9789819632114 - cross-device federated recommendation: privacy-preserving personalization di kong, xiangjie; wang, lingyun; wang, mengmeng; shen, guoqiang (9 risultati)
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Hardcover. Condizione: new. Hardcover. This book introduces the prevailing domains of recommender systems and cross-device federated learning, highlighting the latest research progress and prospects regarding cross-device federated recommendation. As a privacy-oriented distributed computing paradigm, cross-device federated learn…ing enables collaborative intelligence across multiple devices while ensuring the security of local data. In this context, ubiquitous recommendation services emerge as a crucial application of device-side AI, making a deep exploration of federated recommendation systems highly significant.This book is self-contained, and each chapter can be comprehended independently. Overall, the book organizes existing efforts in federated recommendation from three different perspectives. The perspective of learning paradigms includes statistical machine learning, deep learning, reinforcement learning, and meta learning, where each has detailed techniques (e.g., different neural building blocks) to present relevant studies. The perspective of privacy computing covers homomorphic encryption, differential privacy, secure multi-party computing, and malicious attacks. More specific encryption and obfuscation techniques, such as randomized response and secret sharing, are involved. The perspective of federated issues discusses communication optimization and fairness perception, which are widely concerned in the cross-device distributed environment. In the end, potential issues and promising directions for future research are identified point by point.This book is especially suitable for researchers working on the application of recommendation algorithms to the privacy-preserving federated scenario. The target audience includes graduate students, academic researchers, and industrial practitioners who specialize in recommender systems, distributed machine learning, information retrieval, information security, or artificial intelligence. This book introduces the prevailing domains of recommender systems and cross-device federated learning, highlighting the latest research progress and prospects regarding cross-device federated recommendation. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Cross-device Federated Recommendation: Privacy-Preserving Personalization (Machine Learning: Foundations, Methodologies, and Applications)
Kong, Xiangjie; Wang, Lingyun; Wang, Mengmeng; Shen, Guojiang
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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EUR 172,44
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces the prevailing domains of recommender systems and cross-device federated learning, highlighting the latest research progress and prospects regarding cross-device federated recommendation. As a privacy-oriented distributed computing p…aradigm, cross-device federated learning enables collaborative intelligence across multiple devices while ensuring the security of local data. In this context, ubiquitous recommendation services emerge as a crucial application of device-side AI, making a deep exploration of federated recommendation systems highly significant.This book is self-contained, and each chapter can be comprehended independently. Overall, the book organizes existing efforts in federated recommendation from three different perspectives. The perspective of learning paradigms includes statistical machine learning, deep learning, reinforcement learning, and meta learning, where each has detailed techniques (e.g., different neural building blocks) to present relevant studies. The perspective of privacy computing covers homomorphic encryption, differential privacy, secure multi-party computing, and malicious attacks. More specific encryption and obfuscation techniques, such as randomized response and secret sharing, are involved. The perspective of federated issues discusses communication optimization and fairness perception, which are widely concerned in the cross-device distributed environment. In the end, potential issues and promising directions for future research are identified point by point.This book is especially suitable for researchers working on the application of recommendation algorithms to the privacy-preserving federated scenario. The target audience includes graduate students, academic researchers, and industrial practitioners who specialize in recommender systems, distributed machine learning, information retrieval, information security, or artificial intelligence.
