Titlebook: Communication Efficient Federated Learning for Wireless Networks; Mingzhe Chen,Shuguang Cui Book 2024 The Editor(s) (if applicable) and Th

AVID

abduction

Anticlimax

https://doi.org/10.1007/978-3-030-25482-7to optimize wireless FL performance metrics (i.e., FL training loss, convergence time, energy efficiency). In particular, we first analyze how wireless factors (i.e., transmit power, spectrum interference, packet error rates) affect the FL performance. Then, using the FL convergence analytical resul

HIKE

Biologische Informationsverarbeitung,AVs). One key challenge in developing CAVs is the design of an autonomous controller that can accurately execute near real-time control decisions, such as a quick acceleration when merging to a highway and frequent speed changes in a stop-and-go traffic. However, the use of conventional feedback con

headway

Biologische Informationsverarbeitung,mputing (MEC)-enabled balloon network. In the considered network, each user needs to process a computational task in each time instant, where high-altitude balloons (HABs), acting as flying wireless base stations, can use their powerful computational abilities to process the tasks offloaded from the

情感

Accrue

HAWK

SLING

hermetic

Fundamentals and Preliminaries of Federated Learning,en, we introduce four important performance metrics to quantify the FL performance over wireless networks and analyze how wireless factors affect these metrics. Finally, we present the research directions and industry interest of designing communication efficient FL over wireless networks.