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Sinopsis
This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements.
The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionarylearning, and privacy preservation. The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses."Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Yaochu Jin is an “Alexander von Humboldt Professor for Artificial Intelligence” in the Faculty of Technology, Bielefeld University, Germany. He is also a part-time Distinguished Chair Professor in Computational Intelligence at the Department of Computer Science, University of Surrey, Guildford, UK. He was a “Finland Distinguished Professor” at the University of Jyväskylä, Finland, “Changjiang Distinguished Visiting Professor” at Northeastern University, China, and “Distinguished Visiting Scholar” at the University of Technology in Sydney, Australia. His main research interests include data-driven optimization, multi-objective optimization, multi-objective learning, trustworthy machine learning, and evolutionary developmental systems. Prof Jin is a Member of Academia Europaea and IEEE Fellow.
Hangyu Zhu received B.Sc. degree from Yangzhou University, Yangzhou, China, in 2015, M.Sc. degree from RMIT University, Melbourne, VIC, Australia, in 2017, and PhD degree from University of Surrey, Guildford, UK, in 2021. He is currently a Lecturer with the Department of Artificial Intelligence and Computer Science, Jiangnan University, China. His main research interests are federated learning and evolutionary neural architecture search.
Jinjin Xu received the B.S and Ph.D. degrees from East China University of Science and Technology, Shanghai, China, in 2017 and 2022, respectively. He is currently a researcher with the Intelligent Perception and Interaction Research Department, OPPO Research Institute, Shanghai, China. His research interests include federated learning, data-driven optimization and its applications.
Yang Chen received Ph.D. from the School of Information and Control Engineering, China University of Mining and Technology, China, in 2019. He was a Research Fellow with the School of Computer Science and Engineering, Nanyang Technological University, Singapore, 2019-2022. He is currently with the School of Electrical Engineering, China University of Mining and Technology, China. His research interests include deep learning, secure machine learning, edge computing, anomaly detection, evolutionary computation, and intelligence optimization.
De la contraportada
This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements.
The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionarylearning, and privacy preservation.The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses.
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- EditorialSpringer
- Año de publicación2023
- ISBN 10 9811970858
- ISBN 13 9789811970856
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de edición1
- Número de páginas232
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Federated Learning
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ISBN 10: 9811970858
ISBN 13: 9789811970856
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents the fundamentals of and latest advances in federated learningAddresses communication efficiency and privacy-preservation problems in federated learning Proposes applying evolutionary neural architecture search for federated learni. Nº de ref. del artículo: 1228909514
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Federated Learning: Fundamentals and Advances (Machine Learning: Foundations, Methodologies, and Applications)
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Federated Learning
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ISBN 10: 9811970858
ISBN 13: 9789811970856
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionarylearning, and privacy preservation.The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses. 232 pp. Englisch. Nº de ref. del artículo: 9789811970856
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Federated Learning : Fundamentals and Advances
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ISBN 10: 9811970858
ISBN 13: 9789811970856
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionarylearning, and privacy preservation.The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses. Nº de ref. del artículo: 9789811970856
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Federated Learning
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Springer Nature Singapore, Springer Nature Singapore Dez 2023, 2023
ISBN 10: 9811970858
ISBN 13: 9789811970856
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Taschenbuch. Condición: Neu. Neuware -This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements.The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionarylearning, and privacy preservation.The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 232 pp. Englisch. Nº de ref. del artículo: 9789811970856
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Paperback. Condición: new. Paperback. This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionarylearning, and privacy preservation.The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses. This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9789811970856
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Federated Learning: Fundamentals and Advances (Machine Learning: Foundations, Methodologies, and Applications)
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