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Synopsis
Federated learning is a Distributed Machine Learning model that has been used in many applications today. Most edge devices can execute models with local dataset since their computation power is unutilized.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Saravanan Krishnan is working as Associate Professor at the Department of Computer Science & Engineering, College of Engineering, Guindy, Anna University, Tirunelveli, India. He has published papers in 14 international conferences and 30 reputed journals. He has also written 16 book chapters and nine books with reputed publishers. He is an active researcher and academician. Also, he is reviewer for many reputed journals published by Elsevier, IEEE etc.
A. Jose Anand is working as Professor at the Department of Electronics and Communication Engineering, KCG College of Technology, Chennai, India. He has one year of industrial experience and twenty-four years of teaching experience. He has presented several papers at conferences. He has published several papers in reputed journals. He has also published books for polytechnic & engineering subjects. He is a Member of CSI, IEI, IET, IETE, ISTE, INS, QCFI and EWB. His current research interest is in Wireless Sensor Networks, Embedded Systems, IoT, Machine Learning and Image Processing, etc.
R. Srinivasan is working as Professor at the Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India having vast teaching experience. He received a Ph.D. in Computer Science and Engineering from Vel Tech University. His research interest spans across Computer Networking, Wireless Sensor Networks and Internet of Things (IoT). Much of his work has been on improvising the understanding, design and the performance of networked computer systems and performance evaluation. He is a recognised supervisor at Vel Tech University guiding 8 research scholars. He has published over 25 papers in reputed journals and conferences. He had delivered technical sessions to various reputed institutes. He has been a reviewer member for many conferences and has served as technical committee member. He is also a member in many professional societies and a member in IEEE. He has published several reputed articles. He is presently Editor in Chief for Wireless Networks, Peer-to-Peer Networking and Applications- Springer Series.
R. Kavitha received a master's in software engineering from College of Engineering, Anna University, India and Ph. D in Computer Science and Engineering from Vel Tech, Chennai, India. Her research areas are Machine Learning, Image Processing and Software Engineering. She worked as Professor at Vel Tech, Chennai with 15 years of teaching experience. She had guided projects of many UG and PG students. She is a recognised supervisor at Vel Tech University guiding 8 research scholars. She has published over 35 papers in reputed journals. She is an active member of IEEE and IEEE WIE and has been a part of events in association with professional societies. She had delivered technical sessions to various reputed institutes. She has been a reviewer member for many conferences and has served as technical committee member.
S. Suresh was a Professor of Cloud Big Data and Analytics, Faculty of Computer Science and Engineering at P.A. College of Engineering and Technology, India. He undertook extensive research on Big Data & Analytics, Internet of Things and Machine Learning. He wrote more than 30 scientific papers some of which have been published in well-known journals from Elsevier, Springer, etc. and presented at important conferences. In his lifetime, he had received various best paper and best speaker awards. Suresh authored 6 books and numerous book chapters. He fetched research and events grants from various Indian agencies. His research is summarized at Google Scholar Citation. He also regularly tutors, advises and provides consulting support to regional firms with respect to their Cloud Big Data Analytics, IoT, Machine Learning and Mobile Application Development.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurCRC Press
- Date d'édition2025
- ISBN 10 1032471638
- ISBN 13 9781032471631
- ReliureBroché
- Langueanglais
- Nombre de pages356
- ÉditeurKrishnan Saravanan, Anand A. Jose, Srinivasan R., Kavitha R., Suresh S.
- Coordonnées du fabricantnon disponible
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Handbook on Federated Learning: Advances, Applications and Opportunities
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Handbook on Federated Learning : Advances, Applications and Opportunities
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ISBN 13 : 9781032471631
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Handbook on Federated Learning : Advances, Applications and Opportunities
Edité par
CRC Press, 2025
ISBN 10 : 1032471638
ISBN 13 : 9781032471631
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Handbook on Federated Learning : Advances, Applications and Opportunities
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Taschenbuch. Etat : Neu. Neuware - Federated learning is a Distributed Machine Learning model that has been used in many applications today. Most edge devices can execute models with local dataset since their computation power is unutilized. N° de réf. du vendeur 9781032471631
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Handbook on Federated Learning (Paperback)
Edité par
Taylor & Francis Ltd, London, 2025
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Paperback. Etat : new. Paperback. Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects. Federated learning is a Distributed Machine Learning model that has been used in many applications today. Most edge devices can execute models with local dataset since their computation power is unutilized. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9781032471631
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Handbook on Federated Learning (Paperback)
Edité par
Taylor & Francis Ltd, London, 2025
ISBN 10 : 1032471638
ISBN 13 : 9781032471631
Neuf
Paperback
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Paperback. Etat : new. Paperback. Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects. Federated learning is a Distributed Machine Learning model that has been used in many applications today. Most edge devices can execute models with local dataset since their computation power is unutilized. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9781032471631
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