Inhaltsangabe
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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Über die Autorin bzw. den Autor
Prof. M. Sukanya is an Assistant Professor in the Data Science Dept. at Holy Cross College (Autonomous), Tiruchirappalli. Ms. A. Kavipriya is serving as an Assistant Professor in the Department of Artificial Intelligence and Machine Learning at Holy Cross College (Autonomous), Tiruchirappalli.
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786209074318
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Paperback. Zustand: 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. Bestandsnummer des Verkäufers 9786209074318
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786209074318
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786209074318
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Taschenbuch. Zustand: Neu. Blockchain-Enabled Federated Learning for Privacy and Security | M. Sukanya (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786209074318 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 134204657
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