions to the same. The goal of this edited book is to provide a reference guide to the theme.
Chapter 1 explores the “Fundamentals of Federated Learning,” laying the groundwork for readers to comprehend the underlying concepts and principles that govern this groundbreaking methodology. Moving forward, Chapter 2, “Federated Learning and its Classifications,” offers a comprehensive understanding of the varied approaches and techniques employed in different scenarios.
The pivotal role of federated learning in transforming the landscape of financial insights and security in the digital age is the focus of Chapter 3. Here, readers will gain invaluable insights into how federated learning is reshaping the financial sector, ensuring both data security and analytical accuracy.
Chapter 4 further delves into the realm of recent applications, shedding light on how federated learning is making its mark across diverse fields.
The intersection of federated learning, fundamental theories, protocols, and enabling technologies in healthcare forms a substantial portion of this collection. The exploration of federated learning’s applications in healthcare, particularly in securing data access and its implications, is presented comprehensively. Moreover, a detailed examination of time series analysis in healthcare that offers valuable insights into leveraging temporal data patterns for predictive analysis and decision-making is presented in Chapters 6, 7, and 8.
Chapter 9 focuses on Federated Learning using TensorFlow, one of the most popular open-source Machine Learning frameworks. This chapter provides practical insights into the implementation aspects of Federated Learning, equipping readers with essential skills to embark on their own experiments and projects.
“Opportunities and Challenges in Federated Learning” and “Future Directions and Advances in Federated Learning” are presented in Chapters 10 and 11.
Whether you are a seasoned professional, a researcher, or an enthusiast eager to explore the frontier of machine learning, this book offers a comprehensive and insightful guide to understanding, implementing, and envisioning the future of Federated Learning.
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