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Titlebook: Federated Learning; Fundamentals and Adv Yaochu Jin,Hangyu Zhu,Yang Chen Book 2023 The Editor(s) (if applicable) and The Author(s), under e

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書目名稱Federated Learning
副標(biāo)題Fundamentals and Adv
編輯Yaochu Jin,Hangyu Zhu,Yang Chen
視頻videohttp://file.papertrans.cn/342/341593/341593.mp4
概述Presents the fundamentals of and latest advances in federated learning.Addresses communication efficiency and privacy-preservation problems in federated learning.Proposes applying evolutionary neural
叢書名稱Machine Learning: Foundations, Methodologies, and Applications
圖書封面Titlebook: Federated Learning; Fundamentals and Adv Yaochu Jin,Hangyu Zhu,Yang Chen Book 2023 The Editor(s) (if applicable) and The Author(s), under e
描述.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.
出版日期Book 2023
關(guān)鍵詞Machine Learning; Federated Learning; Data Privacy; Cryptology; Neural Networks
版次1
doihttps://doi.org/10.1007/978-981-19-7083-2
isbn_softcover978-981-19-7085-6
isbn_ebook978-981-19-7083-2Series ISSN 2730-9908 Series E-ISSN 2730-9916
issn_series 2730-9908
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

書目名稱Federated Learning影響因子(影響力)




書目名稱Federated Learning影響因子(影響力)學(xué)科排名




書目名稱Federated Learning網(wǎng)絡(luò)公開度




書目名稱Federated Learning網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Federated Learning被引頻次




書目名稱Federated Learning被引頻次學(xué)科排名




書目名稱Federated Learning年度引用




書目名稱Federated Learning年度引用學(xué)科排名




書目名稱Federated Learning讀者反饋




書目名稱Federated Learning讀者反饋學(xué)科排名




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oducible laboratory protocols, and notes on troubleshooting and avoiding known pitfalls..?.Authoritative and accessible, .Unnatural Amino Acids: Methods and Protocols. serves as an ideal guide for sci978-1-4939-5888-7978-1-61779-331-8Series ISSN 1064-3745 Series E-ISSN 1940-6029
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