{"product_id":"energy-optimization-and-security-in-federated-learning-for-iot-environments-hardcover","title":"Energy Optimization and Security in Federated Learning for Iot Environments - 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\u003eBalamurugan Balusamy\u003c\/b\u003e (Editor), \u003cb\u003eDaniel Arockiam\u003c\/b\u003e (Editor), \u003cb\u003ePethuru Raj\u003c\/b\u003e (Editor)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eSmart environments such as smart homes and industrial automation have been transformed by the rapid developments in internet of things (IoT) devices and systems. However, the widespread use of these devices poses significant difficulties, particularly in settings with limited energy resources. Due to the significant energy consumption and communication overhead associated with delivering huge amounts of data, traditional machine learning algorithms which rely on centralized cloud servers for training are not always suitable.\u003c\/p\u003e \u003cp\u003eFederated learning is a decentralized strategy that enables collaborative machine learning model training while keeping the data local on edge devices. It has emerged as a suitable solution to overcome the energy constraints of IoT devices. Federated learning works by dividing the training process among several nodes and using the processing power of edge devices. As opposed to sending raw data to a central server, only the model changes are communicated thereby considerably lowering the communication costs while protecting data privacy. This strategy reduces energy usage while simultaneously reducing network latency and bandwidth-related problems.\u003c\/p\u003e \u003cp\u003eIn this book, the authors show how to optimise federated learning algorithms and develop new communication protocols and resource allocation methodologies to maximize energy savings while retaining respectable model accuracy, to develop long-lasting and scalable IoT solutions that can function independently with no dependency on an external cloud infrastructure.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eEnergy Optimization and Security in Federated Learning for IoT Environments\u003c\/i\u003e is intended to be a useful resource for academic researchers, R\u0026amp;D professionals, IoT engineers in the IT industry, and data scientists creating optimised AI models to be run in cloud environments.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 349\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.81 x 9.21 x 6.14 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e February 04, 2025\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47213845479673,"sku":"9781839539626","price":195.3,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/MKzuZuTVdA9781839539626.webp?v=1768109378","url":"https:\/\/bookscloud.io\/products\/energy-optimization-and-security-in-federated-learning-for-iot-environments-hardcover","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}