… machine learning … communication cost can be reduced. Given these compelling benefits,
increasing research attention has been dedicated to applying FL in wireless communications […

… Section II presents the federated learning model and our FL algorithm. Section III
provides the convergence analysis of our proposed algorithm. Section IV analyzes the system …

… communication systems are prone to packet loss and transmission errors. The transmission
errors are pervasive in federated learning due to the harsh wireless … layer communication …

… Recently, various strategies to train machine learning models have been proposed such
as transfer learning, active learning, and federated learning. The federated learning approach …

… federated learning (FL) is proposed to estimate the tail distribution of the queue lengths.
Considering the communication delays incurred by FL over wireless … use of federated learning in …

… in future wireless networks to exploit the data for serving diverse … Federated learning (FL)
is a promising distributed learning framework that enables multiple edge devices to learn a …

… are being trained on machine learning models for countering communicable infectious
diseases. Federated Learning (FL) is a paradigm of distributed machine learning to deal with the …

… edge of wireless networks. Massive data processing and analysis entail machine learning
(ML… , an innovative distributed machine learning algorithm namely federated learning (FL) was …

… Learning [2] or Edge Intelligence [3]. Federated learning (FL) [4]–[7] is a new edge learning
framework that enables many edge devices to collaboratively train a machine learning …

… of communication resources for training. To cope with these issues, we propose a novel
framework of dispersed federated learning (… implementation of federated learning for various IoT-…