TY - BOOK AU - Lim,Wei Yang Bryan AU - Ng,Jer Shyuan AU - Xiong,Zehui AU - Niyato,Dusit AU - Miao,Chunyan TI - Federated Learning Over Wireless Edge Networks T2 - Wireless Networks, SN - 9783031078385 U1 - 621.382 23 PY - 2022/// CY - Cham PB - Springer International Publishing, Imprint: Springer KW - Telecommunication KW - Computational intelligence KW - Machine learning KW - Artificial intelligence KW - Communications Engineering, Networks KW - Computational Intelligence KW - Machine Learning KW - Artificial Intelligence N1 - Federated Learning at Mobile Edge Networks: A Tutorial -- Multi-Dimensional Contract Matching Design for Federated Learning in UAV Networks -- Joint Auction-Coalition Formation Framework for UAV-assisted Communication-Efficient Federated Learning -- Evolutionary Edge Association and Auction in Hierarchical Federated Learning -- Conclusion and Future Works N2 - This book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively. Provides a concise introduction to Federated Learning (FL) and how it enables Edge Intelligence; Highlights the challenges inherent to achieving scalable implementation of FL at the wireless edge; Presents how FL can address challenges resulting from the confluence of AI and wireless communications UR - https://doi.org/10.1007/978-3-031-07838-5 ER -