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[null,"1.","Record Nr.",null,"UNINA9910633937203321"]
[null,null,"Autore",null,"Jin Yaochu <1966->"]
[null,null,"Titolo",null,"Federated Learning : Fundamentals and Advances / / by Yaochu Jin, Hangyu Zhu, Jinjin Xu, Yang Chen"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023"]
[null,null,"ISBN",null,"9789811970832"]
[{"$nulls":4},"9811970831"]
[null,null,"Edizione",null,"[1st ed. 2023.]"]
[null,null,"Descrizione fisica",null,"1 online resource (227 pages)"]
[null,null,"Collana",null,"Machine Learning: Foundations, Methodologies, and Applications, , 2730-9916"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Machine learning"]
[{"$nulls":4},"Data protection - Law and legislation"]
[{"$nulls":4},"Cryptography"]
[{"$nulls":4},"Data encryption (Computer science)"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Privacy"]
[{"$nulls":4},"Cryptology"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Introduction -- Communication-Efficient Federated Learning -- Evolutionary Federated Learning.-Secure Federated Learning -- Summary and Outlook."]
[null,null,null,"Sommario/riassunto","This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionarylearning, and privacy "]
[{"$nulls":4},"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. ."]