… Motivated by the aforementioned challenges, this paper first develops an asynchronous
federated learning (AFL) to achieve fast convergence speed and high learning accuracy for multi…

… determined by learning accuracy level, and thus (ii) between the Federated Learning
time and UE energy consumption. We fill this gap by formulating a Federated Learning over …

… resource-constrained nature of the wireless medium. In this paper, we propose a federated
learning … We prove, in practice, federated learning algorithms may solve a different problem …

… In this context, federated learning (FL) has emerged as a promising solution for realizing
cost-effective smart healthcare applications with improved privacy protection [6, 7, 8, 9]. …

… resources due to the long local training latency of sensors. The main contributions of this
paper are summarized as follows: 1) considering federated learning … cost and learning cost; 2) …

… In this paper, a novel framework is proposed to implement distributed federated learning (FL)
algorithms within a UAV swarm that consists of a leading UAV and several following UAVs. …

… Abstract—In this paper we introduce federated learning (FL) based resource allocation (RA)
for wireless communication networks, where users cooperatively train a RA policy in a dis…

… federated learning by jointly optimizing the local accuracy, subcarrier assignment, transmit
power allocation, and computational resource allocation… assignment and power allocation, we …

… In this context, federated learning (FL) has been proposed to provide collaborative data
training solutions, by coordinating multiple mobile devices to train a shared AI model without …

… learning, it has still privacy concerns. In this paper, first, we present the recent advances of
federated learning towards enabling federated learning-… taxonomy for federated learning over …