Artificial Intelligence Using Federated Learning：Fundamentals, Challenges, and Applications
商品資訊
系列名：Intelligent Manufacturing and Industrial Engineeri
ISBN13：9781032771649
出版社：PBKTYFRL
作者：Elngar; Ahmed A (Beni-Suef Uni.); Oliva; Diego (University de Guadalajara; Mexico); Balas; Valentina E. (Aurel Vlaicu University of Arad and Romanian Academy of Scientists; Romania)
出版日：2024/12/16
裝訂／頁數：精裝／344頁
規格：15.6cm*23.4cm (高/寬)
商品簡介
Federated machine learning is a novel approach to combining distributed machine learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data protection regulations like GDPR and CCPA.
Artificial Intelligence Using Federated Learning: Fundamentals, Challenges, and Applications enables training AI models on a large number of decentralized devices or servers, making it a scalable and efficient solution. It also allows organizations to create more versatile AI models by training them on data from diverse sources or domains. This approach can unlock innovative use cases in fields like healthcare, finance, and IoT, where data privacy is paramount.
The book is designed for researchers working in Intelligent Federated Learning and its related applications, as well as technology development, and is also of interest to academicians, data scientists, industrial professionals, researchers, and students.
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