This reference text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. The book covers topics such as the prediction and diagnosis of Alzheimer's disease using neural networks and ensuring data privacy and security in federated learning for neural disorders.

This book:

Provides a thorough examination of the transformative impact of federated learning on the diagnosis, treatment, and understanding of brain disorders. Focuses on combining federated learning with magnetic resonance imaging (MRI) data, which is a fundamental aspect of contemporary neuroimaging research. Examines the use of federated learning as a promising approach for collaborative data analysis in healthcare, with a focus on maintaining privacy and security. Explores the cutting-edge field of healthcare innovation by examining the interface of neuroscience and machine learning, with a specific focus on the breakthrough technique of federated learning. Offers a comprehensive comprehension of how federated learning may transform patient care, covering both theoretical ideas and practical examples.

It is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, and biomedical engineering.