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.