The Convergence of Federated Learning and Healthcare 5.0 and Beyond: A New Era of Intelligent Health Systems / edited by Wasswa Shafik, Pushan Kumar Dutta, Priyadarshini Pattanaik.

Format:

Book

Author/Creator:

Shafik, Wasswa.

Contributor:

Shafik

Series:

Studies in Computational Intelligence, 1860-9503 ; 1247

Language:

English

Subjects (All):

Computational intelligence.

Artificial intelligence.

Medical informatics.

Computational Intelligence.

Artificial Intelligence.

Health Informatics.

Local Subjects:

Computational Intelligence.

Artificial Intelligence.

Health Informatics.

Physical Description:

1 online resource (1088 pages)

Edition:

1st ed. 2026.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2026.

Summary:

This book introduces a novel integration of Federated Learning with the vision of Healthcare 5.0 to enable secure, adaptive, and intelligent health systems. It presents cutting-edge frameworks that support decentralized model training across medical institutions while preserving patient privacy and ensuring compliance with data regulations. Focusing on real-world use cases, such as predictive diagnostics, edge-based patient monitoring, personalized medicine, and surgical robotics, it bridges theoretical advances with practical implementations. This book provides deep insights into the design of scalable, privacy-preserving artificial intelligence infrastructures suited for cross-institutional collaboration. It is designed for artificial intelligence researchers, digital health architects, healthcare technologists, and policy advisors. This supports the development of human-centric, resilient, and interoperable smart healthcare ecosystems.

Contents:

Understanding Healthcare 5.0 and Emerging Technologies

Fundamentals of Federated Learning: Principles and Applications

Data Privacy Challenges in Artificial Intelligence-Driven Healthcare

Regulatory Frameworks: HIPAA, GDPR, and Compliance in Federated Learning

Real Time Patient Monitoring and IOMT Applications

Integration of Blockchain Technology for Ensuring Trust and Security in the Digital Health Market: A Comprehensive Review

The Convergence of Federated Learning for the Digital Healthcare Market: An Overview

Differential Privacy and Homomorphic Encryption in Healthcare Artificial Intelligence

Analysis of Consumer Emotions Impacted By COVID-19

Guiding The Development of AI In Healthcare Through Ethical Considerations and Effective Governance

Intelligent Workforce Management in Healthcare 5.0: Redefining HR Through Federated Learning

The Legal Labyrinth of Smart Wearable Medical Devices: A Literary Overview

From Traditional to Intelligent: Transforming Global Health Care through Innovation

Ethical Considerations of Emotion AI used in the Synthetic Media Generations and Applications

Machine Learning-Based Prediction of Gene-Disease Associations for Reliable Evidence

Addressing Computational Overhead in Federated Learning Models in Healthcare 5.0 and Beyond

Robustness Against Adversarial Attacks and Model Security in Healthcare 5.0 and Beyond.-Scalable Model Aggregation and Interoperability Solutions in Healthcare Systems

Federated Learning for Decentralized Healthcare: Privacy, Efficiency, and Scalability in Healthcare 5.0

Federated Learning Architectures: Centralized Vs. Decentralized Models In Human Resource(HR)

A Two-staged Optimized Stacking Ensemble learning Classifier for the Prediction of Cervical Cancer

AI-Assisted Histopathological Image Analysis for Automated Gastric Cancer Detection

Robotics and AI-Powered Surgical Interventions in Gastric Cancer: Enhancing Precision and Efficacy of Oncologic Treatment24. Electronic Health Records using Blockchain

Centralized vs. Decentralized Federated Learning Architectures: Design Trade-offs, Security, and Performance in Healthcare 5.0 Applications

Navigating Healthcare 5.0: How Emerging Technologies Are Transforming Care Delivery and Medical Innovation

Identification of Stress in IT Professionals Using Convolutional Neural Network

Federated Learning for Precision Medicine: A Blockchain Enhanced Framework for Privacy Preserving Predictive Analytics in Healthcare 5.0

Machine Learning Advancements for Diabetes Prediction with LightGBM

Blockchain Integration for Enhanced Trust and Security in Federated Learning for Healthcare 5.0

Ontology-Based Data Harmonization and Federated Transfer Learning: Enabling Scalable and Interoperable Intelligence in Healthcare 5.0 for Next-Generation Healthcare

Future Trends in Federated Learning for Next-Generation Healthcare

Advancing Federated Learning in Healthcare 5.0

A Futuristic Pathway in Healthcare

Federated Learning in Healthcare Finance: A Systematic Review of Privacy-Preserving Models

AI-Induced Digital Addiction: Its Impact on Human Relationships within Healthcare 5.0 Ecosystems

Real-Time Detection of Latent Infections Using LSTM and IoMT-Based Health Monitoring

Federated Learning and Healthcare 5.0: Paving the Road Ahead for Privacy-Preserving Smart Health Systems

Neuro-Symbolic Federated Learning Models for Diagnostic Intelligence in Healthcare 5.0

Reducing Computational Overhead in Federated Learning: A Comprehensive Analysis

Future Trends in Federated Learning: Enabling Secure and Personalized Healthcare Solutions.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

3-032-03985-1

9783032039859

OCLC:

1574813855