… federated learning (FL) in a classic wireless network, where learning clients share a common
wireless link to a coordinating server to perform federated model training using their local …

… Federated learning (FL) is a distributed machine learning architecture that allows different
devices to train a machine learning … Yonetani, “Client selection for federated learning with …

… at the network edge. To this end, this paper proposes a new client selection algorithm that
aims to accelerate the convergence rate for obtaining specialized machine learning models …

… Trust-driven reinforcement selection for federated learning … hybrid split and federated learning
in wireless uav networks. In … client selection for federated learning in vehicular networks. …

… We will particularly focus in this work on leveraging the wireless networks when they are
stable and not congested, such as at midnight or in the early morning time, mainly because ML …

… of the client selection to minimize convergence time while guaranteeing the desired learning
… a heterogeneous wireless network in which M wireless networks (eg, wireless local area …

… client selection algorithm for asynchronous FL over wireless networks to minimize the training
… the client selection problem to minimize the training latency for asynchronous FL over …

… wireless networks, the CS problem, and the used selection metrics in the CS methods. 2.1
Federated Learning … datasets of clients in the network while preserving the privacy of clients’ …

… the clients to make the data distribution of the clients nearIID. Chen et al. [7] designed a
semi-federated learning algo… Min, “Communication-efficient federated learning for wireless edge …

… for joint client selection and CPU frequency control in wireless federated learning networks
with … The objective is to minimize the time-averaged cost function, taking into account learning …