Federated Learning and Privacy-Preserving in Healthcare AI

Dutt, Vishal
Kombinasjonsprodukt / 2024 / Engelsk

Produktdetaljer

ISBN
9798369318775
Publisert
2024-05-02
Utgiver
Vendor
Medical Information Science Reference
Høyde
279 mm
Bredde
216 mm
Aldersnivå
U, P, 05, 06
Språk
Product language
Engelsk
Format
Product format
Kombinasjonsprodukt
Antall sider
351

Redaktør
Dutt, Vishal

Biographical note

Umesh Kumar Lilhore is currently a Professor at the School of Computing Science & Engineering (CSE) at Galgotia University, Greater Noida. With over 19 years of teaching and 8 years of research experience, he has previously held positions at various renowned universities and colleges in India and abroad. Dr. Lilhore holds a Ph.D. and M.Tech in CSE and has completed his postdoctoral research at the Institute of Advanced Computing, University of Louisiana at Lafayette. He has a strong publication record with articles in reputed, peer-reviewed national and international Scopus journals and conferences. Sarita Simaiya is a distinguished Professor at the School of Computing Science & Engineering (CSE) at Galgotias University, Greater Noida. Boasting over 17 years of teaching and 8 years of research experience, Dr. Sarita has held esteemed positions at various prestigious universities and colleges in India and abroad. With a Ph.D. and M.Tech, BE in CSE, Dr. Sarita completed postdoctoral research at the Institute of Advanced Computing, University of Louisiana at Lafayette. His extensive publication record includes articles in reputed, peer- reviewed national and international Scopus journals and conferences. |Manoharan Poongodi - Editor|Dr. M. Poongodi received the B.Tech. degree in information technology (IT) from Anna University, Chennai, the M.E. degree in computer science (CSE) from St.Peters University, and the Ph.D. degree in information security from Anna University. She has teaching experience of more than five years. Her many cited publications in highly indexed journals talks about her vast knowledge and skill set in the area of blended areas network security, the IoT, machine learning, and deep learning. Her research interest is also extended in the areas of network analysis using social networking, mobile computing, Web services, 4G communication, cloud computing, and information security through anomaly detection. She is a renowned expert in networks field and a mass stunning speaker who has inspired a lot of students on network simulation through her hands on experience sessions. Many students have been done their B.E., M.E., M.Tech., and MCA projects under her guidance. She is with Hamad Bin Khalifa University and having expertise in many research areas.

Federated Learning and Privacy-Preserving in Healthcare AI

Dutt, Vishal
Kombinasjonsprodukt / 2024 / Engelsk
Dutt, Vishal
Kombinasjonsprodukt / 2024 / Engelsk
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The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. As AI use begins to increase in healthcare, the specter of data breaches, privacy infringements, and ethical quandaries appear formidable. Patient data, a cornerstone of medical advancement, becomes susceptible to compromise, necessitating a delicate balance between innovation and safeguarding individual privacy. Existing concerns focus on the potential misuse and unauthorized access to this sensitive information, resulting in a significant obstacle to the full realization of AI's potential in healthcare. Federated Learning and Privacy-Preserving in Healthcare AI emerges as the definitive solution to balancing medical progress with patient data security. This carefully curated volume not only outlines the challenges of federated learning but also provides a roadmap for implementing privacy-preserving AI systems in healthcare. By decentralizing the training of AI models, federated learning mitigates the risks associated with centralizing patient data, ensuring that critical information never leaves its original location. Aimed at healthcare professionals, AI experts, policymakers, and academics, this book not only delves into the technical aspects of federated learning but also fosters a collaborative approach to address the multifaceted challenges at the intersection of healthcare and AI. For those seeking a comprehensive guide to navigate the complexities of AI in healthcare while upholding patient privacy, this reference book serves as an indispensable resource.
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The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. As AI use begins to increase in healthcare, the specter of data breaches, privacy infringements, and ethical quandaries appear formidable. Patient data, a cornerstone of medical advancement, becomes susceptible to compromise, necessitating a delicate balance between innovation and safeguarding individual privacy. Existing concerns focus on the potential misuse and unauthorized access to this sensitive information, resulting in a significant obstacle to the full realization of AI's potential in healthcare. Federated Learning and Privacy-Preserving in Healthcare AI emerges as the definitive solution to balancing medical progress with patient data security. This carefully curated volume not only outlines the challenges of federated learning but also provides a roadmap for implementing privacy-preserving AI systems in healthcare. By decentralizing the training of AI models, federated learning mitigates the risks associated with centralizing patient data, ensuring that critical information never leaves its original location. Aimed at healthcare professionals, AI experts, policymakers, and academics, this book not only delves into the technical aspects of federated learning but also fosters a collaborative approach to address the multifaceted challenges at the intersection of healthcare and AI. For those seeking a comprehensive guide to navigate the complexities of AI in healthcare while upholding patient privacy, this reference book serves as an indispensable resource.
Les mer

Produktdetaljer

ISBN
9798369318775
Publisert
2024-05-02
Utgiver
Vendor
Medical Information Science Reference
Høyde
279 mm
Bredde
216 mm
Aldersnivå
U, P, 05, 06
Språk
Product language
Engelsk
Format
Product format
Kombinasjonsprodukt
Antall sider
351

Redaktør
Dutt, Vishal

Biographical note

Umesh Kumar Lilhore is currently a Professor at the School of Computing Science & Engineering (CSE) at Galgotia University, Greater Noida. With over 19 years of teaching and 8 years of research experience, he has previously held positions at various renowned universities and colleges in India and abroad. Dr. Lilhore holds a Ph.D. and M.Tech in CSE and has completed his postdoctoral research at the Institute of Advanced Computing, University of Louisiana at Lafayette. He has a strong publication record with articles in reputed, peer-reviewed national and international Scopus journals and conferences. Sarita Simaiya is a distinguished Professor at the School of Computing Science & Engineering (CSE) at Galgotias University, Greater Noida. Boasting over 17 years of teaching and 8 years of research experience, Dr. Sarita has held esteemed positions at various prestigious universities and colleges in India and abroad. With a Ph.D. and M.Tech, BE in CSE, Dr. Sarita completed postdoctoral research at the Institute of Advanced Computing, University of Louisiana at Lafayette. His extensive publication record includes articles in reputed, peer- reviewed national and international Scopus journals and conferences. |Manoharan Poongodi - Editor|Dr. M. Poongodi received the B.Tech. degree in information technology (IT) from Anna University, Chennai, the M.E. degree in computer science (CSE) from St.Peters University, and the Ph.D. degree in information security from Anna University. She has teaching experience of more than five years. Her many cited publications in highly indexed journals talks about her vast knowledge and skill set in the area of blended areas network security, the IoT, machine learning, and deep learning. Her research interest is also extended in the areas of network analysis using social networking, mobile computing, Web services, 4G communication, cloud computing, and information security through anomaly detection. She is a renowned expert in networks field and a mass stunning speaker who has inspired a lot of students on network simulation through her hands on experience sessions. Many students have been done their B.E., M.E., M.Tech., and MCA projects under her guidance. She is with Hamad Bin Khalifa University and having expertise in many research areas.

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