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Sinopsis
The book "Blockchain-Enabled Federated Learning for Privacy and Security" explores the integration of blockchain technology and federated learning to address critical challenges in healthcare data sharing. With the rise of electronic health records, medical imaging, IoMT devices, and genomics, safeguarding patient privacy while enabling collaborative AI has become essential. Blockchain provides decentralization, immutability, and trust, while federated learning ensures model training without exposing raw data. Together, they form a privacy-preserving, auditable, and scalable framework for healthcare AI. The book covers fundamentals, system architectures, cryptographic techniques, and performance trade-offs, along with real-world case studies in cancer research, IoMT, and COVID-19 diagnosis. It highlights regulatory and ethical considerations such as GDPR, HIPAA, and India's DPDP Act, and proposes future research in quantum integration, explainable AI, fairness-aware FL, and governance through smart contracts. This comprehensive guide serves researchers, healthcare professionals, and policymakers in building secure, transparent, and patient-centric healthcare ecosystems.
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Blockchain-Enabled Federated Learning for Privacy and Security
Publicado por
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209074316
ISBN 13: 9786209074318
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PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Nº de ref. del artículo: L0-9786209074318
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Blockchain-Enabled Federated Learning for Privacy and Security
Publicado por
LAP Lambert Academic Publishing, 2025
ISBN 10: 6209074316
ISBN 13: 9786209074318
Nuevo
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Librería: California Books, Miami, FL, Estados Unidos de America
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Blockchain-Enabled Federated Learning for Privacy and Security
Publicado por
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209074316
ISBN 13: 9786209074318
Nuevo
PAP
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Nº de ref. del artículo: L0-9786209074318
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Blockchain-Enabled Federated Learning for Privacy and Security
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Omniscriptum, LAP Lambert Academic Publishing, 2025
ISBN 10: 6209074316
ISBN 13: 9786209074318
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book 'Blockchain-Enabled Federated Learning for Privacy and Security' explores the integration of blockchain technology and federated learning to address critical challenges in healthcare data sharing. With the rise of electronic health records, medical imaging, IoMT devices, and genomics, safeguarding patient privacy while enabling collaborative AI has become essential. Blockchain provides decentralization, immutability, and trust, while federated learning ensures model training without exposing raw data. Together, they form a privacy-preserving, auditable, and scalable framework for healthcare AI. The book covers fundamentals, system architectures, cryptographic techniques, and performance trade-offs, along with real-world case studies in cancer research, IoMT, and COVID-19 diagnosis. It highlights regulatory and ethical considerations such as GDPR, HIPAA, and India's DPDP Act, and proposes future research in quantum integration, explainable AI, fairness-aware FL, and governance through smart contracts. This comprehensive guide serves researchers, healthcare professionals, and policymakers in building secure, transparent, and patient-centric healthcare ecosystems. 72 pp. Englisch. Nº de ref. del artículo: 9786209074318
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Blockchain-Enabled Federated Learning for Privacy and Security
Publicado por
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209074316
ISBN 13: 9786209074318
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The book 'Blockchain-Enabled Federated Learning for Privacy and Security' explores the integration of blockchain technology and federated learning to address critical challenges in healthcare data sharing. With the rise of electronic health records, medical imaging, IoMT devices, and genomics, safeguarding patient privacy while enabling collaborative AI has become essential. Blockchain provides decentralization, immutability, and trust, while federated learning ensures model training without exposing raw data. Together, they form a privacy-preserving, auditable, and scalable framework for healthcare AI. The book covers fundamentals, system architectures, cryptographic techniques, and performance trade-offs, along with real-world case studies in cancer research, IoMT, and COVID-19 diagnosis. It highlights regulatory and ethical considerations such as GDPR, HIPAA, and India's DPDP Act, and proposes future research in quantum integration, explainable AI, fairness-aware FL, and governance through smart contracts. This comprehensive guide serves researchers, healthcare professionals, and policymakers in building secure, transparent, and patient-centric healthcare ecosystems. Nº de ref. del artículo: 9786209074318
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Blockchain-Enabled Federated Learning for Privacy and Security
Publicado por
LAP Lambert Academic Publishing, 2025
ISBN 10: 6209074316
ISBN 13: 9786209074318
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Blockchain-Enabled Federated Learning for Privacy and Security
Publicado por
LAP Lambert Academic Publishing, 2025
ISBN 10: 6209074316
ISBN 13: 9786209074318
Nuevo
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Blockchain-Enabled Federated Learning for Privacy and Security
Publicado por
LAP Lambert Academic Publishing, 2025
ISBN 10: 6209074316
ISBN 13: 9786209074318
Nuevo
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Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Blockchain-Enabled Federated Learning for Privacy and Security (Paperback)
Publicado por
LAP Lambert Academic Publishing, 2025
ISBN 10: 6209074316
ISBN 13: 9786209074318
Nuevo
Paperback
Librería: CitiRetail, Stevenage, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: new. Paperback. The book "Blockchain-Enabled Federated Learning for Privacy and Security" explores the integration of blockchain technology and federated learning to address critical challenges in healthcare data sharing. With the rise of electronic health records, medical imaging, IoMT devices, and genomics, safeguarding patient privacy while enabling collaborative AI has become essential. Blockchain provides decentralization, immutability, and trust, while federated learning ensures model training without exposing raw data. Together, they form a privacy-preserving, auditable, and scalable framework for healthcare AI. The book covers fundamentals, system architectures, cryptographic techniques, and performance trade-offs, along with real-world case studies in cancer research, IoMT, and COVID-19 diagnosis. It highlights regulatory and ethical considerations such as GDPR, HIPAA, and India's DPDP Act, and proposes future research in quantum integration, explainable AI, fairness-aware FL, and governance through smart contracts. This comprehensive guide serves researchers, healthcare professionals, and policymakers in building secure, transparent, and patient-centric healthcare ecosystems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9786209074318
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Blockchain-Enabled Federated Learning for Privacy and Security
Publicado por
LAP LAMBERT Academic Publishing Okt 2025, 2025
ISBN 10: 6209074316
ISBN 13: 9786209074318
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The book 'Blockchain-Enabled Federated Learning for Privacy and Security' explores the integration of blockchain technology and federated learning to address critical challenges in healthcare data sharing. With the rise of electronic health records, medical imaging, IoMT devices, and genomics, safeguarding patient privacy while enabling collaborative AI has become essential. Blockchain provides decentralization, immutability, and trust, while federated learning ensures model training without exposing raw data. Together, they form a privacy-preserving, auditable, and scalable framework for healthcare AI. The book covers fundamentals, system architectures, cryptographic techniques, and performance trade-offs, along with real-world case studies in cancer research, IoMT, and COVID-19 diagnosis. It highlights regulatory and ethical considerations such as GDPR, HIPAA, and India's DPDP Act, and proposes future research in quantum integration, explainable AI, fairness-aware FL, and governance through smart contracts. This comprehensive guide serves researchers, healthcare professionals, and policymakers in building secure, transparent, and patient-centric healthcare ecosystems.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Englisch. Nº de ref. del artículo: 9786209074318
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