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
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| Autore: |
Shafik Wasswa
|
| Titolo: |
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
|
| Pubblicazione: | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026 |
| Edizione: | 1st ed. 2026. |
| Descrizione fisica: | 1 online resource (1088 pages) |
| Disciplina: | 006.3 |
| Soggetto topico: | Computational intelligence |
| Artificial intelligence | |
| Medical informatics | |
| Computational Intelligence | |
| Artificial Intelligence | |
| Health Informatics | |
| Altri autori: |
Shafik
|
| Nota di contenuto: | 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. |
| Sommario/riassunto: | 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. |
| Titolo autorizzato: | The Convergence of Federated Learning and Healthcare 5.0 and Beyond: A New Era of Intelligent Health Systems |
| ISBN: | 3-032-03985-1 |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9911064735803321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |
Serie:
Studies in Computational Intelligence, . 1860-9503 ; ; 1247