{"product_id":"split-federated-learning-for-secure-iot-applications-concepts-frameworks-applications-and-case-studies-hardcover-1","title":"Split Federated Learning for Secure Iot Applications: Concepts, Frameworks, Applications and Case Studies - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eGururaj Harinahalli Lokesh\u003c\/b\u003e (Editor), \u003cb\u003eGeetabai S. Hukkeri\u003c\/b\u003e (Editor), \u003cb\u003eN. Z. Jhanjhi\u003c\/b\u003e (Editor)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eNew approaches in federated learning and split learning have the potential to significantly improve ubiquitous intelligence in internet of things (IoT) applications. In split federated learning, the machine learning model is divided into smaller network segments, with each segment trained independently on a server using distributed local client data.\u003c\/p\u003e \u003cp\u003eThe split learning method mitigates two fundamental drawbacks of federated learning: affordability, and privacy and security. When running machine learning computation on devices with limited resources, assigning only a portion of the network to train at the client-side minimizes the processing burden, compared to running a complete network as in federated learning. In addition, neither client nor server has full access to the other, which is more secure.\u003c\/p\u003e \u003cp\u003eThis book reviews cutting edge technologies and advanced research in split federated learning. Coverage includes approaches to realizing and evaluating the effectiveness and advantages of federated learning and split-fed learning, the role of this technology in advancing and securing IoTs, advanced research on emerging AI models for preserving the privacy of the data owned by the clients, and the analysis and development of AI mechanisms in IoT architectures and applications. The use of split federated learning in natural language processing, recommendation systems, healthcare systems, emotion detection, smart agriculture, smart transportation and smart cities is discussed.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eSplit Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies\u003c\/i\u003e offers useful insights to the latest developments in the field for researchers, engineers and scientists in academia and industry, who are working in computing, AI, data science and cybersecurity with a focus on federated learning, machine learning and deep learning.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 285\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.69 x 9.21 x 6.14 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e October 15, 2024\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47213736755449,"sku":"9781839539459","price":195.3,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/4i8gZgt4xk9781839539459_a8062439-5546-4a53-b2a0-417012682f02.webp?v=1768109131","url":"https:\/\/bookscloud.io\/products\/split-federated-learning-for-secure-iot-applications-concepts-frameworks-applications-and-case-studies-hardcover-1","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}