{"product_id":"federated-learning-over-wireless-edge-networks-9783031078378","title":"Federated Learning Over Wireless Edge Networks","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eThis book provides an introduction to Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks, with a focus on resource heterogeneity. It offers solutions through network economics, game theory, and machine learning to improve resource allocation efficiency for FL. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Hardback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 165 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 29 September 2022\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Springer International Publishing AG\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eThis book provides a comprehensive tutorial on Federated Learning (FL) and its pivotal role in enabling Edge Intelligence over wireless edge networks. It offers readers a concise introduction to the challenges and cutting-edge approaches associated with implementing FL in the context of wireless edge networks. Recognizing the diverse resource characteristics at the network edge, the authors present a multifaceted set of solutions that intersect network economics, game theory, and machine learning. These solutions aim to enhance the efficiency of resource allocation for FL, thereby optimizing the performance of edge computing systems. A deep understanding of these issues and the theoretical studies presented herein will empower practitioners and researchers to develop resource-efficient FL systems and address the remaining open challenges in the field.\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e\u003ch1\u003eIntroduction\u003c\/h1\u003e\u003cbr\u003e\u003cbr\u003eFederated Learning (FL) is a promising approach that enables distributed learning across multiple edge devices, leveraging the collective intelligence of the network. By federating data and computing power, FL can address the limitations of traditional centralized learning methods, such as high data transmission costs and limited processing capabilities at the edge. In this book, we will delve into the fundamentals of FL, its applications in edge intelligence, and the challenges associated with implementing FL over wireless edge networks.\u003cbr\u003e\u003cbr\u003e\u003ch1\u003eChallenges in Implementing Federated Learning over Wireless Edge Networks\u003c\/h1\u003e\u003cbr\u003e\u003cbr\u003eImplementing FL over wireless edge networks presents several challenges. Firstly, the wireless edge network is characterized by resource heterogeneity, where different devices have varying capabilities and constraints. This heterogeneity can lead to imbalances in the distribution of data and computing power, making it challenging to achieve efficient FL training. Secondly, the wireless edge network is often subject to unreliable and unstable connections, which can affect the quality of communication and the accuracy of the training results. Thirdly, FL requires a high degree of synchronization between the edge devices, which can be challenging to achieve in a distributed environment with limited communication bandwidth.\u003cbr\u003e\u003cbr\u003e\u003ch1\u003eState-of-the-Art Approaches to Addressing Challenges in Implementing Federated Learning over Wireless Edge Networks\u003c\/h1\u003e\u003cbr\u003e\u003cbr\u003eTo address these challenges, researchers have developed a range of state-of-the-art approaches in implementing FL over wireless edge networks. One approach is to leverage the concept of edge computing, where the computation and storage of data are performed closer to the edge devices, reducing the need for data transmission to centralized servers. This can help alleviate the resource heterogeneity issue and improve the efficiency of FL training. Another approach is to incorporate game theory into FL, where the edge devices cooperate to achieve a common objective, such as improving the accuracy of a machine learning model. This can help mitigate the unreliable and unstable connections issue and enhance the overall performance of FL.\u003cbr\u003e\u003cbr\u003e\u003ch1\u003eMultifaceted Solutions at the Intersection of Network Economics, Game Theory, and Machine Learning\u003c\/h1\u003e\u003cbr\u003e\u003cbr\u003eIn addition to these approaches, the authors of this book propose a multifaceted set of solutions that intersect network economics, game theory, and machine learning. These solutions aim to optimize the resource allocation for FL over wireless edge networks. One solution is to develop a pricing mechanism that incentivizes edge devices to share their resources with other devices. This can help balance the resource distribution and improve the efficiency of FL training. Another solution is to design a reward mechanism that encourages edge devices to participate in FL training and contribute their data. This can help improve the accuracy of the training results and promote the adoption of FL in wireless edge networks.\u003cbr\u003e\u003cbr\u003e\u003ch1\u003eConclusion\u003c\/h1\u003e\u003cbr\u003e\u003cbr\u003eIn conclusion, this book provides a comprehensive tutorial on Federated Learning and its role in enabling Edge Intelligence over wireless edge networks. It offers readers a concise introduction to the challenges and state-of-the-art approaches associated with implementing FL in the context of wireless edge networks. By presenting a multifaceted set of solutions that intersect network economics, game theory, and machine learning, the authors aim to guide practitioners and researchers in developing resource-efficient FL systems and solving the remaining open issues in FL. With a clear understanding of these issues and the presented theoretical studies, practitioners and researchers can leverage FL to enhance the performance of edge computing systems and drive innovation in the field of wireless edge networks.\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 448g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 235 x 155 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9783031078378\u003cbr\u003e \u003cstrong\u003eEdition number\u003c\/strong\u003e: 1st ed. 2022\u003c\/p\u003e","brand":"Wei Yang Bryan Lim,Jer Shyuan Ng,Zehui Xiong,Dusit Niyato,Chunyan Miao","offers":[{"title":"Hardback","offer_id":44272382869754,"sku":"9783031078378","price":74.96,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/noImage_1_1b62baaa-de72-4dfe-bda1-c8a06c2def4c.jpg?v=1686252972","url":"https:\/\/shulphink.com\/products\/federated-learning-over-wireless-edge-networks-9783031078378","provider":"Shulph Ink","version":"1.0","type":"link"}