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Hardcover. Condizione: new. Hardcover. This book introduces the prevailing domains of recommender systems and cross-device federated learning, highlighting the latest research progress and prospects regarding cross-device federated recommendation. As a privacy-oriented distributed computing paradigm, cross-device federated learn…ing enables collaborative intelligence across multiple devices while ensuring the security of local data. In this context, ubiquitous recommendation services emerge as a crucial application of device-side AI, making a deep exploration of federated recommendation systems highly significant.This book is self-contained, and each chapter can be comprehended independently. Overall, the book organizes existing efforts in federated recommendation from three different perspectives. The perspective of learning paradigms includes statistical machine learning, deep learning, reinforcement learning, and meta learning, where each has detailed techniques (e.g., different neural building blocks) to present relevant studies. The perspective of privacy computing covers homomorphic encryption, differential privacy, secure multi-party computing, and malicious attacks. More specific encryption and obfuscation techniques, such as randomized response and secret sharing, are involved. The perspective of federated issues discusses communication optimization and fairness perception, which are widely concerned in the cross-device distributed environment. In the end, potential issues and promising directions for future research are identified point by point.This book is especially suitable for researchers working on the application of recommendation algorithms to the privacy-preserving federated scenario. The target audience includes graduate students, academic researchers, and industrial practitioners who specialize in recommender systems, distributed machine learning, information retrieval, information security, or artificial intelligence. This book introduces the prevailing domains of recommender systems and cross-device federated learning, highlighting the latest research progress and prospects regarding cross-device federated recommendation. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer, Berlin, Springer Nature Singapore, Springer, 2025
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 128,39
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduces the prevailing domains of recommender systems and cross-device federated learning, highlighting the latest research progress and prospects regarding cross-device federated recommendation. As a privacy-oriented distrib…uted computing paradigm, cross-device federated learning enables collaborative intelligence across multiple devices while ensuring the security of local data. In this context, ubiquitous recommendation services emerge as a crucial application of device-side AI, making a deep exploration of federated recommendation systems highly significant.This book is self-contained, and each chapter can be comprehended independently. Overall, the book organizes existing efforts in federated recommendation from three different perspectives. The perspective of learning paradigms includes statistical machine learning, deep learning, reinforcement learning, and meta learning, where each has detailed techniques (e.g., different neural building blocks) to present relevant studies. The perspective of privacy computing covers homomorphic encryption, differential privacy, secure multi-party computing, and malicious attacks. More specific encryption and obfuscation techniques, such as randomized response and secret sharing, are involved. The perspective of federated issues discusses communication optimization and fairness perception, which are widely concerned in the cross-device distributed environment. In the end, potential issues and promising directions for future research are identified point by point.This book is especially suitable for researchers working on the application of recommendation algorithms to the privacy-preserving federated scenario. The target audience includes graduate students, academic researchers, and industrial practitioners who specialize in recommender systems, distributed machine learning, information retrieval, information security, or artificial intelligence. 157 pp. Englisch.
Cross-device Federated Recommendation: Privacy-Preserving Personalization (Machine Learning: Foundations, Methodologies, and Applications)
Kong, Xiangjie; Wang, Lingyun; Wang, Mengmeng; Shen, Guojiang
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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EUR 181,81
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book introduces the prevailing domains of recommender systems and cross-device federated learning, highlighting the latest research progress and prospects regarding cross-device federated recommendation. As a privacy-oriented distributed… computing paradigm, cross-device federated learning enables collaborative intelligence across multiple devices while ensuring the security of local data. In this context, ubiquitous recommendation services emerge as a crucial application of device-side AI, making a deep exploration of federated recommendation systems highly significant.This book is self-contained, and each chapter can be comprehended independently. Overall, the book organizes existing efforts in federated recommendation from three different perspectives. The perspective of learning paradigms includes statistical machine learning, deep learning, reinforcement learning, and meta learning, where each has detailed techniques (e.g., different neural building blocks) to present relevant studies. The perspective of privacy computing covers homomorphic encryption, differential privacy, secure multi-party computing, and malicious attacks. More specific encryption and obfuscation techniques, such as randomized response and secret sharing, are involved. The perspective of federated issues discusses communication optimization and fairness perception, which are widely concerned in the cross-device distributed environment. In the end, potential issues and promising directions for future research are identified point by point.This book is especially suitable for researchers working on the application of recommendation algorithms to the privacy-preserving federated scenario. The target audience includes graduate students, academic researchers, and industrial practitioners who specialize in recommender systems, distributed machine learning, information retrieval, information security, or artificial intelligence.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 172 pp. Englisch.
Cross-device Federated Recommendation: Privacy-Preserving Personalization (Machine Learning: Foundations, Methodologies, and Applications)
Kong, Xiangjie; Wang, Lingyun; Wang, Mengmeng; Shen, Guojiang
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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EUR 182,07
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Condizione: New. PRINT ON DEMAND.
